Myravyova, E. A.; Sharipov, M. I.; Radakina, D. S.
During writing this work, the fuzzy controller with a double base of rules was studied, which was applied for the synthesis of the automated control system. A method for fuzzy controller adaptation has been developed. The adaptation allows the fuzzy controller to automatically compensate for parametric interferences that occur at the control object. Specifically, the fuzzy controller controlled the outlet steam temperature in the boiler unit BKZ-75-39 GMA. The software code was written in the programming support environment Unity Pro XL designed for fuzzy controller adaptation.
El-Sharkawi, Mohamed A. (Renton, WA); Xing, Jian (Seattle, WA); Butler, Nicholas G. (Newberg, OR); Rodriguez, Alonso (Pasadena, CA)
An adaptive sequential controller (50/50') for controlling a circuit breaker (52) or other switching device to substantially eliminate transients on a distribution line caused by closing and opening the circuit breaker. The device adaptively compensates for changes in the response time of the circuit breaker due to aging and environmental effects. A potential transformer (70) provides a reference signal corresponding to the zero crossing of the voltage waveform, and a phase shift comparator circuit (96) compares the reference signal to the time at which any transient was produced when the circuit breaker closed, producing a signal indicative of the adaptive adjustment that should be made. Similarly, in controlling the opening of the circuit breaker, a current transformer (88) provides a reference signal that is compared against the time at which any transient is detected when the circuit breaker last opened. An adaptive adjustment circuit (102) produces a compensation time that is appropriately modified to account for changes in the circuit breaker response, including the effect of ambient conditions and aging. When next opened or closed, the circuit breaker is activated at an appropriately compensated time, so that it closes when the voltage crosses zero and opens when the current crosses zero, minimizing any transients on the distribution line. Phase angle can be used to control the opening of the circuit breaker relative to the reference signal provided by the potential transformer.
El-Sharkawi, Mohamed A.; Xing, Jian; Butler, Nicholas G.; Rodriguez, Alonso
An adaptive sequential controller (50/50') for controlling a circuit breaker (52) or other switching device to substantially eliminate transients on a distribution line caused by closing and opening the circuit breaker. The device adaptively compensates for changes in the response time of the circuit breaker due to aging and environmental effects. A potential transformer (70) provides a reference signal corresponding to the zero crossing of the voltage waveform, and a phase shift comparator circuit (96) compares the reference signal to the time at which any transient was produced when the circuit breaker closed, producing a signal indicative of the adaptive adjustment that should be made. Similarly, in controlling the opening of the circuit breaker, a current transformer (88) provides a reference signal that is compared against the time at which any transient is detected when the circuit breaker last opened. An adaptive adjustment circuit (102) produces a compensation time that is appropriately modified to account for changes in the circuit breaker response, including the effect of ambient conditions and aging. When next opened or closed, the circuit breaker is activated at an appropriately compensated time, so that it closes when the voltage crosses zero and opens when the current crosses zero, minimizing any transients on the distribution line. Phase angle can be used to control the opening of the circuit breaker relative to the reference signal provided by the potential transformer.
Rysdyk, Rolf Theoduor
Research under supervision of Dr. Calise and Dr. Prasad at the Georgia Institute of Technology, School of Aerospace Engineering. has demonstrated the applicability of an adaptive controller architecture. The architecture successfully combines model inversion control with adaptive neural network (NN) compensation to cancel the inversion error. The tiltrotor aircraft provides a specifically interesting control design challenge. The tiltrotor aircraft is capable of converting from stable responsive fixed wing flight to unstable sluggish hover in helicopter configuration. It is desirable to provide the pilot with consistency in handling qualities through a conversion from fixed wing flight to hover. The linear model inversion architecture was adapted by providing frequency separation in the command filter and the error-dynamics, while not exiting the actuator modes. This design of the architecture provides for a model following setup with guaranteed performance. This in turn allowed for convenient implementation of guaranteed handling qualities. A rigorous proof of boundedness is presented making use of compact sets and the LaSalle-Yoshizawa theorem. The analysis allows for the addition of the e-modification which guarantees boundedness of the NN weights in the absence of persistent excitation. The controller is demonstrated on the Generic Tiltrotor Simulator of Bell-Textron and NASA Ames R.C. The model inversion implementation is robustified with respect to unmodeled input dynamics, by adding dynamic nonlinear damping. A proof of boundedness of signals in the system is included. The effectiveness of the robustification is also demonstrated on the XV-15 tiltrotor. The SHL Perceptron NN provides a more powerful application, based on the universal approximation property of this type of NN. The SHL NN based architecture is also robustified with the dynamic nonlinear damping. A proof of boundedness extends the SHL NN augmentation with robustness to unmodeled actuator
Papp, Z.; Driessen, B.J.F.
The paper presents an adaptive nonlinear (state-) feedback control structure, where the nonlinearities are implemented as smooth fuzzy mappings defined as rule sets. The fine tuning and adaption of the controller is realized by an indirect adaptive scheme, which modifies the parameters of the fuzzy
Goodwin, Graham C
Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o
Berghuis, Harry; Berghuis, Harry; Ortega, Romeo; Nijmeijer, Henk
A globally convergent adaptive control scheme for robot motion control with the following features is proposed. First, the adaptation law possesses enhanced robustness with respect to noisy velocity measurements. Second, the controller does not require the inclusion of high gain loops that may
Hua Changchun E-mail: email@example.com; Guan Xinping
Control problem of chaotic system is investigated via adaptive method. A fairly simple adaptive controller is constructed, which can control chaotic systems to unstable fixed points. The precise mathematical models of chaotic systems need not be known and only the fixed points and the dimensions of chaotic systems are required to be known. Simulations on controlling different chaotic systems are investigated and the results show the validity and feasibility of the proposed controller.
National Aeronautics and Space Administration — Researchers at NASA Armstrong are working to further the development of an adaptive augmenting control algorithm (AAC). The AAC was developed to improve the...
This thesis analyzes unmanned aerial vehicles and its adaptivity - their structures, operational principles and components. Also analyzing algorithms of adaptive neural networks and their usage in unmanned aerial vehicles. The main objective of this thesis is to analyze structures, control systems of unmanned aerial vehicles and their abilities to adapt to changing environment. This thesis contains analysis of already used solutions and their drawbacks. As research made in this thesis shown t...
Toersche, Hermen; Nykamp, Stefan; Molderink, Albert; Hurink, Johann L.; Smit, Gerardus Johannes Maria
Methods are discussed for planning oriented smart grid control to cope with scenarios with limited predictability, supporting an increasing penetration of stochastic renewable resources. The performance of these methods is evaluated with simulations using measured wind generation and consumption
This study investigates a hybrid adaptive flight control method as a design possibility for a flight control system that can enable an effective adaptation strategy to deal with off-nominal flight conditions. The hybrid adaptive control blends both direct and indirect adaptive control in a model inversion flight control architecture. The blending of both direct and indirect adaptive control provides a much more flexible and effective adaptive flight control architecture than that with either direct or indirect adaptive control alone. The indirect adaptive control is used to update the model inversion controller by an on-line parameter estimation of uncertain plant dynamics based on two methods. The first parameter estimation method is an indirect adaptive law based on the Lyapunov theory, and the second method is a recursive least-squares indirect adaptive law. The model inversion controller is therefore made to adapt to changes in the plant dynamics due to uncertainty. As a result, the modeling error is reduced that directly leads to a decrease in the tracking error. In conjunction with the indirect adaptive control that updates the model inversion controller, a direct adaptive control is implemented as an augmented command to further reduce any residual tracking error that is not entirely eliminated by the indirect adaptive control.
This thesis is divided into two parts, i.e., adaptive extremum control and modelling and control of a wind turbine. The rst part of the thesis deals with the design of adaptive extremum controllers for some processes which have the behaviour that process should have as high e ciency as possible...... in parameters, and thus directly lends itself to parameter estimation and adaptive control. The extremum control law is derived based on static optimization of a performance function. For a process with nonlinearity at output the intermediate signal between the linear part and nonlinear part plays an important...... role. If it can be emphasis on control design. The models have beenvalidated by experimental data obtained from an existing wind turbine. The e ective wind speed experienced by the rotor of a wind turbine, which is often required by some control methods, is estimated by using a wind turbine as a wind...
Seraji, Homayoun; Lee, Thomas; Delpech, Michel
Decentralized direct adaptive control scheme for six-jointed industrial robot eliminates part of overall computational burden imposed by centralized controller and degrades performance of robot by reducing sampling rate. Control and controller-adaptation laws based on observed performance of manipulator: no need to model dynamics of robot. Adaptive controllers cope with uncertainties and variations in robot and payload.
George, Jemin; Singla, Puneet; Crassidis, John L.
This article presents a Kalman filter based adaptive disturbance accommodating stochastic control scheme for linear uncertain systems to minimise the adverse effects of both model uncertainties and external disturbances. Instead of dealing with system uncertainties and external disturbances separately, the disturbance accommodating control scheme lumps the overall effects of these errors in a to-be-determined model-error vector and then utilises a Kalman filter in the feedback loop for simultaneously estimating the system states and the model-error vector from noisy measurements. Since the model-error dynamics is unknown, the process noise covariance associated with the model-error dynamics is used to empirically tune the Kalman filter to yield accurate estimates. A rigorous stochastic stability analysis reveals a lower bound requirement on the assumed system process noise covariance to ensure the stability of the controlled system when the nominal control action on the true plant is unstable. An adaptive law is synthesised for the selection of stabilising system process noise covariance. Simulation results are presented where the proposed control scheme is implemented on a two degree-of-freedom helicopter.
Trujillo, Anna C.; Gregory, Irene M.; Hempley, Lucas E.
Adaptive control provides robustness and resilience for highly uncertain, and potentially unpredictable, flight dynamics characteristic. Some of the recent flight experiences of pilot-in-the-loop with an adaptive controller have exhibited unpredicted interactions. In retrospect, this is not surprising once it is realized that there are now two adaptive controllers interacting, the software adaptive control system and the pilot. An experiment was conducted to categorize these interactions on the pilot with an adaptive controller during control surface failures. One of the objectives of this experiment was to determine how the adaptation time of the controller affects pilots. The pitch and roll errors, and stick input increased for increasing adaptation time and during the segment when the adaptive controller was adapting. Not surprisingly, altitude, cross track and angle deviations, and vertical velocity also increase during the failure and then slowly return to pre-failure levels. Subjects may change their behavior even as an adaptive controller is adapting with additional stick inputs. Therefore, the adaptive controller should adapt as fast as possible to minimize flight track errors. This will minimize undesirable interactions between the pilot and the adaptive controller and maintain maneuvering precision.
Polderman, Jan W.; Daams, Jasper
In this paper an almost optimal indirect adaptive controller for input/output dynamical systems is proposed. The control part of the adaptive control scheme is based on a modified LQ control law: by adding a time-varying gain to the certainty equivalent control law the conflict between
Wolpert, David H.
A viewgraph presentation on statistical physics for distributed adaptive control is shown. The topics include: 1) The Golden Rule; 2) Advantages; 3) Roadmap; 4) What is Distributed Control? 5) Review of Information Theory; 6) Iterative Distributed Control; 7) Minimizing L(q) Via Gradient Descent; and 8) Adaptive Distributed Control.
Pavlock, Kate Maureen; Less, James L.; Larson, David Nils
The National Aeronautics and Space Administration s Dryden Flight Research Center completed flight testing of adaptive controls research on a full-scale F-18 testbed. The validation of adaptive controls has the potential to enhance safety in the presence of adverse conditions such as structural damage or control surface failures. This paper describes the research interface architecture, risk mitigations, flight test approach and lessons learned of adaptive controls research.
Ghosh, D.; Baldi, S.
Several classes of multi-model adaptive control schemes have been proposed in literature: instead of one single parameter-varying controller, in this adaptive methodology multiple fixed-parameter controllers for different operating regimes (i.e. different models) are utilised. Despite advances in
Berghuis, Harry; Berghuis, Harry; Ortega, Romeo; Nijmeijer, Henk
The authors propose a globally convergent adaptive control scheme for robot motion control with the following features: first, the adaptation law processes enhanced robustness with respect to noisy velocity measurements; secondly, the controller does not require the inclusion of high-gain loops that
Trujillo, Anna; Gregory, Irene
Adaptive control is considered for highly uncertain, and potentially unpredictable, flight dynamics characteristic of adverse conditions. This experiment looked at how adaptive controller adaptation time to recover nominal aircraft dynamics affects pilots and how pilots want information about available control authority transmitted. Results indicate that an adaptive controller that takes three seconds to adapt helped pilots when looking at lateral and longitudinal errors. The controllability ratings improved with the adaptive controller, again the most for the three seconds adaptation time while workload decreased with the adaptive controller. The effects of the displays showing the percentage amount of available safe flight envelope used in the maneuver were dominated by the adaptation time. With the displays, the altitude error increased, controllability slightly decreased, and mental demand increased. Therefore, the displays did require some of the subjects resources but these negatives may be outweighed by pilots having more situation awareness of their aircraft.
On the commercial side, the implementation of L1 adaptive controller on NASA’s subscale generic transport model (GTM) aircraft demonstrated...I. Gregory, L. Valavani, Experimental Validation of 1L Adaptive Control: Rohrs ’ Counterexample in Flight, Submitted to AIAA Journal of Guidance
Full Text Available The paper presents an adaptive method using the controlled grid deformation over an elastic, isotropic and continuous domain. The adaptive process is controlled with the principal strains and principal strain directions and uses the finite elements method. Numerical results are presented for several test cases.
.... We regard this as a major step towards flight certification of adaptive controllers. The approach is more general in that it permits a broad class of input nonlinearities, including such effects as discrete and bang/bang control...
National Aeronautics and Space Administration — I propose to develop methods for soft and inflatable robots that will allow the control system to adapt and change control parameters based on changing conditions...
Zhang, Mengzhen; Zou, Chang-Ling; Jiang, Liang
Quantum transducers play a crucial role in hybrid quantum networks. A good quantum transducer can faithfully convert quantum signals from one mode to another with minimum decoherence. Most investigations of quantum transduction are based on the protocol of direct mode conversion. However, the direct protocol requires the matching condition, which in practice is not always feasible. Here we propose an adaptive protocol for quantum transducers, which can convert quantum signals without requiring the matching condition. The adaptive protocol only consists of Gaussian operations, feasible in various physical platforms. Moreover, we show that the adaptive protocol can be robust against imperfections associated with finite squeezing, thermal noise, and homodyne detection, and it can be implemented to realize quantum state transfer between microwave and optical modes.
Pavlock, Kate Maureen; Less, James L.; Larson, David Nils
The National Aeronautics and Space Administration's Dryden Flight Research Center completed flight testing of adaptive controls research on a full-scale F-18 testbed. The testbed served as a full-scale vehicle to test and validate adaptive flight control research addressing technical challenges involved with reducing risk to enable safe flight in the presence of adverse conditions such as structural damage or control surface failures. This paper describes the research interface architecture, risk mitigations, flight test approach and lessons learned of adaptive controls research.
Full Text Available A feedback control method and an adaptive feedback control method are proposed for Chua's circuit chaos system, which is a simple 3D autonomous system. The asymptotical stability is proven with Lyapunov theory for both of the proposed methods, and the system can be dragged to one of its three unstable equilibrium points respectively. Simulation results show that the proposed methods are valid, and control performance is improved through introducing adaptive technology.
Motter, Mark A.
A broad overview of current adaptive flight control research efforts at NASA is presented, as well as some more detailed discussion of selected specific approaches. The stated objective of the Integrated Resilient Aircraft Control Project, one of NASA s Aviation Safety programs, is to advance the state-of-the-art of adaptive controls as a design option to provide enhanced stability and maneuverability margins for safe landing in the presence of adverse conditions such as actuator or sensor failures. Under this project, a number of adaptive control approaches are being pursued, including neural networks and multiple models. Validation of all the adaptive control approaches will use not only traditional methods such as simulation, wind tunnel testing and manned flight tests, but will be augmented with recently developed capabilities in unmanned flight testing.
National Aeronautics and Space Administration — An Adaptive Feedforward and Feedback Control (AFFC) Framework is proposed to suppress the aircraft's structural vibrations and to increase the resilience of the...
Despite the remarkable theoretical accomplishments and successful applications of adaptive control, the field is not sufficiently mature to solve challenging control problems requiring strict performance and safety guarantees. Towards addressing these issues, a novel deterministic multiple-model adaptive control approach called adaptive mixing control is proposed. In this approach, adaptation comes from a high-level system called the supervisor that mixes into feedback a number of candidate controllers, each finely-tuned to a subset of the parameter space. The mixing signal, the supervisor's output, is generated by estimating the unknown parameters and, at every instant of time, calculating the contribution level of each candidate controller based on certainty equivalence. The proposed architecture provides two characteristics relevant to solving stringent, performance-driven applications. First, the full-suite of linear time invariant control tools is available. A disadvantage of conventional adaptive control is its restriction to utilizing only those control laws whose solutions can be feasibly computed in real-time, such as model reference and pole-placement type controllers. Because its candidate controllers are computed off line, the proposed approach suffers no such restriction. Second, the supervisor's output is smooth and does not necessarily depend on explicit a priori knowledge of the disturbance model. These characteristics can lead to improved performance by avoiding the unnecessary switching and chattering behaviors associated with some other multiple adaptive control approaches. The stability and robustness properties of the adaptive scheme are analyzed. It is shown that the mean-square regulation error is of the order of the modeling error. And when the parameter estimate converges to its true value, which is guaranteed if a persistence of excitation condition is satisfied, the adaptive closed-loop system converges exponentially fast to a closed
Griot Helium Neon Class II laser with output power of 0.5 mW, operating at a wavelength of 633 nm. The science camera is an IDS uEye-2210SE CCD camera...produced by Edmund Optics, Newport/New Focus, Thor Labs, and CVI Melles Griot . Two computer controllers are used for the full experimental system. The
Bialasiewicz, Jan T.
The control problem of a flexible robotic arm has been investigated. The control strategies that have been developed have a wide application in approaching the general control problem of flexible space structures. The following control strategies have been developed and evaluated: neural self-tuning control algorithm, neural-network-based fuzzy logic control algorithm, and adaptive pole assignment algorithm. All of the above algorithms have been tested through computer simulation. In addition, the hardware implementation of a computer control system that controls the tip position of a flexible arm clamped on a rigid hub mounted directly on the vertical shaft of a dc motor, has been developed. An adaptive pole assignment algorithm has been applied to suppress vibrations of the described physical model of flexible robotic arm and has been successfully tested using this testbed.
O. F. Opeiko
Full Text Available The goal of this articl is to improve acсuracy and stability margine for system with proportional differential (PD-controllers and parameters unsertaity by means of adaptation. The adaptive controller must produce the accuraсy improving by encreasing the proportional gain of controller, when the error is non zero. Consequently, the error decrease, adaptation become less intensive, and the system maintain the stability. The is provided by the correctly constructed Lapunov function. The method of parametric synthesis for adaptive PD-controller is developed based on roots location on complex plane. The numerical example of synthesis is presented with simulation results, which demonstrate the correctness of developed method. The adaptive PD-controller allow accuracy improuving with stability retaining, i. e. the adaptivity is able to replace the integrator by proportional gain tuning. The adaptive PD-controller is especially helpful for systems, working with inputs variability, and when the exponential dynamic is of importance. In cases, when diturbances are restricted, the adaptive PD-controller provides the stability and accuracy, but slowly operation.
Totah, Joseph J.
The objective of this paper is to present results from the evaluation of a direct adaptive tracking controller. The control architecture employs both pre-trained and an on-line neural networks to represent the non-linear aircraft dynamics in the model inversion portion of the controller. The aircraft model used for this evaluation is representative of the F-15 Advanced Control Technology for Integrated Vehicles (ACTIVE) aircraft. The controller was evaluated for three cases: (1) nominal conditions; (2) loss of control power; and (3) loss of control power in the presence of atmospheric turbulence. The results were compared with the existing F-15 ACTIVE conventional mode controller in all cases. The results indicate extremely desirable airframe stabilization characteristics for case (1) that do not degrade significantly for case (2) or (3) as does the conventional mode controller. It was concluded that this controller exhibits both stable and robust adaptive characteristics when subjected to mild and extreme loss of control power conditions. Integration of this neural adaptive flight controller into the full non-linear six degree-of-freedom F-15 ACTIVE simulation is recommended for evaluation in a real-time high fidelity piloted simulation environment.
Full Text Available The obesity epidemic continues rising as a global health challenge, despite the increasing public awareness and the use of lifestyle and medical interventions. The biomedical community is urged to develop new treatments to obesity. Excess energy is stored as fat in white adipose tissue (WAT, dysfunction of which lie at the core of obesity and associated metabolic disorders. In contrast, brown adipose tissue (BAT burns fat and dissipates chemical energy as heat. The development and activation of brown-like adipocytes, also known as beige cells, result in WAT browning and thermogenesis. The recent discovery of brown and beige adipocytes in adult humans has sparked the exploration of the development, regulation, and function of these thermogenic adipocytes. The central nervous system (CNS drives the sympathetic nerve activity in BAT and WAT to control heat production and energy homeostasis. This review provides an overview of the integration of thermal, hormonal, and nutritional information on hypothalamic circuits in thermoregulation.
Full Text Available The article discusses the the problem of controlling a synchronous generator, namely, maintaining the stability of the control object in the conditions of occurrence of noise and disturbances in the regulatory process. The model of a synchronous generator is represented by a system of differential equations of Park-Gorev, where state variables are computed relative to synchronously rotating d, q-axis. Management of synchronous generator is proposed to organize on the basis of the position-path control using algorithms to adapt with the reference model. Basic control law directed on the stabilizing indicators the frequency generated by the current and the required power level, which is achieved by controlling the mechanical torque on the shaft of the turbine and the value of the excitation voltage of the synchronous generator. Modification of the classic adaptation algorithm using the reference model, allowing to minimize the error of the reference regulation and the model under investigation within the prescribed limits, produced by means of the introduction of additional variables controller adaptation in the model. Сarried out the mathematical modeling of control provided influence on the studied model of continuous nonlinear and unmeasured the disturbance. Simulation results confirm the high level accuracy of tracking and adaptation investigated model with respect to the reference, and the present value of the loop error depends on parameters performance of regulator.
Lichtenwalner, Peter F.; Little, Gerald R.; Scott, Robert C.
The Adaptive Neural Control of Aeroelastic Response (ANCAR) program is a joint research and development effort conducted by McDonnell Douglas Aerospace (MDA) and the National Aeronautics and Space Administration, Langley Research Center (NASA LaRC) under a Memorandum of Agreement (MOA). The purpose of the MOA is to cooperatively develop the smart structure technologies necessary for alleviating undesirable vibration and aeroelastic response associated with highly flexible structures. Adaptive control can reduce aeroelastic response associated with buffet and atmospheric turbulence, it can increase flutter margins, and it may be able to reduce response associated with nonlinear phenomenon like limit cycle oscillations. By reducing vibration levels and loads, aircraft structures can have lower acquisition cost, reduced maintenance, and extended lifetimes. Phase I of the ANCAR program involved development and demonstration of a neural network-based semi-adaptive flutter suppression system which used a neural network for scheduling control laws as a function of Mach number and dynamic pressure. This controller was tested along with a robust fixed-gain control law in NASA's Transonic Dynamics Tunnel (TDT) utilizing the Benchmark Active Controls Testing (BACT) wing. During Phase II, a fully adaptive on-line learning neural network control system has been developed for flutter suppression which will be tested in 1996. This paper presents the results of Phase I testing as well as the development progress of Phase II.
Suresh, S; Omkar, SN; Mani, V; Sundararajan, N
A model reference indirect adaptive neural control scheme that uses both off-line and online learning strategies is proposed for an,unstable nonlinear aircraft controller design. The bounded-input-bounded-output stability requirement for the controller design is circumvented using an off-line, finite interval of time training scheme. The aircraft model is first identified using a neural network with linear filter (also known as time-delayed neural network) with the available input-output data...
In this dissertation, the adaptive critic neural network technique is applied to solve complex nonlinear system control problems. Based on dynamic programming, the adaptive critic neural network can embed the optimal solution into a neural network. Though trained off-line, the neural network forms a real-time feedback controller. Because of its general interpolation properties, the neurocontroller has inherit robustness. The problems solved here are an agile missile control for U.S. Air Force and a midcourse guidance law for U.S. Navy. In the first three papers, the neural network was used to control an air-to-air agile missile to implement a minimum-time heading-reverse in a vertical plane corresponding to following conditions: a system without constraint, a system with control inequality constraint, and a system with state inequality constraint. While the agile missile is a one-dimensional problem, the midcourse guidance law is the first test-bed for multiple-dimensional problem. In the fourth paper, the neurocontroller is synthesized to guide a surface-to-air missile to a fixed final condition, and to a flexible final condition from a variable initial condition. In order to evaluate the adaptive critic neural network approach, the numerical solutions for these cases are also obtained by solving two-point boundary value problem with a shooting method. All of the results showed that the adaptive critic neural network could solve complex nonlinear system control problems.
Lemos, João M; Igreja, José M
This book describes methods for adaptive control of distributed-collector solar fields: plants that collect solar energy and deliver it in thermal form. Controller design methods are presented that can overcome difficulties found in these type of plants:they are distributed-parameter systems, i.e., systems with dynamics that depend on space as well as time;their dynamics is nonlinear, with a bilinear structure;there is a significant level of uncertainty in plant knowledge.Adaptive methods form the focus of the text because of the degree of uncertainty in the knowledge of plant dynamics. Parts
Thor I. Fossen
Full Text Available The problem of controlling underwater mobile robots in 6 degrees of freedom (DOF is addressed. Uncertainties in the input matrix due to partly known nonlinear thruster characteristics are modeled as multiplicative input uncertainty. This paper proposes two methods to compensate for the model uncertainties: (1 an adaptive passivity-based control scheme and (2 deriving a hybrid (adaptive and sliding controller. The hybrid controller consists of a switching term which compensates for uncertainties in the input matrix and an on-line parameter estimation algorithm. Global stability is ensured by applying Barbalat's Lyapunovlike lemma. The hybrid controller is simulated for the horizontal motion of the Norwegian Experimental Remotely Operated Vehicle (NEROV.
Full Text Available This paper deals with experimental comparison between stable adaptive controllers of robotic manipulators based on Model Based Adaptive, Neural Network and Wavelet -Based control. The above control methods were compared with each other in terms of computational efficiency, need for accurate mathematical model of the manipulator and tracking performances. An original management algorithm of the Wavelet Network control scheme has been designed, with the aim of constructing the net automatically during the trajectory tracking, without the need to tune it to the trajectory itself. Experimental tests, carried out on a planar two link manipulator, show that the Wavelet-Based control scheme, with the new management algorithm, outperforms the conventional Model-Based schemes in the presence of structural uncertainties in the mathematical model of the robot, without pre-training and more efficiently than the Neural Network approach.
Wang, Ding; He, Haibo; Liu, Derong
Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized. Hence, this survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems. The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized stabilization of interconnected systems. Additionally, further comprehensive discussions are presented, including event-based robust control design, improvement of the critic learning rule, nonlinear H ∞ control design, and several notes on future perspectives. By applying the ADP-based optimal and robust control methods to a practical power system and an overhead crane plant, two typical examples are provided to verify the effectiveness of theoretical results. Overall, this survey is beneficial to promote the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.
Frost, Susan A.; Balas, Mark J.
We propose a new framework called Evolving Systems to describe the self-assembly, or autonomous assembly, of actively controlled dynamical subsystems into an Evolved System with a higher purpose. An introduction to Evolving Systems and exploration of the essential topics of the control and stability properties of Evolving Systems is provided. This chapter defines a framework for Evolving Systems, develops theory and control solutions for fundamental characteristics of Evolving Systems, and provides illustrative examples of Evolving Systems and their control with adaptive key component controllers.
Nguyen, Nhan T.; Boskovic, Jovan D.
This paper presents a bounded linear stability analysis for a hybrid adaptive control that blends both direct and indirect adaptive control. Stability and convergence of nonlinear adaptive control are analyzed using an approximate linear equivalent system. A stability margin analysis shows that a large adaptive gain can lead to a reduced phase margin. This method can enable metrics-driven adaptive control whereby the adaptive gain is adjusted to meet stability margin requirements.
Polderman, Jan W.; Mareels, I.M.Y.; Mareels, Iven
Simple adaptive controllers based on high gain output feedback suffer a lack of robustness with respect to bounded disturbances. Existing modifications achieve boundedness of all solutions but introduce solutions that, even in the absence of disturbances, do not achieve regulation. In this paper a
In the design of a Model Reference Adaptive Control system, a reference model serves as the (well-known) basis through which system and user requirements can find their way into the design. By tuning the design parameters, the response of the actual vehicle should track the response of the
Ploeg, J.; Semsar-Kazerooni, E.; Lijster, G.; Wouw, N. van de; Nijmeijer, H.
Cooperative adaptive cruise control (CACC) employs wireless intervehicle communication, in addition to onboard sensors, to obtain string-stable vehicle-following behavior at small intervehicle distances. As a consequence, however, CACC is vulnerable to communication impairments such as latency and
Ramesh, A. V.; Utku, S.
Forward and inverse kinematics equations are derived for the large geometry maneuver of adaptive trusses. A new algorithm based on higher-order multistep methods is proposed as a means of computing the length control for a described large geometry maneuver. The algorithm is shown to improve in computational speed at least five times over the algorithm presented in an earlier paper. The acceleration in control computation and other features such as varying velocity profiles and curved trajectories are illustrated by simulation results.
Maher, M.; Bahhou, B.; Roux, G. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Maher, M. [Faculte des Sciences, Rabat (Morocco). Lab. de Physique
This paper presents a multivariable adaptive control of a continuous-flow fermentation process for the alcohol production. The linear quadratic control strategy is used for the regulation of substrate and ethanol concentrations in the bioreactor. The control inputs are the dilution rate and the influent substrate concentration. A robust identification algorithm is used for the on-line estimation of linear MIMO model`s parameters. Experimental results of a pilot-plant fermenter application are reported and show the control performances. (authors) 8 refs.
Robust and Adaptive Control shows the reader how to produce consistent and accurate controllers that operate in the presence of uncertainties and unforeseen events. Driven by aerospace applications the focus of the book is primarily on continuous-dynamical systems. The text is a three-part treatment, beginning with robust and optimal linear control methods and moving on to a self-contained presentation of the design and analysis of model reference adaptive control (MRAC) for nonlinear uncertain dynamical systems. Recent extensions and modifications to MRAC design are included, as are guidelines for combining robust optimal and MRAC controllers. Features of the text include: · case studies that demonstrate the benefits of robust and adaptive control for piloted, autonomous and experimental aerial platforms; · detailed background material for each chapter to motivate theoretical developments; · realistic examples and simulation data illustrating key features ...
The control input to the plant is given by the sum of the output of conventional MRAC and the output of NN. The proposed Neural Network -based Model Reference Adaptive Controller (NN-MRAC) can significantly improve the system behavior and force the system to follow the reference model and minimize the error ...
Full Text Available Adaptive traffic control systems represent complex, but powerful tool for improvement of traffic flow conditions in locations or zones where applied. Many traffic agencies, especially those that have a large number of signalized intersections with high variability of the traffic demand, choose to apply some of the adaptive traffic control systems. However, those systems are manufactured and offered by multiple vendors (companies that are competing for the market share. Due to that fact, besides the information available from the vendors themselves, or the information from different studies conducted on different continents, very limited amount of information is available about the details how those systems are operating. The reason for that is the protecting of the intellectual property from plagiarism. The primary goal of this paper is to make a brief analysis of the functionalities, characteristics, abilities and results of the most recognized, but also less known adaptive traffic control systems to the professional public and other persons with interest in this subject.
Full Text Available In this study, vertical rail vehicle vibrations are controlled by the use of conventional PID and parameters which are adaptive to PID controllers. A parameters adaptive PID controller is designed to improve the passenger comfort by intuitional usage of this method that renews the parameters online and sensitively under variable track inputs. Sinusoidal vertical rail misalignment and measured real rail irregularity are considered as two different disruptive effects of the system. Active vibration control is applied to the system through the secondary suspension. The active suspension application of rail vehicle is examined by using 5-DOF quarter-rail vehicle model by using Manchester benchmark dynamic parameters. The new parameters of adaptive controller are optimized by means of genetic algorithm toolbox of MATLAB. Simulations are performed at maximum urban transportation speed (90 km/h of the rail vehicle with ±5% load changes of rail vehicle body to test the robustness of controllers. As a result, superior performance of parameters of adaptive controller is determined in time and frequency domain.
Full Text Available Abstract. This paper deals with adaptive regulation of a discrete-time linear time-invariant plant witharbitrary bounded disturbances whose control input is constrained to lie within certain limits. The adaptivecontrol algorithm exploits the one-step-ahead control strategy and the gradient projection type estimationprocedure using the modified dead zone. The convergence property of the estimation algorithm is shown tobe ensured. The sufficient conditions guaranteeing the global asymptotical stability and simultaneously thesuboptimality of the closed-loop systems are derived. Numerical examples and simulations are presented tosupport the theoretical results.
Full Text Available Nonlinear systems are modeled as piecewise linear systems at multiple operating points, where the operating points are modeled as switches between constituent linearized systems. In this paper, adaptive piecewise linear switch controller is proposed for improving the response time and tracking performance of the hydraulic actuator control system, which is essentially piecewise linear. The controller composed of PID and Model Reference Adaptive Control (MRAC adaptively chooses the proportion of these two components and makes the designed system have faster response time at the transient phase and better tracking performance, simultaneously. Then, their stability and tracking performance are analyzed and evaluated by the hydraulic actuator control system, the hydraulic actuator is controlled by the electrohydraulic system, and its model is built, which has piecewise linear characteristic. Then the controller results are compared between PID and MRAC and the switch controller designed in this paper is applied to the hydraulic actuator; it is obvious that adaptive switch controller has better effects both on response time and on tracking performance.
Schumann, Johann; Gupta, Pramod
We present a tool to estimate the performance of the neural network in a neural network based adaptive controller. Using a Bayesian approach, this tool supports verification and validation of the adaptive controller as well as on-line monitoring. In this paper, we discuss our approach and present simulation results using the adaptive controller developed for NASA's IFCS (Intelligent Flight Control System) project.
Kahveci, Nazli E.
The objective of meeting higher endurance requirements remains a challenging task for any type and size of Unmanned Aerial Vehicles (UAVs). According to recent research studies significant energy savings can be realized through utilization of thermal currents. The navigation strategies followed across thermal regions, however, are based on rather intuitive assessments of remote pilots and lack any systematic path planning approaches. Various methods to enhance the autonomy of UAVs in soaring applications are investigated while seeking guarantees for flight performance improvements. The dynamics of the aircraft, small UAVs in particular, are affected by the environmental conditions, whereas unmodeled dynamics possibly become significant during aggressive flight maneuvers. Besides, the demanded control inputs might have a magnitude range beyond the limits dictated by the control surface actuators. The consequences of ignoring these issues can be catastrophic. Supporting this claim NASA Dryden Flight Research Center reports considerable performance degradation and even loss of stability in autonomous soaring flight tests with the subsequent risk of an aircraft crash. The existing control schemes are concluded to suffer from limited performance. Considering the aircraft dynamics and the thermal characteristics we define a vehicle-specific trajectory optimization problem to achieve increased cross-country speed and extended range of flight. In an environment with geographically dispersed set of thermals of possibly limited lifespan, we identify the similarities to the Vehicle Routing Problem (VRP) and provide both exact and approximate guidance algorithms for the navigation of automated UAVs. An additional stochastic approach is used to quantify the performance losses due to incorrect thermal data while dealing with random gust disturbances and onboard sensor measurement inaccuracies. One of the main contributions of this research is a novel adaptive control design with
In light of the complex and highly uncertain nature of dynamical systems requiring controls, it is not surprising that reliable system models for many high performance engineering and life science applications are unavailable. In the face of such high levels of system uncertainty, robust controllers may unnecessarily sacrifice system performance whereas adaptive controllers are clearly appropriate since they can tolerate far greater system uncertainty levels to improve system performance. In this dissertation, we develop a Lyapunov-based direct adaptive and neural adaptive control framework that addresses parametric uncertainty, unstructured uncertainty, disturbance rejection, amplitude and rate saturation constraints, and digital implementation issues. Specifically, we consider the following research topics; direct adaptive control for nonlinear uncertain systems with exogenous disturbances; robust adaptive control for nonlinear uncertain systems; adaptive control for nonlinear uncertain systems with actuator amplitude and rate saturation constraints; adaptive reduced-order dynamic compensation for nonlinear uncertain systems; direct adaptive control for nonlinear matrix second-order dynamical systems with state-dependent uncertainty; adaptive control for nonnegative and compartmental dynamical systems with applications to general anesthesia; direct adaptive control of nonnegative and compartmental dynamical systems with time delay; adaptive control for nonlinear nonnegative and compartmental dynamical systems with applications to clinical pharmacology; neural network adaptive control for nonlinear nonnegative dynamical systems; passivity-based neural network adaptive output feedback control for nonlinear nonnegative dynamical systems; neural network adaptive dynamic output feedback control for nonlinear nonnegative systems using tapped delay memory units; Lyapunov-based adaptive control framework for discrete-time nonlinear systems with exogenous disturbances
Morales, S.; Dahhou, B.; Dilhac, J.M. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Morales, S.
In Rapid Thermal Processing (RTP) control of the wafer temperature during all processing to get good trajectory following, together with spatial temperature uniformity, is essential. It is well know as RTP process is nonlinear, classical control laws are not very efficient. In this work, the authors aim at studying the applicability of MIMO (Multiple Inputs Multiple Outputs) adaptive techniques to solve the temperature control problems in RTP. A multivariable linear discrete time CARIMA (Controlled Auto Regressive Integrating Moving Average) model of the highly non-linear process is identified on-line using a robust identification technique. The identified model is used to compute an infinite time LQ (Linear Quadratic) based control law, with a partial state reference model. This reference model smooths the original setpoint sequence, and at the same time gives a tracking capability to the LQ control law. After an experimental open-loop investigation, the results of the application of the adaptive control law are presented. Finally, some comments on the future difficulties and developments of the application of adaptive control in RTP are given. (author) 13 refs.
McDonald, P. V.; Riccio, G. E.
Our research on postural control and human-environment interactions provides an appropriate scientific foundation for understanding the skill of mass handling by astronauts in weightless conditions (e.g., extravehicular activity or EVA). We conducted an investigation of such skills in NASA's principal mass-handling simulator, the Precision Air-Bearing Floor, at the Johnson Space Center. We have studied skilled movement-body within a multidisciplinary context that draws on concepts and methods from biological and behavioral sciences (e.g., psychology, kinesiology and neurophysiology) as well as bioengineering. Our multidisciplinary research has led to the development of measures, for manual interactions between individuals and the substantial environment, that plausibly are observable by human sensory systems. We consider these methods to be the most important general contribution of our EVA investigation. We describe our perspective as control theoretic because it draws more on fundamental concepts about control systems in engineering than it does on working constructs from the subdisciplines of biomechanics and motor control in the bio-behavioral sciences. At the same time, we have attempted to identify the theoretical underpinnings of control-systems engineering that are most relevant to control by human beings. We believe that these underpinnings are implicit in the assumptions that cut across diverse methods in control-systems engineering, especially the various methods associated with "nonlinear control", "fuzzy control," and "adaptive control" in engineering. Our methods are based on these theoretical foundations rather than on the mathematical formalisms that are associated with particular methods in control-systems engineering. The most important aspects of the human-environment interaction in our investigation of mass handling are the functional consequences that body configuration and stability have for the pick up of information or the achievement of
Nguyen, Charles C.; Antrazi, Sami S.; Zhou, Zhen-Lei; Campbell, Charles E., Jr.
A joint-space adaptive control scheme for controlling noncompliant motion of a Stewart platform-based manipulator (SPBM) was implemented in the Hardware Real-Time Emulator at Goddard Space Flight Center. The six-degrees of freedom SPBM uses two platforms and six linear actuators driven by dc motors. The adaptive control scheme is based on proportional-derivative controllers whose gains are adjusted by an adaptation law based on model reference adaptive control and Liapunov direct method. It is concluded that the adaptive control scheme provides superior tracking capability as compared to fixed-gain controllers.
Omur Can Ozguney
Full Text Available The robot industry has developed along with the increasing the use of robots in industry. This has led to increase the studies on robots. The most important part of these studies is that the robots must be work with minimum tracking trajectory error. But it is not easy for robots to track the desired trajectory because of the external disturbances and parametric uncertainty. Therefore adaptive and robust controllers are used to decrease tracking error. The aim of this study is to increase the tracking performance of the robot and minimize the trajectory tracking error. For this purpose adaptive control law for robot manipulator is identified and fuzzy logic controller is applied to find the accurate values for adaptive control parameter. Based on the Lyapunov theory stability of the uncertain system is guaranteed. In this study robot parameters are assumed to be unknown. This controller is applied to a robot model and the results of simulations are given. Controller with fuzzy logic and without fuzzy logic are compared with each other. Simulation results show that the fuzzy logic controller has improved the results.
Stein, Erwin; Ramm, E
... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Error-controlled Adaptive Finite-element-methods . . . . . . . . . . . . Missing Features and Properties of Today's General Purpose FE Programs for Structural...
Frost, Susan A.; Balas, Mark J.
Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a model reference direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will augment the adaptive controller using a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. We apply these theoretical results to design an adaptive collective pitch controller for a high-fidelity simulation of a utility-scale, variable-speed wind turbine that has minimum phase zeros.
Full Text Available Multimedia communications are communications with several types of media, such as audio, video and data. The current Internet has some levels of capability to support multimedia communications, unfortunately, the QoS (Quality of Service is still challenging. A large number of QoS mechanisms has been proposed; however, the main concern is for low levels, e.g. layer 2 (Data Link or 3 (Transport. In this paper, mechanisms for control the quality of audio and video are proposed. G.723.1 and MPEG-4 are used as the audio and video codec respectively. The proposed algorithm for adaptive quality control of audio communication is based on forward error correction (FEC. In the case of video communication, the proposed algorithm adapts the value of key frame interval, which is an encoding parameter of MPEG-4. We evaluated our proposed algorithms by computer simulation. We have shown that, in most cases, the proposed scheme gained a higher throughput compared to other schemes.
Handelman, David A.
The agility and adaptability of biological systems are worthwhile goals for next-generation unmanned ground vehicles. Management of the requisite number of degrees of freedom, however, remains a challenge, as does the ability of an operator to transfer behavioral intent from human to robot. This paper reviews American Android research funded by NASA, DARPA, and the U.S. Army that attempts to address these issues. Limb coordination technology, an iterative form of inverse kinematics, provides a fundamental ability to control balance and posture independently in highly redundant systems. Goal positions and orientations of distal points of the robot skeleton, such as the hands and feet of a humanoid robot, become variable constraints, as does center-of-gravity position. Behaviors utilize these goals to synthesize full-body motion. Biped walking, crawling and grasping are illustrated, and behavior parameterization, layering and portability are discussed. Robotic skill acquisition enables a show-and-tell approach to behavior modification. Declarative rules built verbally by an operator in the field define nominal task plans, and neural networks trained with verbal, manual and visual signals provide additional behavior shaping. Anticipated benefits of the resultant adaptive collaborative controller for unmanned ground vehicles include increased robot autonomy, reduced operator workload and reduced operator training and skill requirements.
Haley, Pam; Soloway, Don; Gold, Brian
The objective of this paper is to demonstrate the feasibility of a Nonlinear Generalized Predictive Control algorithm by showing real-time adaptive control on a plant with relatively fast time-constants. Generalized Predictive Control has classically been used in process control where linear control laws were formulated for plants with relatively slow time-constants. The plant of interest for this paper is a magnetic levitation device that is nonlinear and open-loop unstable. In this application, the reference model of the plant is a neural network that has an embedded nominal linear model in the network weights. The control based on the linear model provides initial stability at the beginning of network training. In using a neural network the control laws are nonlinear and online adaptation of the model is possible to capture unmodeled or time-varying dynamics. Newton-Raphson is the minimization algorithm. Newton-Raphson requires the calculation of the Hessian, but even with this computational expense the low iteration rate make this a viable algorithm for real-time control.
Henningsen, Arne; Ravn, Ole
A comparison of the standard concepts in MRAC design suggests that a combination of the implicit and the explicit design techniques may lead to an improvement of the overall system performance in the presence of unmodelled dynamics. Using the ideas of adaptive stability augmentation a combined...
Johnson, K. E.
The primary focus of this work is a new adaptive controller that is designed to resemble the standard non-adaptive controller used by the wind industry for variable speed wind turbines below rated power. This adaptive controller uses a simple, highly intuitive gain adaptation law designed to seek out the optimal gain for maximizing the turbine's energy capture. It is designed to work even in real, time-varying winds.
Efremov, A. V.; Tiaglik, M. S.; Tiumentsev, Yu V.
We need to provide motion control of modern and advanced aircraft under diverse uncertainty conditions. This problem can be solved by using adaptive control laws. We carry out an analysis of the capabilities of these laws for such adaptive systems as MRAC (Model Reference Adaptive Control) and MPC (Model Predictive Control). In the case of a nonlinear control object, the most efficient solution to the adaptive control problem is the use of neural network technologies. These technologies are suitable for the development of both a control object model and a control law for the object. The approximate nature of the ANN model was taken into account by introducing additional compensating feedback into the control system. The capabilities of adaptive control laws under uncertainty in the source data are considered. We also conduct simulations to assess the contribution of adaptivity to the behavior of the system.
Benitez R, J.S.
Linearization by feedback of states is based on the idea of transform the nonlinear dynamic equation of a system in a linear form. This linear behavior can be achieve well in a complete way (input state) or in partial way (input output). This can be applied to systems of single or multiple inputs, and can be used to solve problems of stabilization and tracking of references trajectories. Comparing this method with conventional ones, linearization by feedback of states is exact in certain region of the space of state, instead of linear approximations of the equations in a certain point of the operation. In the presence of parametric uncertainties in the model of the system, the introduction of adaptive schemes provide a type toughness to the control system by nonlinear feedback, which gives as result the eventual cancellation of the nonlinear terms in the dynamic relationship between the output and the input of the auxiliary control. In the same way, it has been presented the design of a nonlinearizing control for the non lineal model of a TRIGA Mark III type reactor, with the aim of tracking a predetermined power profile. The asymptotic tracking of such profile is, at the present moment, in the stage of verification by computerized simulation the relative easiness in the design of auxiliary variable of control, as well as the decoupling action of the output variable, make very attractive the utilization of the method herein presented. (Author)
Kedar-Dongarkar, Gurunath; Weslati, Feisel
A powertrain control system for a plugin hybrid electric vehicle. The system comprises an adaptive charge sustaining controller; at least one internal data source connected to the adaptive charge sustaining controller; and a memory connected to the adaptive charge sustaining controller for storing data generated by the at least one internal data source. The adaptive charge sustaining controller is operable to select an operating mode of the vehicle's powertrain along a given route based on programming generated from data stored in the memory associated with that route. Further described is a method of adaptively controlling operation of a plugin hybrid electric vehicle powertrain comprising identifying a route being traveled, activating stored adaptive charge sustaining mode programming for the identified route and controlling operation of the powertrain along the identified route by selecting from a plurality of operational modes based on the stored adaptive charge sustaining mode programming.
Full Text Available On-line monitoring devices to control functions such as volume, body temperature, and ultrafiltration, were considered more toys than real tools for routine clinical application. However, bio-feedback blood volume controlled hemodialysis (HD is now possible in routine dialysis, allowing the delivery of a more physiologically acceptable treatment. This system has proved to reduce the incidence of intra-HD hypotension episodes significantly. Ionic dialysance and the patient′s plasma conductivity can be calculated easily from on-line measurements at two different steps of dialysate conductivity. A bio-feedback system has been devised to calculate the patient′s plasma conductivity and modulate the conductivity of the dialysate continuously in order to achieve a desired end-dialysis patient plasma conductivity corresponding to a desired end-dialysis plasma sodium concentration. Another bio-feedback system can control the body tempe-rature by measuring it at the arterial and venous lines of the extra-corporeal circuit, and then modulating the dialysate temperature in order to stabilize the patients′ temperature at constant values that result in improved intra-HD cardiovascular stability. The module can also be used to quantify vascular access recirculation. Finally, the simultaneous computer control of ultrafiltration has proven the most effective means for automatic blood pressure stabilization during hemo-dialysis treatment. The application of fuzzy logic in the blood-pressure-guided biofeedback con-trol of ultrafiltration during hemodialysis is able to minimize HD-induced hypotension. In con-clusion, online monitoring and adaptive control of the patient during the dialysis session using the bio-feedback systems is expected to render the process of renal replacement therapy more physiological and less eventful.
Full Text Available For the generator excitation control system which is equipped with static var compensator (SVC and unknown parameters, a novel adaptive dynamic surface control scheme is proposed based on neural network and tracking error transformed function with the following features: (1 the transformation of the excitation generator model to the linear systems is omitted; (2 the prespecified performance of the tracking error can be guaranteed by combining with the tracking error transformed function; (3 the computational burden is greatly reduced by estimating the norm of the weighted vector of neural network instead of the weighted vector itself; therefore, it is more suitable for the real time control; and (4 the explosion of complicity problem inherent in the backstepping control can be eliminated. It is proved that the new scheme can make the system semiglobally uniformly ultimately bounded. Simulation results show the effectiveness of this control scheme.
research activities that aim to alleviate this problem. In this paper, the L1 adaptive controller has been introduced to suppress the PIO, which is caused by rate limiting and pure time delay. Due to its architecture, the L1 adaptive controller will achieve a desired response with fast adaptation. The analysis of PIO and its suppression by L1 adaptive controller are presented in detail in the paper. The simulation results indicate that the L1 adaptive control is efficient in solving this kind of problem.
Jantzen, Jan; Poulsen, Niels Kjølstad
This simulation study provides an analysis of the adaptation mechanism in the self-organising fuzzy controller, SOC. The approach is to apply a traditional adaptive control viewpoint. A simplified performance measure in the SOC controller is used in a loss function, and thus the MIT rule implies...... an update mechanism similar to the SOC update mechanism. Two simulations of proportionally controlled systems show the behaviour of the proportional gain as it adapts to a specified behaviour....
Ponnusamy, Sangeeth Saagar; Bordeneuve-Guibé, Joël
The application of adaptive output feedback augmentative control to the flexible aircraft problem is presented. Experimental validation of control scheme was carried out using a three disk torsional pendulum. In the reference model adaptive control scheme, the rigid aircraft reference model and neural network adaptation is used to control structural flexible modes and compensate for the effects unmodeled dynamics and parametric variations of a classical high order large passenger aircraft. Th...
K. Prabhu; V. Murali Bhaskaran
Continues Stirred Tank Reactor (CSTR) is an important issue in chemical process and a wide range of research in the area of chemical engineering. Temperature Control of CSTR has been an issue in the chemical control engineering since it has highly non-linear complex equations. This study presents problem of temperature control of CSTR with the adaptive Controller. The Simulation is done in MATLAB and result shows that adaptive controller is an efficient controller for temperature control of C...
.... Additional application areas included optical communication systems, blind identification and deconvolution, active control of noise and vibration, and detection of damage in elastic structures...
Full Text Available In this paper, two types of robust adaptive compensation control schemes for the trajectory tracking control of robot manipulator with uncertain dynamics are proposed. The proposed controllers incorporate the computed-torque control scheme as a nominal portion of the controller; an adaptive fuzzy control algorithm to approximate the structured uncertainties; and a nonlinear H∞ tracking control model as a feedback portion to eliminate the effects of the unstructured uncertainties and approximation errors. The validity of the robust adaptive compensation control schemes is investigated by numerical simulations of a two-link rotary robot manipulator
Henningsen, Arne; Ravn, Ole
stability augmented model reference design is proposed. By utilizing the closed-loop control error, a simple auxiliary controller is tuned, using a normalized MIT rule for the parameter adjustment. The MIT adjustment is protected against the effects of unmodelled dynamics by lowpass filtering...
van Nort, Douglas
parameters. In this view, desired gestural dynamics and sonic response are achieved through modular construction of mapping layers that are themselves subject to parametric control. Complementing this view of the design process, the work concludes with an approach in which the creation of gestural control/sound dynamics are considered in the low-level of the underlying sound model. The result is an adaptive system that is specialized to noise-based transformations that are particularly relevant in an electroacoustic music context. Taken together, these different approaches to design and evaluation result in a unified framework for creation of an instrumental system. The key point is that this framework addresses the influence that mapping structure and control dynamics have on the perceived feel of the instrument. Each of the results illustrate this using either top-down or bottom-up approaches that consider musical control context, thereby pointing to the greater potential for refined sonic articulation that can be had by combining them in the design process.
Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.
Full Text Available Stress initiates adaptive processes that allow the organism to physiologically cope with prolonged or intermittent exposure to real or perceived threats. A major component of this response is repeated activation of glucocorticoid secretion by the hypothalamo-pituitary-adrenocortical (HPA axis, which promotes redistribution of energy in a wide range of organ systems, including the brain. Prolonged or cumulative increases in glucocorticoid secretion can reduce benefits afforded by enhanced stress reactivity and eventually become maladaptive. The long-term impact of stress is kept in check by the process of habituation, which reduces HPA axis responses upon repeated exposure to homotypic stressors and likely limits deleterious actions of prolonged glucocorticoid secretion. Habituation is regulated by limbic stress-regulatory sites, and is at least in part glucocorticoid feedback-dependent. Chronic stress also sensitizes reactivity to new stimuli. While sensitization may be important in maintaining response flexibility in response to new threats, it may also add to the cumulative impact of glucocorticoids on the brain and body. Finally, unpredictable or severe stress exposure may cause long-term and lasting dysregulation of the HPA axis, likely due to altered limbic control of stress effector pathways. Stress-related disorders, such as depression and PTSD, are accompanied by glucocorticoid imbalances and structural/ functional alterations in limbic circuits that resemble those seen following chronic stress, suggesting that inappropriate processing of stressful information may be part of the pathological process.
Nguyen, Nhan T.; Burken, John; Ishihara, Abraham
This paper presents a new adaptive control approach using Chebyshev orthogonal polynomials as basis functions in a least-squares functional approximation. The use of orthogonal basis functions improves the function approximation significantly and enables better convergence of parameter estimates. Flight control simulations demonstrate the effectiveness of the proposed adaptive control approach.
National Aeronautics and Space Administration — Adaptive control offers an opportunity to fulfill aircraft safety objectives though automated vehicle recovery while maintaining performance and stability...
National Aeronautics and Space Administration — To address the NASA need for quiet on-orbit crew quarters (CQ), Physical Optics Corporation (POC) proposes to develop a new Adaptive Intelligent Ventilation Noise...
National Aeronautics and Space Administration — To address NASA needs for quiet crew volumes in a space habitat, Physical Optics Corporation (POC) proposes to develop a new Adaptive Intelligent Ventilation Noise...
The active reduction of acoustic noise is achieved by the addition of a cancelling acoustic signal to the unwanted sound. Successful definition of the cancelling signal amounts to a system identification problem. Recent advances in adaptive signal processing have allowed this problem to be tackled using adaptive filters, which offer significant advantages over conventional solutions. The extension of adaptive noise cancelling techniques, which were developed in the electrical signal conditioning context, to the control of acoustic systems is studied. An analysis is presented of the behavior of the Widrow-Hoff LMS adaptive noise canceller with a linear filter in its control loop. The active control of plane waves propagating axially in a hardwalled duct is used as a motivating model problem. The model problem also motivates the study of the effects of feedback around an LMS adaptive filter. An alternative stochastic gradient algorithm for controlling adaptive filters in the presence of feedback is presented.
Full Text Available In this paper, an adaptive controller is designed for a UAV flight control system against faults and parametric uncertainties based on quantum information technology and the Popov hyperstability theory. First, considering the bounded control input, the state feedback controller is designed to make the system stable. The model of adaptive control is introduced to eliminate the impact by the uncertainties of system parameters via quantum information technology. Then, according to the model reference adaptive principle, an adaptive control law based on the Popov hyperstability theory is designed. This law enable better robustness of the flight control system and tracking control performances. The closed-loop system's stability is guaranteed by the Popov hyperstability theory. The simulation results demonstrate that a better dynamic performance of the UAV flight control system with faults and parametric uncertainties can be maintained with the proposed method.
Choi, Han Ho; Jung, Jin-Woo; Kim, Rae-Young
A fuzzy adaptive speed controller is proposed for a permanent magnet synchronous motor (PMSM). The proposed fuzzy adaptive speed regulator is insensitive to model parameter and load torque variations because it does not need any accurate knowledge about the motor parameter and load torque values. The stability of the proposed control system is also proven. The proposed adaptive speed regulator system is implemented by using a TMS320F28335 floating point DSP. Simulation and experimental results are presented to verify the effectiveness of the proposed fuzzy adaptive speed controller under uncertainties such as motor parameter and load torque variations using a prototype PMSM drive system.
Schumann, Johann; Gupta, Pramod
Traditional control has proven to be ineffective to deal with catastrophic changes or slow degradation of complex, highly nonlinear systems like aircraft or spacecraft, robotics, or flexible manufacturing systems. Control systems which can adapt toward changes in the plant have been proposed as they offer many advantages (e.g., better performance, controllability of aircraft despite of a damaged wing). In the last few years, use of neural networks in adaptive controllers (neuro-adaptive control) has been studied actively. Neural networks of various architectures have been used successfully for online learning adaptive controllers. In such a typical control architecture, the neural network receives as an input the current deviation between desired and actual plant behavior and, by on-line training, tries to minimize this discrepancy (e.g.; by producing a control augmentation signal). Even though neuro-adaptive controllers offer many advantages, they have not been used in mission- or safety-critical applications, because performance and safety guarantees cannot b e provided at development time-a major prerequisite for safety certification (e.g., by the FAA or NASA). Verification and Validation (V&V) of an adaptive controller requires the development of new analysis techniques which can demonstrate that the control system behaves safely under all operating conditions. Because of the requirement to adapt toward unforeseen changes during operation, i.e., in real time, design-time V&V is not sufficient.
Power, M.A.; Edwards, R.M.
Simulation results are presented for an adaptive H ∞ controller, a fixed H ∞ controller, and a classical controller. The controllers are applied to a simulation of the Experimental Breeder Reactor II primary system. The controllers are tested for the best robustness and performance by step-changing the demanded reactor power and by varying the combined uncertainty in initial reactor power and control rod worth. The adaptive H ∞ controller shows the fastest settling time, fastest rise time and smallest peak overshoot when compared to the fixed H ∞ and classical controllers. This makes for a superior and more robust controller
Full Text Available Automatic profiling machine is a movement system that has a high degree of parameter variation and high frequency of transient process, and it requires an accurate control in time. In this paper, the discrete model reference adaptive control system of automatic profiling machine is discussed. Firstly, the model of automatic profiling machine is presented according to the parameters of DC motor. Then the design of the discrete model reference adaptive control is proposed, and the control rules are proven. The results of simulation show that adaptive control system has favorable dynamic performances.
The paper describes the design considerations and implementational aspects of the Adaptive Blockset for Simulink which has been developed in a prototype implementation. The concept behind the Adaptive Blockset for Simulink is to bridge the gap between simulation and prototype controller implement...... design, controller and state variable filter.The use of the Adaptive Blockset is demonstrated using a simple laboratory setup. Both the use of the blockset for simulation and for rapid prototyping of a real-time controller are shown.......The paper describes the design considerations and implementational aspects of the Adaptive Blockset for Simulink which has been developed in a prototype implementation. The concept behind the Adaptive Blockset for Simulink is to bridge the gap between simulation and prototype controller...
Yucelen, Tansel (Inventor); Kim, Kilsoo (Inventor); Calise, Anthony J. (Inventor)
An adaptive control system is disclosed. The control system can control uncertain dynamic systems. The control system can employ one or more derivative-free adaptive control architectures. The control system can further employ one or more derivative-free weight update laws. The derivative-free weight update laws can comprise a time-varying estimate of an ideal vector of weights. The control system of the present invention can therefore quickly stabilize systems that undergo sudden changes in dynamics, caused by, for example, sudden changes in weight. Embodiments of the present invention can also provide a less complex control system than existing adaptive control systems. The control system can control aircraft and other dynamic systems, such as, for example, those with non-minimum phase dynamics.
Orlicki, D.; Valavani, L.; Athans, M.; Stein, G.
It has been found that fixed error dead-zones as defined in the existing literature result in serious degradation of performance, due to the conservativeness which characterizes the determination of their width. In the present paper, variable width dead-zones are derived for the adaptive control of plants with unmodeled dynamics. The derivation makes use of information available about the unmodeled dynamics both a priori as well as during the adaptation process, so as to stabilize the adaptive loop and at the same time overcome the conservativeness and performance limitations of fixed-dead zone adaptive or fixed gain controllers.
Ferrari, Silvia; Steck, James E; Chandramohan, Rajeev
A constrained approximate dynamic programming (ADP) approach is presented for designing adaptive neural network (NN) controllers with closed-loop stability and performance guarantees. Prior knowledge of the linearized equations of motion is used to guarantee that the closed-loop system meets performance and stability objectives when the plant operates in a linear parameter-varying (LPV) regime. In the presence of unmodeled dynamics or failures, the NN controller adapts to optimize its performance online, whereas constrained ADP guarantees that the LPV baseline performance is preserved at all times. The effectiveness of an adaptive NN flight controller is demonstrated for simulated control failures, parameter variations, and near-stall dynamics.
Cherrat, N.; Boubertakh, H.; Arioui, H.
This paper deals with the design of an adaptive fuzzy PID control law for attitude and altitude stabilization of a quadrotor despite the system model uncertainties and disturbances. To this end, a PID control with adaptive gains is used in order to approximate a virtual ideal control previously designed to achieve the predefined objective. Specifically, the control gains are estimated and adjusted by mean of a fuzzy system whose parameters are adjusted online via a gradient descent-based adaptation law. The analysis of the closed-loop system is given by the Lyapunov approach. The simulation results are presented to illustrate the efficiency of the proposed approach.
Andersen, T.O.; Hansen, M.R.; Conrad, Finn
control.Using the Lyapunov approach, under slowly time-varying assumptions, it is shown that the tracking error and the parameter error remain bounded. This bound is a function of the ideal parameters and a bounded disturbance. The control algorithm decouples and linearizes the manipulator so that each......A method for synthesis of a robust adaptive scheme for a hydraulically driven manipulator, that takes full advantage of any known system dynamics to simplify the adaptive control problem for the unknown portion of the dynamics is presented. The control method is based on adaptive perturbation...
Hormetic dose response occurs for many endpoints associated with exposures of biological organisms to environmental stressors. Cell-based U- or inverted U-shaped responses may derive from common processes involved in activation of adaptive responses required to protect cells from...
Odgaard, Peter Fogh; Stoustrup, Jakob; Vidal, Enrique Sanchez
-players playing CDs with surface fault is derived and described. This feature based control scheme uses precomputed base to remove the surface fault influence from the position measurements. In this paper an adaptive version of the feature based control scheme is proposed and described. This adaptive scheme can...... result that the adaptive scheme clearly adapts better to the given faults compared with the non-adaptive version of the feature based control scheme.......Many have experienced the problem that their Compact Disc players have difficulties playing Compact Discs with surface faults like scratches and fingerprints. The cause of this is due to the two servo control loops used to keep the Optical Pick-up Unit focused and radially on the information track...
Wise, Steven L.; And Others
This study investigated the relationship between examinee achievement-specific locus of control and the differences between self-adapted testing (SAT) and computerized adaptive testing (CAT) in terms of mean estimated proficiency and posttest state anxiety. Subjects were 379 college students. A disordinal interaction was found between test type…
Full Text Available A B-spline neural networks-based adaptive control technique for angular speed reference trajectory tracking tasks with highly efficient performance for direct current shunt motors is proposed. A methodology for adaptive control and its proper training procedure are introduced. This algorithm sets the control signal without using a detailed mathematical model nor exact values of the parameters of the nonlinear dynamic system. The proposed robust adaptive tracking control scheme only requires measurements of the velocity output signal. Thus, real-time measurements or estimations of acceleration, current and disturbance signals are avoided. Experimental results confirm the efficient and robust performance of the proposed control approach for highly demanding motor operation conditions exposed to variable-speed reference trajectories and completely unknown load torque. Hence, laboratory experimental tests on a direct current shunt motor prove the viability of the proposed adaptive output feedback trajectory tracking control approach.
© 2015 Mathematical Sciences Publishers. Adaptive step-size control is a critical feature for the robust and efficient numerical solution of initial-value problems in ordinary differential equations. In this paper, we show that adaptive step-size control can be incorporated within a family of parallel time integrators known as revisionist integral deferred correction (RIDC) methods. The RIDC framework allows for various strategies to implement stepsize control, and we report results from exploring a few of them.
ARL-TR-8085 ● AUG 2017 US Army Research Laboratory Adaptive Missile Flight Control for Complex Aerodynamic Phenomena by Frank...Adaptive Missile Flight Control for Complex Aerodynamic Phenomena by Frank Fresconi and Jubaraj Sahu Weapons and Materials Research Directorate...currently valid OMB control number . PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. 1. REPORT DATE (DD-MM-YYYY) August 2017 2. REPORT TYPE
Herzallah, R.; Kárný, Miroslav
Roč. 24, č. 10 (2011), s. 1128-1135 ISSN 0893-6080 R&D Projects: GA ČR GA102/08/0567 Institutional research plan: CEZ:AV0Z10750506 Keywords : Stochastic control design * Fully probabilistic design * Adaptive control * Adaptive critic Subject RIV: BC - Control Systems Theory Impact factor: 2.182, year: 2011 http://library.utia.cas.cz/separaty/2011/AS/karny-0364820.pdf
Kato, Akio; Wada, Yoshihisa
Control to improve control characteristics of aircraft, CA (Control Augmentation), is used to realize the desirable motion of aircraft corresponding to pilot's control action. When the control laws using fuzzy inference were designed, trial and error was repeated for optimization of the parameter. Here, in designing control laws using fuzzy neural networks, the systematic optimization of the parameter was possible using the learning algorithm usually used in neural networks, by expressing the fuzzy inference in the form of neural networks. Here, the control laws, which learned the characteristics of the aircraft for one flight condition only, were used in all flight conditions without changing any parameter. Evaluation of the designed control laws showed good performance in all flight conditions. This proves that fuzzy neural networks are an effective and flexible method when applied to control laws for control augmentation of aircraft.
Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.
Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.
Adaptive control techniques are well suited to applications that have unknown modeling parameters and poorly known operating conditions. Many physical systems experience external disturbances that are persistent or continually recurring. Flexible structures and systems with compliance between components often form a class of systems that fail to meet standard requirements for adaptive control. For these classes of systems, a residual mode filter can restore the ability of the adaptive controller to perform in a stable manner. New theory will be presented that enables adaptive control with accommodation of persistent disturbances using residual mode filters. After a short introduction to some of the control challenges of large utility-scale wind turbines, this theory will be applied to a high-fidelity simulation of a wind turbine.
Ho Dac Loc
Full Text Available In this paper, an adaptive fuzzy controller is designed for the robot-manipulator. The synthesized controller ensures that 1 the close-loop system is globally stable and 2 the tracking error converges to zero asymptotically and a cost function is minimized. The fuzzy controller is synthesized from a collection of IF-THEN rules. The parameters of the membership functions characterizing the linguistic terms change according to some adaptive law for the purpose of controlling a plant to track a reference trajectory. The proposed control scheme is demonstrated in a typical nonlinear plant two link manipulator. The computer simulation of control is done by the language MATLAB. The results of simulation show that the adaptive controller well operates and provides good qualities of the control system. The presented results are analyzed.
Nguyen, Nhan; Krishnakumar, Kalmanje; Kaneshige, John; Nespeca, Pascal
This paper presents a recent study of a damaged generic transport model as part of a NASA research project to investigate adaptive control methods for stability recovery of damaged aircraft operating in off-nominal flight conditions under damage and or failures. Aerodynamic modeling of damage effects is performed using an aerodynamic code to assess changes in the stability and control derivatives of a generic transport aircraft. Certain types of damage such as damage to one of the wings or horizontal stabilizers can cause the aircraft to become asymmetric, thus resulting in a coupling between the longitudinal and lateral motions. Flight dynamics for a general asymmetric aircraft is derived to account for changes in the center of gravity that can compromise the stability of the damaged aircraft. An iterative trim analysis for the translational motion is developed to refine the trim procedure by accounting for the effects of the control surface deflection. A hybrid direct-indirect neural network, adaptive flight control is proposed as an adaptive law for stabilizing the rotational motion of the damaged aircraft. The indirect adaptation is designed to estimate the plant dynamics of the damaged aircraft in conjunction with the direct adaptation that computes the control augmentation. Two approaches are presented 1) an adaptive law derived from the Lyapunov stability theory to ensure that the signals are bounded, and 2) a recursive least-square method for parameter identification. A hardware-in-the-loop simulation is conducted and demonstrates the effectiveness of the direct neural network adaptive flight control in the stability recovery of the damaged aircraft. A preliminary simulation of the hybrid adaptive flight control has been performed and initial data have shown the effectiveness of the proposed hybrid approach. Future work will include further investigations and high-fidelity simulations of the proposed hybrid adaptive Bight control approach.
IJtsma (Student TU Delft), Martijn; Borst, C.; Mercado Velasco, G.A.; Mulder, M.; van Paassen, M.M.; Tsang, P.S.; Vidulich, M.A.
Through smart scheduling and triggering of automation support, adaptive automation has the potential to balance air traffic controller workload. The challenge in the design of adaptive automation systems is to decide how and when the automation should provide support. This paper describes the design
L M WANG
Aug 16, 2017 ... A novel model-free adaptive sliding mode strategy is proposed for a generalized projective synchronization (GPS) ... the neural network theory, a model-free adaptive sliding mode controller is designed to guarantee asymptotic stability of the generalized ..... following optimization parameters are needed: ⎧.
Sonneveldt, L.; Van Oort, E.R.; Chu, Q.P.; Mulder, J.A.
In this paper a theoretical framework for nonlinear adaptive flight control is developed and applied to a simplified, over-actuated nonlinear fighter aircraft model. The framework is based on a modular adaptive backstepping scheme with a new type of nonlinear estimator. The nonlinear estimator is
Broom, Donald M
The term adaptation is used in biology in three different ways. It may refer to changes which occur at the cell and organ level, or at the individual level, or at the level of gene action and evolutionary processes. Adaptation by cells, especially nerve cells helps in: communication within the body, the distinguishing of stimuli, the avoidance of overload and the conservation of energy. The time course and complexity of these mechanisms varies. Adaptive characters of organisms, including adaptive behaviours, increase fitness so this adaptation is evolutionary. The major part of this paper concerns adaptation by individuals and its relationships to welfare. In complex animals, feed forward control is widely used. Individuals predict problems and adapt by acting before the environmental effect is substantial. Much of adaptation involves brain control and animals have a set of needs, located in the brain and acting largely via motivational mechanisms, to regulate life. Needs may be for resources but are also for actions and stimuli which are part of the mechanism which has evolved to obtain the resources. Hence pigs do not just need food but need to be able to carry out actions like rooting in earth or manipulating materials which are part of foraging behaviour. The welfare of an individual is its state as regards its attempts to cope with its environment. This state includes various adaptive mechanisms including feelings and those which cope with disease. The part of welfare which is concerned with coping with pathology is health. Disease, which implies some significant effect of pathology, always results in poor welfare. Welfare varies over a range from very good, when adaptation is effective and there are feelings of pleasure or contentment, to very poor. A key point concerning the concept of individual adaptation in relation to welfare is that welfare may be good or poor while adaptation is occurring. Some adaptation is very easy and energetically cheap and
Schmidt, Lasse; Andersen, Torben Ole; Pedersen, Henrik C.
. The main target is to overcome problems with linear controllers deteriorating performance due to the inherent nonlinear nature of such systems, without requiring extensive knowledge on system parameters nor advanced control theory. In order to accomplish this task, an integral sliding mode controller...
Szelitzky, Tibor; Henrietta Dulf, Eva
Permanent variations of the electric properties of the load in induction heating equipment make difficult to control the plant. To overcome these disadvantages, the authors propose a new approach based on adaptive control methods. For real plants it is enough to present desired performances or start-up variables for the controller, from which the algorithms tune the controllers by itself. To present the advantages of the proposed controllers, comparisons are made to a PI controller tuned through Ziegler-Nichols method.
Alex M C Smith
Full Text Available In this paper we present a hybrid control scheme, combining the advantages of task-space and joint-space control. The controller is based on a human-like adaptive design, which minimises both control effort and tracking error. Our novel hybrid adaptive controller has been tested in extensive simulations, in a scenario where a Baxter robot manipulator is affected by external disturbances in the form of interaction with the environment and tool-like end-effector perturbations. The results demonstrated improved performance in the hybrid controller over both of its component parts. In addition, we introduce a novel method for online adaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledge from the experimenter. This mechanism of meta-learning induces further improvement in performance and avoids the need for tuning through trial testing.
Taeed, Fazel; Nymand, Morten
An adaptive slope compensation method for digital current mode control of dc-dc converters is proposed in this paper. The compensation slope is used for stabilizing the inner current loop in peak current mode control. In this method, the compensation slope is adapted with the variations...... in converter duty cycle. The adaptive slope compensation provides optimum controller operation in term of bandwidth over wide range of operating points. In this paper operation principle of the controller is discussed. The proposed controller is implemented in an FPGA to control a 100 W buck converter....... The experimental results of measured loop-gain at different operating points are presented to validate the theoretical performance of the controller....
, (LS) identification and generalized predictive control (GPC) has been implemented and tested on the CVC drive. Allthough GPC is a robust control method, it was not possible to maintain specified controller performance in the entire operating range. This was the main reason for investigating truly...... adaptive speed control of the CVC drive. A direct truly adaptive speed controller has been implemented. The adaptive controller is a moving Average Self-Tuning Regulator which is abbreviated MASTR throughout the thesis. Two practical implementations of this controller were proposed. They were denoted MASTR...... and measurement noise in general, were the major reasons for the drifting parameters. Two approaches was proposed to robustify MASTR2 against the output noise. The first approach consists of filtering the output. Output filtering had a significant effect in simulations, but the robustness against the output noise...
National Aeronautics and Space Administration — SSCI, in collaboration with Boeing Phantom Works, proposes to develop and test an efficient Integrated Damage Adaptive Control System (IDACS). The proposed system is...
This report documents the work completed by the Crash Avoidance Metrics Partners LLC (CAMP) Vehicle to Infrastructure (V2I) Consortium during the project titled Cooperative Adaptive Cruise Control (CACC). Participating companies in the V2I Cons...
Full Text Available Inaccurate system parameters and unpredicted external disturbances affect the performance of non-linear controllers. In this paper, a new adaptive control algorithm under the reinforcement framework is proposed to stabilize a quadrotor helicopter. Based on a command-filtered non-linear control algorithm, adaptive elements are added and learned by policy-search methods. To predict the inaccurate system parameters, a new kernel-based regression learning method is provided. In addition, Policy learning by Weighting Exploration with the Returns (PoWER and Return Weighted Regression (RWR are utilized to learn the appropriate parameters for adaptive elements in order to cancel the effect of external disturbance. Furthermore, numerical simulations under several conditions are performed, and the ability of adaptive trajectory-tracking control with reinforcement learning are demonstrated.
Popovskij, Vladimir; Titarenko, Larysa
This book is devoted to mathematical foundations providing synthesis and analysis of control and adaptation algorithms targeting modern telecommunication systems (TCS). The most popular technologies and network management methods are discussed.
Dirkx, Kim; Kester, Liesbeth; Kirschner, Paul A.
Dirkx, K. J. H., Kester, L., & Kirschner, P. A. (2011, September). Optimizing adaptive learning through testing, diagnostic reflection and learner control. Presentation for visitors of KU Leuven, Open University, Heerlen, The Netherlands.
National Aeronautics and Space Administration — SSCI proposes to further develop, implement and test the damage-adaptive control algorithms developed in Phase I within the framework of an Integrated Damage...
National Aeronautics and Space Administration — Most fault adaptive control research addresses the preservation of system stability or functionality in the presence of a specific failure (fault). This paper...
National Aeronautics and Space Administration — The proposed project is the continued development of the High Efficiency Lighting with Integrated Adaptive Control (HELIAC) system. Solar radiation is not a viable...
National Aeronautics and Space Administration — The innovation of the proposed project is the development of High Efficiency Lighting with Integrated Adaptive Control (HELIAC) systems to drive plant growth. Solar...
The objective for this study was to investigate whether the postural control adaptation during galvanic stimulation of the vestibular nerve were similar to that found during vibration stimulation to the calf muscles...
.... The objective of the Real-time Application Performance Steering and Adaptive Control project is to replace ad hoc, post-mortem performance optimization with an extensible, portable, and distributed...
Wen, John T.; Kreutz-Delgado, Kenneth; Bayard, David S.
A new class of joint level control laws for all-revolute robot arms is introduced. The analysis is similar to a recently proposed energy-like Liapunov function approach, except that the closed-loop potential function is shaped in accordance with the underlying joint space topology. This approach gives way to a much simpler analysis and leads to a new class of control designs which guarantee both global asymptotic stability and local exponential stability. When Coulomb and viscous friction and parameter uncertainty are present as model perturbations, a sliding mode-like modification of the control law results in a robustness-enhancing outer loop. Adaptive control is formulated within the same framework. A linear-in-the-parameters formulation is adopted and globally asymptotically stable adaptive control laws are derived by simply replacing unknown model parameters by their estimates (i.e., certainty equivalence adaptation).
Feng Gang; Chen Guanrong
This paper discusses adaptive control of a class of discrete-time chaotic systems from a fuzzy control approach. Using the T-S model of discrete-time chaotic systems, an adaptive control algorithm is developed based on some conventional adaptive control techniques. The resulting adaptively controlled chaotic system is shown to be globally stable, and its robustness is discussed. A simulation example of the chaotic Henon map control is finally presented, to illustrate an application and the performance of the proposed control algorithm
multioutput ARMA system. The plant has constant but unknown parameters. The cautious controller with a one-step horizon and a new dual controller with a two...system is shown to be stable. Multiinput multioutput systems with unknown parameters are encoun- Remark 4.1: tered in many practical situations, and...dual (181 B. Witterunark, "An active suboptimal dual controller for systems with stochastic c irfsoluin applied t-a multiipvt multioutput model
Belda, Květoslav; Böhm, Josef
Roč. 5, č. 8 (2006), s. 1830-1837 ISSN 1109-2777 R&D Projects: GA ČR GP102/06/P275; GA ČR GA102/05/0271 Institutional research plan: CEZ:AV0Z10750506 Keywords : on-line identification * predictive control * input/output equations of predictions * real-time control Subject RIV: BC - Control Systems Theory http://library.utia.cas.cz/separaty/historie/belda-0040149.pdf
Kárný, Miroslav; Herzallah, R.
Roč. 47, č. 3 (2017), s. 394-404 ISSN 2168-2216 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Adaptive control * Adaptive estimation * Bayes methods * Complex networks * Decentralized control * Feedback * Feedforward systems * Recursive estimation Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 2.350, year: 2016 http://library.utia.cas.cz/separaty/2016/AS/karny-0457337.pdf
Hansen, Poul Erik; Conrad, Finn
Presentation of two new developed adaptive non-liner controllers for hydraulic actuator systems to give stable operation and improved performance.Results from the IMCIA project supported by the Danish Technical Research Council (STVF).......Presentation of two new developed adaptive non-liner controllers for hydraulic actuator systems to give stable operation and improved performance.Results from the IMCIA project supported by the Danish Technical Research Council (STVF)....
Nguyen, Nhan T.
This paper presents a new model-reference adaptive control method based on a bi-objective optimal control formulation for systems with input uncertainty. A parallel predictor model is constructed to relate the predictor error to the estimation error of the control effectiveness matrix. In this work, we develop an optimal control modification adaptive control approach that seeks to minimize a bi-objective linear quadratic cost function of both the tracking error norm and predictor error norm simultaneously. The resulting adaptive laws for the parametric uncertainty and control effectiveness uncertainty are dependent on both the tracking error and predictor error, while the adaptive laws for the feedback gain and command feedforward gain are only dependent on the tracking error. The optimal control modification term provides robustness to the adaptive laws naturally from the optimal control framework. Simulations demonstrate the effectiveness of the proposed adaptive control approach.
Borisov, Oleg I.; Bobtsov, Alexey A.; Pyrkin, Anton A.; Gromov, Vladislav S.
The stabilization problem for quadcopters with saturated actuators is considered. A simple adaptive output control approach is proposed. The control law "consecutive compensator" is augmented with the auxiliary integral loop and anti-windup scheme. Efficiency of the obtained regulator was confirmed by simulation of the quadcopter control problem.
Sawitz, P.; Sullivan, T.
Adaptive power control concepts for the compensation of rain attenuation are considered for uplinks and downlinks. The performance of example power-controlled and fixed-EIRP uplinks is compared in terms of C/Ns and C/Is. Provisional conclusions are drawn with regard to the efficacy of uplink and downlink power control orbit/spectrum utilization efficiency.
Karr, C. L.
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Karr, C. L.
Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.
Ninomiya, Tetsujiro; Miyazawa, Yoshikazu
More and more UAVs are developed for various purposes and their flight controllers are required to have sufficient robustness and performance to realize their versatile missions. To design these sophisticated controller is pretty much time-consuming task by traditional design method. Neural network based adaptive control with dynamic inversion is applied to solve this problem. This method has two advantages. One is that the gain scheduling is not necessary because nonlinear dynamic inversion is applied to control nonlinear systems. The other is that neural network improves the controller performance by estimating parameters of the actual plant. Numerical examples show its effectiveness and its ability to adapt to modeling errors. This paper concludes that proposed method reduces the workload of controller design task and it has ability to adapt various errors of nonlinear systems.
Yuan, Yuan; Tang, J.
In this research, model reference adaptive control is examined for the pitch control of wind turbines that may suffer from reduced life owing to extreme loads and fatigue when operated under a high wind speed. Specifically, we aim at making a trade-off between the maximum energy captured and the load induced. The adaptive controller is designed to track the optimal generator speed and at the same time to mitigate component loads under turbulent wind field and other uncertainties. The proposed algorithm is tested on the NREL offshore 5-MW baseline wind turbine, and its performance is compared with that those of the gain scheduled proportional integral (GSPI) control and the disturbance accommodating control (DAC). The results show that the blade root flapwise load can be reduced at a slight expense of optimal power output. The generator speed regulation under adaptive controller is better than DAC.
Full Text Available An adaptive neural network controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties. The first scheme consists of a PD feedback and a dynamic compensator which is composed by neural network controller and variable structure controller. Neutral network controller is designed to adaptive learn and compensate the unknown uncertainties, variable structure controller is designed to eliminate approach errors of neutral network. The adaptive weight learning algorithm of neural network is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation result s show that the kind of the control scheme is effective and has good robustness.
Berkhoff, Arthur P.; Wesselink, J.M.
Recent implementations of multiple-input multiple-output adaptive controllers for reduction of broadband noise and vibrations provide considerably improved performance over traditional adaptive algorithms. The most significant performance improvements are in terms of speed of convergence, the amount
National Aeronautics and Space Administration — A novel approach is proposed for the suppression of the aircraft's structural vibration to increase the resilience of the flight control law in the presence of the...
Benitez R, J.S.
The study of the behavior of a nuclear reactor is of great importance as it allows to know a priori the conditions at which a reactor is submitted. In the sareactor are the design and simulation of control algorithms based on the theories of modern control with the objective of improving improving the performance criterions as well as to guarantee the the stability of the retrofitting system. (author)
Full Text Available A fed-batch alcohol fermentation on a pilot plant scale with a digital supervisory control system was evaluated as an experimental application case study of an adaptive controller. The verification of intrinsically dynamic variations in the characteristics of the fermentation, observed in previous work, showed the necessity of an adaptive control strategy for controller parameter tuning in order to adjust the changes in the specific rates of consumption, growth and product formation during the process. Satisfactory experimental results were obtained for set-point variations and sugar feed concentration load changes in the manipulated inlet flow to the fermenter
Lian, Jianming; Hu, Jianghai; Żak, Stanislaw H
Variable neural adaptive robust control strategies are proposed for the output tracking control of a class of multiinput multioutput uncertain systems. The controllers incorporate a novel variable-structure radial basis function (RBF) network as the self-organizing approximator for unknown system dynamics. It can determine the network structure online dynamically by adding or removing RBFs according to the tracking performance. The structure variation is systematically considered in the stability analysis of the closed-loop system using a switched system approach with the piecewise quadratic Lyapunov function. The performance of the proposed variable neural adaptive robust controllers is illustrated with simulations.
Full Text Available This paper focuses on precision automated pushing of multiple micro objects. An adaptive control system is proposed to accurately push and position the micro objects on a substrate. Each micro object exhibits different characteristics in terms of the surface micro forces governing the manipulation process. The controller is designed to compensate for the effect of the micro forces whose aggregated magnitude varies during the process. An experimental setup is designed to validate the performance of the proposed controller. The results of the experiments confirm that the proposed adaptive controller is capable of learning to adjust its parameters effectively, when the surface micro forces change under varying surface and ambient conditions.
Full Text Available This paper presents an application of adaptive neuro-fuzzy (ANFIS control for switched reluctance motor (SRM speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI.
Gundy-Burlet, Karen; Krishnakumar, K.; Limes, Greg; Bryant, Don
This paper examines the feasibility, potential benefits and implementation issues associated with retrofitting a neural-adaptive flight control system (NFCS) to existing transport aircraft, including both cable/hydraulic and fly-by-wire configurations. NFCS uses a neural network based direct adaptive control approach for applying alternate sources of control authority in the presence of damage or failures in order to achieve desired flight control performance. Neural networks are used to provide consistent handling qualities across flight conditions, adapt to changes in aircraft dynamics and to make the controller easy to apply when implemented on different aircraft. Full-motion piloted simulation studies were performed on two different transport models: the Boeing 747-400 and the Boeing C-17. Subjects included NASA, Air Force and commercial airline pilots. Results demonstrate the potential for improving handing qualities and significantly increased survivability rates under various simulated failure conditions.
Lefevre, Brian D.
For the purpose of maintaining dynamic stability and improving guidance command tracking performance under off-nominal flight conditions, a hybrid adaptive control scheme is selected and modified for use as a launch vehicle flight controller. This architecture merges a model reference adaptive approach, which utilizes both direct and indirect adaptive elements, with a classical dynamic inversion controller. This structure is chosen for a number of reasons: the properties of the reference model can be easily adjusted to tune the desired handling qualities of the spacecraft, the indirect adaptive element (which consists of an online parameter identification algorithm) continually refines the estimates of the evolving characteristic parameters utilized in the dynamic inversion, and the direct adaptive element (which consists of a neural network) augments the linear feedback signal to compensate for any nonlinearities in the vehicle dynamics. The combination of these elements enables the control system to retain the nonlinear capabilities of an adaptive network while relying heavily on the linear portion of the feedback signal to dictate the dynamic response under most operating conditions. To begin the analysis, the ascent dynamics of a launch vehicle with a single 1st stage rocket motor (typical of the Ares 1 spacecraft) are characterized. The dynamics are then linearized with assumptions that are appropriate for a launch vehicle, so that the resulting equations may be inverted by the flight controller in order to compute the control signals necessary to generate the desired response from the vehicle. Next, the development of the hybrid adaptive launch vehicle ascent flight control architecture is discussed in detail. Alterations of the generic hybrid adaptive control architecture include the incorporation of a command conversion operation which transforms guidance input from quaternion form (as provided by NASA) to the body-fixed angular rate commands needed by the
Full Text Available We propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. In order to design a semiactive control system with high control performance against earthquakes with different time and/or frequency properties, multiple semiactive control laws with high performance for each of multiple earthquake disturbances are scheduled with an adaptive manner. Each semiactive control law to be scheduled is designed based on the output emulation approach that has been proposed by the authors. As the adaptive gain scheduling mechanism, we introduce an artificial neural network (ANN. Input signals of the ANN are the measured earthquake disturbance itself, for example, the acceleration, velocity, and displacement. The output of the ANN is the parameter for the scheduling of multiple semiactive control laws each of which has been optimized for a single disturbance. Parameters such as weight and bias in the ANN are optimized by the genetic algorithm (GA. The proposed design method is applied to semiactive control design of a base-isolated building with a semiactive damper. With simulation study, the proposed adaptive gain scheduling method realizes control performance exceeding single semiactive control optimizing the average of the control performance subject to various earthquake disturbances.
Full Text Available The structural scheme of operators studying process control and management in the training complex is proposed for observation. Structural scheme includes the division of operators upon the subgroups according to the level of demonstrated knowledge. Also in the article the example of such divisions criteria is presented.
Vyacheslav K. Mayevski
Full Text Available This paper describes a mathematical model of an industrial chemical reactor for production of synthetic rubber. During reactor operation the model parameters vary considerably. To create a control algorithm performed transformation of mathematical model of the reactor in order to obtain a dependency that can be used to determine the model parameters are changing during reactor operation.
Mathieu and van der Pol equations, for studying dynamical behaviour, chaos control and its synchronization. The van der Pol oscillator is a nonconservative oscillator with nonlinear damping. Due to this feature, it has become popular for systems with limit cycle oscillations in physics, biology, sociology and even economics.
The study demonstrates the feasibility of two eco-driving applications which reduces vehicle fuel consumption and greenhouse gas emissions. In particular, the study develops an eco-drive system that combines eco-cruise control logic with state-of-the...
Spencer, Michael G.; Sanner, Robert M.; Chopra, Inderjit
This paper presents research into developing an adaptive nonlinear neural network control algorithm that can be used with smart structure actuators and sensors to control the vibrations of rotor blades. The dynamic equations of motion for a blade have the same form as a multilink manipulator (robot arm) and adaptive nonlinear control algorithms have proven successful in active control of these manipulators. The recent development of neural network control algorithms has provided the ability to adaptively learn in real time a set of parameters that will approximate external forces operating on the blades. The controller combines these two control techniques enabling the controller to adapt its parameters in response to changes in blade properties such as its mass or stiffness and to also learn the parameters necessary to account for the unknown but bounded, periodic disturbance forces such as those caused by the unsteady, periodic aerodynamic forces in the rotor system. Current efforts have been directed at testing the control algorithm on real beams with piezoceramic actuators and sensors. The initial test results have shown that vibration reduction and desired beam motion tracking can be achieved even under the influences of periodic disturbances.
Seraji, Homayoun; Lee, Thomas; Delpech, Michel
The implementation and experimental validation of a new direct adaptive control scheme on a PUMA 560 industrial robot is described. The testbed facility consists of a Unimation PUMA 560 six-jointed robot and controller, and a DEC MicroVAX II computer which hosts the Robot Control C Library software. The control algorithm is implemented on the MicroVAX which acts as a digital controller for the PUMA robot, and the Unimation controller is effectively bypassed and used merely as an I/O device to interface the MicroVAX to the joint motors. The control algorithm for each robot joint consists of an auxiliary signal generated by a constant-gain Proportional plus Integral plus Derivative (PID) controller, and an adaptive position-velocity (PD) feedback controller with adjustable gains. The adaptive independent joint controllers compensate for the inter-joint couplings and achieve accurate trajectory tracking without the need for the complex dynamic model and parameter values of the robot. Extensive experimental results on PUMA joint control are presented to confirm the feasibility of the proposed scheme, in spite of strong interactions between joint motions. Experimental results validate the capabilities of the proposed control scheme. The control scheme is extremely simple and computationally very fast for concurrent processing with high sampling rates.
Bensalem, Y.; Sbita, L.; Abdelkrim, M. N.
This paper proposes an adaptive neural network speed control scheme for an induction motor (IM) drive. The proposed scheme consists of an adaptive neural network identifier (ANNI) and an adaptive neural network controller (ANNC). For learning the quoted neural networks, a back propagation algorithm was used to automatically adjust the weights of the ANNI and ANNC in order to minimize the performance functions. Here, the ANNI can quickly estimate the plant parameters and the ANNC is used to provide on-line identification of the command and to produce a control force, such that the motor speed can accurately track the reference command. By combining artificial neural network techniques with fuzzy logic concept, a neural and fuzzy adaptive control scheme is developed. Fuzzy logic was used for the adaptation of the neural controller to improve the robustness of the generated command. The developed method is robust to load torque disturbance and the speed target variations when it ensures precise trajectory tracking with the prescribed dynamics. The algorithm was verified by simulation and the results obtained demonstrate the effectiveness of the IM designed controller
Carlos A. Saldarriaga-Cortés
Full Text Available This paper presents a methodology to implement an adaptive control of the inverted pendulum system; which uses the recursive square minimum method for the identification of a dynamic digital model of the plant and then, with its estimated parameters, tune in real time a pole placement control. The plant to be used is an unstable and nonlinear system. This fact, combined with the adaptive controller characteristics, allows the obtained results to be extended to a great variety of systems. The results show that the above methodology was implemented satisfactorily in terms of estimation, stability and control of such a system. It was established that adaptive techniques have a proper performance even in systems with complex features such as nonlinearity and instability.
Full Text Available This article proposes a novel adaptive switching control of hypersonic aircraft based on type-2 Takagi–Sugeno–Kang fuzzy sliding mode control and focuses on the problem of stability and smoothness in the switching process. This method uses full-state feedback to linearize the nonlinear model of hypersonic aircraft. Combining the interval type-2 Takagi–Sugeno–Kang fuzzy approach with sliding mode control keeps the adaptive switching process stable and smooth. For rapid stabilization of the system, the adaptive laws use a direct constructive Lyapunov analysis together with an established type-2 Takagi–Sugeno–Kang fuzzy logic system. Simulation results indicate that the proposed control scheme can maintain the stability and smoothness of switching process for the hypersonic aircraft.
Patre, Parag; Joshi, Suresh M.
Typical adaptive controllers are restricted to using a specific update law to generate parameter estimates. This paper investigates the possibility of using any exponential parameter estimator with an adaptive controller such that the system tracks a desired trajectory. The goal is to provide flexibility in choosing any update law suitable for a given application. The development relies on a previously developed concept of controller/update law modularity in the adaptive control literature, and the use of a converse Lyapunov-like theorem. Stability analysis is presented to derive gain conditions under which this is possible, and inferences are made about the tracking error performance. The development is based on a class of Euler-Lagrange systems that are used to model various engineering systems including space robots and manipulators.
Yang, Bong-Jun; Calise, Anthony J.; Craig, James I.; Whorton, Mark S.
Neural network-based adaptive control is considered for active control of a highly flexible truss structure which may be used to support solar sail membranes. The objective is to suppress unwanted vibrations in SAFE (Solar Array Flight Experiment) boom, a test-bed located at NASA. Compared to previous tests that restrained truss structures in planar motion, full three dimensional motions are tested. Experimental results illustrate the potential of adaptive control in compensating for nonlinear actuation and modeling error, and in rejecting external disturbances.
Alexey A. Bobtsov
Full Text Available The problem of adaptive output control for parametric and functionally uncertain plants is considered. Application examples illustrating the practical use of the discussed theory are given along with the mathematical formulation of the problem. A brief review of adaptive output control methods, by both linear and non-linear systems, is presented and an extensive bibliography, in which the reader will find a detailed description of the specific algorithms and their properties, is represented. A new approach to the output control problem - a method of consecutive compensator - is considered in detail.
Adaptive FilteringL Prediction and Control, pp. 178-181, Prentice-Hall, Inc., 1984. 3. Astrom , Karl J. and Wittenmark, B., Computer Controlled Systems, pp...Academic Press, Inc., 1980. 7. Astrom , K.J., "Theory and Applications of Adaptive Control: A Survey," Automatica, V. 19, pp. 471-473, September, 1983...September, 1984. 9. Astrom , K.J., Hagander, P., and Sternby, J., "Zeros of Sampled Systems, " Automatica, V. 20, pp. 31-38, January, 1984. 10. Tuschak, R
APR EZT:: F I AN 73S e OsL:-: UNCLASSI?:-- Q4~.’~- .A.., 6, *~*i i~~*~~*.- U ADAPTIVE INCENTIVE CONTROLS FOR STACKELBERG GAMES WITH UNKNOWN COST...AD-A161 885 ADAPTIVE INCENTIVE CONTROLS FOR STACKELBERG GAMES WITH i/1 UNKNOWN COST FUNCTIONALSCU) ILLINOIS UNIV AT URBANA DECISION AND CONTROL LAB T...ORGANIZATION 6b. OFFICE SYMBOL 7.. NAME OF MONITORING ORGANIZATION CoriaeLcenef~pda~ Joint Services Electronics Program Laboratory, Univ. of Illinois N/A
Gunne J. Hegglid
Full Text Available This paper describes an adaptive multivariable control system for hydroelectric generating units. The system is based on a detailed mathematical model of the synchronous generator, the water turbine, the exiter system and turbine control servo. The models of the water penstock and the connected power system are static. These assumptions are not considered crucial. The system uses a Kalman filter for optimal estimation of the state variables and the parameters of the electric grid equivalent. The multivariable control law is computed from a Riccatti equation and is made adaptive to the generators running condition by means of a least square technique.
Tian, Yu-Chu; Gao, Furong
A simple delay system governed by a first-order differential-delay equation may behave chaotically, but the conditions for the system to have such behaviors have not been well recognized. In this paper, a set of rules is postulated first for the conditions for the delay system to display chaos. A model-reference adaptive control scheme is then proposed to control the chaotic system state to converge to an arbitrarily given reference trajectory with certain and uncertain system parameters. Numerical examples are given to analyze the chaotic behaviors of the delay system and to demonstrate the effectiveness of the proposed adaptive control scheme.
Full Text Available This paper deals with the application of a simple adaptive algorithm for robust tracking control of an electro-pneumatic clutch actuator with output feedback. We present a mathematical model of the strongly nonlinear system, and implement an adaptive algorithm, based on a parallel feedforward compensator (PFC to remove the relative-degree-1 restriction. We propose a practical method of constructing the PFC, and introduce a simple modification that removes an inherent restriction on bandwidth of the nonlinear system. We show that the adaptive algorithm deals well with nonlinearities, and we achieve tracking corresponding to a settling-time of 150 ms.
Graybiel, Ann M.; Aosaki, Toshihiko; Flaherty, Alice W.; Kimura, Minoru
The basal ganglia are neural structures within the motor and cognitive control circuits in the mammalian forebrain and are interconnected with the neocortex by multiple loops. Dysfunction in these parallel loops caused by damage to the striatum results in major defects in voluntary movement, exemplified in Parkinson's disease and Huntington's disease. These parallel loops have a distributed modular architecture resembling local expert architectures of computational learning models. During sensorimotor learning, such distributed networks may be coordinated by widely spaced striatal interneurons that acquire response properties on the basis of experienced reward.
Christensen, Anders; Ravn, Ole
SLLAB is a MATLAB-family software package for solving control and identification problems. This paper concerns the planning of a general-purpose subroutine structure for solving identification and adaptive control problems. A general-purpose identification algorithm is suggested, which allows a t...
Bagchi, Arunabha; Chen, Han-Fu
We study linear-quadratic adaptive tracking problems for a special class of stochastic systems expressed in the state-space form. This is a long-standing problem in the control of aircraft flying through atmospheric turbulence. Using an ELS-based algorithm and introducing dither in the control law
Bagchi, Arunabha; Chen, Han-Fu
We study linear-quadratic adaptive tracking problems for a special class of stochastic systems expressed in the state-space form. This is a long-standing problem in the control of aircraft flying through atmospheric turbulence. Using an ELS-based algorithm and introducing dither in the control law
Klein Wolterink, W.; Heijenk, Geert; Karagiannis, Georgios
Cooperative Adaptive Cruise Control (CACC) is a form of cruise control in which a vehicle maintains a constant headway to its preceding vehicle using radar and vehicle-to-vehicle (V2V) communication. Within the Connect & Drive1 project we have implemented and tested a prototype of such a system,
Klein Wolterink, W.; Karagiannis, Georgios; Brogle, Marc; Masip Bruin, Xavier; Braun, Torsten; Heijenk, Gerhard J.
Cooperative Adaptive Cruise Control (CACC) is a form of cruise control in which a vehicle maintains a constant headway to its preceding vehicle using radar and vehicle-to-vehicle (V2V) communication. Within the Connect & Drive1 project we have implemented and tested a prototype of such a system,
The paper developed artificial intelligence technique adaptive neuro-fuzzy controller for air conditioning systems at different pressures. The first order Sugeno fuzzy inference system was implemented and utilized for modeling and controller design. In addition, the estimation of the heat transfer rate and water mass flow rate ...
A systematic approach is developed for designing adaptive and reconfigurable nonlinear control systems that are applicable to plants modeled by ordinary differential equations. The nonlinear controller comprising a network of neural networks is taught using a two-phase learning procedure realized through novel techniques for initialization, on-line training, and adaptive critic design. A critical observation is that the gradients of the functions defined by the neural networks must equal corresponding linear gain matrices at chosen operating points. On-line training is based on a dual heuristic adaptive critic architecture that improves control for large, coupled motions by accounting for actual plant dynamics and nonlinear effects. An action network computes the optimal control law; a critic network predicts the derivative of the cost-to-go with respect to the state. Both networks are algebraically initialized based on prior knowledge of satisfactory pointwise linear controllers and continue to adapt on line during full-scale simulations of the plant. On-line training takes place sequentially over discrete periods of time and involves several numerical procedures. A backpropagating algorithm called Resilient Backpropagation is modified and successfully implemented to meet these objectives, without excessive computational expense. This adaptive controller is as conservative as the linear designs and as effective as a global nonlinear controller. The method is successfully implemented for the full-envelope control of a six-degree-of-freedom aircraft simulation. The results show that the on-line adaptation brings about improved performance with respect to the initialization phase during aircraft maneuvers that involve large-angle and coupled dynamics, and parameter variations.
Full Text Available A new hybrid adaptive control algorithm is proposed for the nonlinear system controller design of underwater robot. Compared with the previous works in the controller design of underwater robot, the main advantages of this work are: (1 A new disturbance prediction and compensation model is proposed; (2 A new adaptive fuzzy smoother is proposed for the control input; (3 A time-varying flow disturbance is considered for the control design which is always neglected in many previous works and several practical experiments under different environment were implemented to verify the control performance. The Lyapunov stability theory proves the stability and convergence of this new control system. Simulation and experiment results demonstrate the performance and the effectiveness of this new algorithm.
Mikulowski, Grzegorz M.; Holnicki-Szulc, Jan
The objective of this paper is to present an integrated feedback control concept for adaptive landing gears (ALG) and its experimental validation. Aeroplanes are subjected to high dynamic loads as a result of the impact during each landing. Classical landing gears, which are in common use, are designed in accordance with official regulations in a way that ensures the optimal energy dissipation for the critical (maximum) sink speed. The regulations were formulated in order to ensure the functional capability of the landing gears during an emergency landing. However, the landing gears, whose characteristics are optimized for these critical conditions, do not perform well under normal impact conditions. For that situation it is reasonable to introduce a system that would adapt the characteristics of the landing gears according to the sink speed of landing. The considered system assumes adaptation of the damping force generated by the landing gear, which would perform optimally in an emergency situation and would adapt itself for regular landings as well. This research covers the formulation and design of the control algorithms for an adaptive landing gear based on MR fluid, implementation of the algorithms on an FPGA platform and experimental verification on a lab-scale landing gear device. The main challenge of the research was to develop a control methodology that could operate effectively within 50 ms, which is assumed to be the total duration of the phenomenon. The control algorithm proposed in this research was able to control the energy dissipation process on the experimental stand.
National Aeronautics and Space Administration — We report here on first steps towards integrating systems health monitoring with adaptive contingency controls. In the scenario considered, the adaptive controller...
P. N. Filippenko
Full Text Available The article deals with the problem of very large-scale integration circuit partitioning. A graph is selected as a mathematical model describing integrated circuit. Modification of ant colony optimization algorithm is presented, which is used to solve graph partitioning problem. Ant colony optimization algorithm is an optimization method based on the principles of self-organization and other useful features of the ants’ behavior. The proposed search system is based on ant colony optimization algorithm with the improved method of the initial distribution and dynamic adjustment of the control search parameters. The experimental results and performance comparison show that the proposed method of very large-scale integration circuit partitioning provides the better search performance over other well known algorithms.
Perez Rocha, Andres E.
The main topic of this dissertation is the design, development and implementation of intelligent adaptive control techniques designed to maintain healthy performance of aerospace systems subjected to malfunctions, external parameter changes and/or unmodeled dynamics. The dissertation is focused on the development of novel adaptive control configurations that rely on non-linear functions that appear in the immune system of living organisms as main source of adaptation. One of the main goals of this dissertation is to demonstrate that these novel adaptive control architectures are able to improve overall performance and protect the system while reducing control effort and maintaining adequate operation outside bounds of nominal design. This research effort explores several phases, ranging from theoretical stability analysis, simulation and hardware implementation on different types of aerospace systems including spacecraft, aircraft and quadrotor vehicles. The results presented in this dissertation are focused on two main adaptivity approaches, the first one is intended for aerospace systems that do not attain large angles and use exact feedback linearization of Euler angle kinematics. A proof of stability is presented by means of the circle Criterion and Lyapunov's direct method. The second approach is intended for aerospace systems that can attain large attitude angles (e.g. space systems in gravity-less environments), the adaptation is incorporated on a baseline architecture that uses partial feedback linearization of quaternions kinematics. In this case, the closed loop stability was analyzed using Lyapunov's direct method and Barbalat's Lemma. It is expected that some results presented in this dissertation can contribute towards the validation and certification of direct adaptive controllers.
Zhang, Yanjun; Tao, Gang; Chen, Mou
This paper presents a new study on the adaptive neural network-based control of a class of noncanonical nonlinear systems with large parametric uncertainties. Unlike commonly studied canonical form nonlinear systems whose neural network approximation system models have explicit relative degree structures, which can directly be used to derive parameterized controllers for adaptation, noncanonical form nonlinear systems usually do not have explicit relative degrees, and thus their approximation system models are also in noncanonical forms. It is well-known that the adaptive control of noncanonical form nonlinear systems involves the parameterization of system dynamics. As demonstrated in this paper, it is also the case for noncanonical neural network approximation system models. Effective control of such systems is an open research problem, especially in the presence of uncertain parameters. This paper shows that it is necessary to reparameterize such neural network system models for adaptive control design, and that such reparameterization can be realized using a relative degree formulation, a concept yet to be studied for general neural network system models. This paper then derives the parameterized controllers that guarantee closed-loop stability and asymptotic output tracking for noncanonical form neural network system models. An illustrative example is presented with the simulation results to demonstrate the control design procedure, and to verify the effectiveness of such a new design method.
Nonlinear and adaptive control is generally considered one of the most effective techniques for stabilizing complex nonlinear systems, where linear control techniques may fail completely. Thousands of research papers are published on either theory or applications of nonlinear and adaptive control. But often one obvious question arises how to implement these techniques in real life model? The best answer that one can think of is to develop simple nonlinear control laws which are easy to implement. Moreover for controlling multi-agent systems, it is often required to distribute the control laws based on limited information available among the agents. This research provides some of these issues in the following way. a) Autopilot design for Aerospace systems: this research developes adaptive backstepping and dynamic inversion methods with internal dynamics stabilization for the quadrotor. Quadrotor helicopter models usually show two main characteristics. First, strong coupling among the system states and second, under-actuation where many states are to be controlled with few control inputs. Due to these unique characteristics, the design of stabilizing control inputs is always challenging for quadrotor models. To confront these problems, first, a dynamic inversion technique with zero dynamics stabilization loop is introduced to a practical quadrotor model, second, an adaptive-backstepping technique is developed to a lagrangian quadrotor model. The stabilizing control laws for both of these techniques are developed using on Lyapunov based method; and b) Coordination of multi-agent systems: coordination among multiple agents is generally done based on balanced or bi-directed communication graph models. If the agents are nonlinear and passive then for a balanced graph model synchronization is possible. But, for other than balanced and bi-directed graph models, it is difficult to synchronize nonlinear systems. Moreover, the performance of synchronization is normally
Lauria, F. E.; Prevete, R.; Milo, M.; Visco, S.
We discuss a method to implement in a Boolean neural network a Hebbian rule so to obtain an adaptable universal control system. We start by presenting both the Boolean neural net and the Hebbian rule we have considered. Then we discuss, first, the problems arising when the latter is naively implemented in a Boolean neural net, second, the method consenting us to overcome them and the ensuing adaptable Boolean neural net paradigm. Next, we present the adaptable Boolean neural net as an intelligent control system, actually controlling a writing robot, and discuss how to train it in the execution of the elementary arithmetic operations on operands represented by numerals with an arbitrary number of digits
Ramesh, A. V.; Utku, S.; Wada, B. K.
The fast computation of geometry control in adaptive truss structures involves two distinct parts: the efficient integration of the inverse kinematic differential equations that govern the geometry control and the fast computation of the Jacobian, which appears on the right-hand-side of the inverse kinematic equations. This paper present an efficient parallel implementation of the Jacobian computation on an MIMD machine. Large speedup from the parallel implementation is obtained, which reduces the Jacobian computation to an O(M-squared/n) procedure on an n-processor machine, where M is the number of members in the adaptive truss. The parallel algorithm given here is a good candidate for on-line geometry control of adaptive structures using attached processors.
Full Text Available Two adaptive switching control strategies are proposed for the trajectory tracking problem of robotic manipulator in this paper. The first scheme is designed for the supremum of the bounded disturbance for robot manipulator being known; while the supremum is not known, the second scheme is proposed. Each proposed scheme consists of an adaptive switching law and a PD controller. Based on the Lyapunov stability theorem, it is shown that two new schemes can guarantee tracking performance of the robotic manipulator and be adapted to the alternating unknown loads. Simulations for two-link robotic manipulator are carried out and show that the two schemes can avoid the overlarge input torque, and the feasibility and validity of the proposed control schemes are proved.
Diner, Daniel B.
Proposed control station for remote robot adapts control system to personal characteristics and preferences of operator. Automatically adjusts positions and angles of video cameras and monitors, adjusts characteristics of hand controller, process images, and provides graphical displays serving operator best. System of one or more video cameras, controlled by computer, views workspace of robot, as shown in article, "Movable Cameras and Monitors For Viewing Telemanipulator" (NPO-17837). Control station includes several video monitors, hand controller, image-processing system providing graphical displays, voice-input command system, keyboards, and mouse.
Zhang, Huaguang; Luo, Yanhong; Wang, Ding
There are many methods of stable controller design for nonlinear systems. In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming for Control approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP). The range of systems treated is extensive; affine, switched, singularly perturbed and time-delay nonlinear systems are discussed as are the uses of neural networks and techniques of value and policy iteration. The text features three main aspects of ADP in which the methods proposed for stabilization and for tracking and games benefit from the incorporation of optimal control methods: • infinite-horizon control for which the difficulty of solving partial differential Hamilton–Jacobi–Bellman equations directly is overcome, and proof provided that the iterative value function updating sequence converges to the infimum of all the value functions obtained by admissible control law sequences; • finite-...
Galeazzi, Roberto; Borup, Kasper Trolle; Niemann, Hans Henrik
This paper investigates the feasibility of operating a wind turbine with lightweight tower in the full load region exploiting an adaptive nonlinear controller that allows the turbine to dynamically lean against the wind while maintaining nominal power output. The use of lightweight structures...... for towers and foundations would greatly reduce the construction cost of the wind turbine, however extra features ought be included in the control system architecture to avoid tower collapse. An adaptive backstepping collective pitch controller is proposed for tower point tracking control, i.e. to modify...... the angular deflection of the tower with respect to the vertical axis in response to variations in wind speed. The controller is shown to guarantee asymptotic tracking of the reference trajectory. The performance of the control system is evaluated through deterministic and stochastic simulations including...
Full Text Available Abstract This paper focuses on robust optimal adaptive control strategy to deal with tracking problem of a quadrotor unmanned aerial vehicle (UAV in presence of parametric uncertainties, actuator amplitude constraints, and unknown time-varying external disturbances. First, Lyapunov-based indirect adaptive controller optimized by particle swarm optimization (PSO is developed for multi-input multi-output (MIMO nonlinear quadrotor to prevent input constraints violation, and then disturbance observer-based control (DOBC technique is aggregated with the control system to attenuate the effects of disturbance generated by an exogenous system. The performance of synthesis control method is evaluated by a new performance index function in time-domain, and the stability analysis is carried out using Lyapunov theory. Finally, illustrative numerical simulations are conducted to demonstrate the effectiveness of the presented approach in altitude and attitude tracking under several conditions, including large time-varying uncertainty, exogenous disturbance, and control input constraints.
Tran Thu Ha
Full Text Available In this paper, an adaptive fuzzy controller is designed for the robot-manipulator. The synthesized controller ensures that 1 the close-loop system is globally stable and 2 the tracking error converges to zero asymptotically and a cost function is minimized. The fuzzy controller is synthesized from a collection of IF-THEN rules. The parameters of the membership functions characterizing the linguistic terms change according to some adaptive law for the purpose of controlling a plant to track a reference trajectory. The proposed control scheme is demonstrated in a typical nonlinear plant two link manipulator. The computer simulation of control is done by the language MATLAB. The results of simulation show that the adaptipresented results are analyzed.
Torrence D J Welch
Full Text Available Balance control must be rapidly modified to provide stability in the face of environmental challenges. Although changes in reactive balance over repeated perturbations have been observed previously, only anticipatory postural adjustments preceding voluntary movements have been studied in the framework of motor adaptation and learning theory. Here, we hypothesized that adaptation occurs in task-level balance control during responses to perturbations due to central changes in the control of both anticipatory and reactive components of balance. Our adaptation paradigm consisted of a Training set of forward support-surface perturbations, a Reversal set of novel countermanding perturbations that reversed direction, and a Washout set identical to the Training set. Adaptation was characterized by a change in a motor variable from the beginning to the end of each set, the presence of aftereffects at the beginning of the Washout set when the novel perturbations were removed, and a return of the variable at the end of the Washout to a level comparable to the end of the Training set. Task-level balance performance was characterized by peak center of mass (CoM excursion and velocity, which showed adaptive changes with repetitive trials. Only small changes in anticipatory postural control, characterized by body lean and background muscle activity were observed. Adaptation was found in the evoked long-latency muscular response, and also in the sensorimotor transformation mediating that response. Finally, in each set, temporal patterns of muscle activity converged towards an optimum predicted by a trade-off between maximizing motor performance and minimizing muscle activity. Our results suggest that adaptation in balance, as well as other motor tasks, is mediated by altering central sensitivity to perturbations and may be driven by energetic considerations.
Liu, Jie; Hu, Youmin; Wu, Bo; Zhou, Kaibo; Ge, Mingfeng
A novel adaptive sliding mode control algorithm is derived to deal with seam tracking control problem of welding robotic manipulator, during the process of large-scale structure component welding. The proposed algorithm does not require the precise dynamic model, and is more practical. Its robustness is verified by the Lyapunov stability theory. The analytical results show that the proposed algorithm enables better high-precision tracking performance with chattering-free than traditional sliding mode control algorithm under various disturbances.
Full Text Available Several multiple model adaptive control architectures have been proposed in the literature. Despite many advances in theory, the crucial question of how to synthesize the pairs model/controller in a structurally optimal way is to a large extent not addressed. In particular, it is not clear how to place the pairs model/controller is such a way that the properties of the switching algorithm (e.g., number of switches, learning transient, final performance are optimal with respect to some criteria. In this work, we focus on the so-called multi-model unfalsified adaptive supervisory switching control (MUASSC scheme; we define a suitable structural optimality criterion and develop algorithms for synthesizing the pairs model/controller in such a way that they are optimal with respect to the structural optimality criterion we defined. The peculiarity of the proposed optimality criterion and algorithms is that the optimization is carried out so as to optimize the entire behavior of the adaptive algorithm, i.e., both the learning transient and the steady-state response. A comparison is made with respect to the model distribution of the robust multiple model adaptive control (RMMAC, where the optimization considers only the steady-state ideal response and neglects any learning transient.
Full Text Available Bottom-fixed vertical rotating devices are widely used in industrial and civilian fields. The free upside of the rotor will cause vibration and lead to noise and damage during operation. Meanwhile, parameter uncertainties, nonlinearities and external disturbances will further deteriorate the performance of the rotor. Therefore, in this paper, we present a rotor orientation control system based on an active magnetic bearing with L 1 adaptive control to restrain the influence of the nonlinearity and uncertainty and reduce the vibration amplitude of the vertical rotor. The boundedness and stability of the adaptive system are analyzed via a theoretical derivation. The impact of the adaptive gain is discussed through simulation. An experimental rig based on dSPACE is designed to test the validity of the rotor orientation system. The experimental results show that the relative vibration amplitude of the rotor using the L 1 adaptive controller will be reduced to ∼50% of that in the initial state, which is a 10% greater reduction than can be achieved with the nonadaptive controller. The control approach in this paper is of some significance to solve the orientation control problem in a low-speed vertical rotor with uncertainties and nonlinearities.
Full Text Available PI controller is very common in the control of DVRs. However, one disadvantage of this conventional controller is its inability to still working well under a wider range of operating conditions. So, as a solution fuzzy controller is proposed in literature. But, the main problem with the conventional fuzzy controllers is that the parameters associated with the membership functions and the rules depend broadly on the intuition of the experts. To overcome this problem, Adaptive Neuro-Fuzzy Inference System (ANFIS based controller design is proposed. The resulted controller is composed of Sugeno fuzzy controller with two inputs and one output. According to the error and error rate of the control system and the output data, ANFIS generates the appropriate fuzzy controller. The simulation results have proved that the proposed design method gives reliable powerful fuzzy controller with a minimum number of membership functions.
Johnson, Eric Norman (Inventor); Calise, Anthony J. (Inventor)
The invention includes an adaptive control system used to control a plant. The adaptive control system includes a hedge unit that receives at least one control signal and a plant state signal. The hedge unit generates a hedge signal based on the control signal, the plant state signal, and a hedge model including a first model having one or more characteristics to which the adaptive control system is not to adapt, and a second model not having the characteristic(s) to which the adaptive control system is not to adapt. The hedge signal is used in the adaptive control system to remove the effect of the characteristic from a signal supplied to an adaptation law unit of the adaptive control system so that the adaptive control system does not adapt to the characteristic in controlling the plant.
Aoi, Shinya; Manoonpong, Poramate; Ambe, Yuichi
for legged robots induced by various factors (locomotion speed, environmental situation, body properties, and task). In addition, we show characteristic properties of adaptive interlimb coordination, such as gait hysteresis and different time-scale adaptations. We also discuss the underlying mechanisms......Walking animals produce adaptive interlimb coordination during locomotion in accordance with their situation. Interlimb coordination is generated through the dynamic interactions of the neural system, the musculoskeletal system, and the environment, although the underlying mechanisms remain unclear....... Recently, investigations of the adaptationmechanisms of living beings have attracted attention, and bio-inspired control systems based on neurophysiological findings regarding sensorimotor interactions are being developed for legged robots. In this review, we introduce adaptive interlimb coordination...
Full Text Available Adaptive mixing control (AMC is a recently developed control scheme for uncertain plants, where the control action coming from a bank of precomputed controller is mixed based on the parameter estimates generated by an on-line parameter estimator. Even if the stability of the control scheme, also in the presence of modeling errors and disturbances, has been shown analytically, its transient performance might be sensitive to the initial conditions of the parameter estimator. In particular, for some initial conditions, transient oscillations may not be acceptable in practical applications. In order to account for such a possible phenomenon and to improve the learning capability of the adaptive scheme, in this paper a new mixing architecture is developed, involving the use of parallel parameter estimators, or multi-estimators, each one working on a small subset of the uncertainty set. A supervisory logic, using performance signals based on the past and present estimation error, selects the parameter estimate to determine the mixing of the controllers. The stability and robustness properties of the resulting approach, referred to as multi-estimator adaptive mixing control (Multi-AMC, are analytically established. Besides, extensive simulations demonstrate that the scheme improves the transient performance of the original AMC with a single estimator. The control scheme and the analysis are carried out in a discrete-time framework, for easier implementation of the method in digital control.
Jorgensen, Charles C.
A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.
Full Text Available Designs of adaptive fuzzy controllers (AFC are commonly based on the Lyapunov approach, which requires a known model of the controlled plant. They need to consider a Lyapunov function candidate as an evaluation function to be minimized. In this study these drawbacks were handled by designing a model-free adaptive fuzzy controller (MFAFC using an approximate evaluation function defined in terms of the current state, the next state, and the control action. MFAFC considers the approximate evaluation function as an evaluative control performance measure similar to the state-action value function in reinforcement learning. The simulation results of applying MFAFC to the inverted pendulum benchmark veriﬁed the proposed scheme’s efficacy.
Full Text Available This paper proposes a new adaptive fuzzy neural control to suppress chaos and also to achieve the speed tracking control in a permanent magnet synchronous motor (PMSM drive system with unknown parameters and uncertainties. The control scheme consists of fuzzy neural and compensatory controllers. The fuzzy neural controller with online parameter tuning is used to estimate the unknown nonlinear models and construct linearization feedback control law, while the compensatory controller is employed to attenuate the estimation error effects of the fuzzy neural network and ensure the robustness of the controlled system. Moreover, due to improvement in controller design, the singularity problem is surely avoided. Finally, numerical simulations are carried out to demonstrate that the proposed control scheme can successfully remove chaotic oscillations and allow the speed to follow the desired trajectory in a chaotic PMSM despite the existence of unknown models and uncertainties.
Full Text Available Artificial pancreas (AP systems offer an important improvement in regulating blood glucose concentration for patients with type 1 diabetes, compared to current approaches. AP consists of sensors, control algorithms and an insulin pump. Different AP control algorithms such as proportional-integral-derivative, model-predictive control, adaptive control, and fuzzy logic control have been investigated in simulation and clinical studies in the past three decades. The variability over time and complexity of the dynamics of blood glucose concentration, unsteady disturbances such as meals, time-varying delays on measurements and insulin infusion, and noisy data from sensors create a challenging system to AP. Adaptive control is a powerful control technique that can deal with such challenges. In this paper, a review of adaptive control techniques for blood glucose regulation with an AP system is presented. The investigations and advances in technology produced impressive results, but there is still a need for a reliable AP system that is both commercially viable and appealing to patients with type 1 diabetes.
Nasirian, Vahidreza; Davoudi, Ali; Lewis, Frank
A distributed-adaptive droop mechanism is proposed for secondary/primary control of dc Microgrids. The conventional secondary control, that adjusts the voltage set point for the local droop mechanism, is replaced by a voltage regulator. A current regulator is then added to fine-tune the droop...... controller precisely accounts for the transmission/distribution line impedances. The controller on each converter exchanges data with only its neighbor converters on a sparse communication graph spanned across the Microgrid. Global dynamic model of the Microgrid is derived, with the proposed controller...
Cheng, George Shu-Xing; Mulkey, Steven L.; Wang, Qiang
A novel 3-Input-3-Output (3.times.3) Model-Free Adaptive (MFA) controller with a set of artificial neural networks as part of the controller is introduced. A 3.times.3 MFA control system using the inventive 3.times.3 MFA controller is described to control key process variables including Power, Steam Throttle Pressure, and Steam Temperature of boiler-turbine-generator (BTG) units in conventional and advanced power plants. Those advanced power plants may comprise Once-Through Supercritical (OTSC) Boilers, Circulating Fluidized-Bed (CFB) Boilers, and Once-Through Supercritical Circulating Fluidized-Bed (OTSC CFB) Boilers.
efficiently updating the weight is useful in many applications such identification of nonlinear systems. Off-line iterative algorithm can be employed in such care of identification or modeling. However, in the aspect of control, the NN should work in on line manner. In the control system structure, the output of NN is the control ...
Sabri Minhat, Mohd; Selamat, Hazlina; Subha, Nurul Adilla Mohd
The 1MWth Reactor TRIGA PUSPATI (RTP) Mark II type has undergone more than 35 years of operation. The existing core power control uses feedback control algorithm (FCA). It is challenging to keep the core power stable at the desired value within acceptable error bands to meet the safety demand of RTP due to the sensitivity of nuclear research reactor operation. Currently, the system is not satisfied with power tracking performance and can be improved. Therefore, a new design core power control is very important to improve the current performance in tracking and regulate reactor power by control the movement of control rods. In this paper, the adaptive controller and focus on Model Reference Adaptive Control (MRAC) and Self-Tuning Control (STC) were applied to the control of the core power. The model for core power control was based on mathematical models of the reactor core, adaptive controller model, and control rods selection programming. The mathematical models of the reactor core were based on point kinetics model, thermal hydraulic models, and reactivity models. The adaptive control model was presented using Lyapunov method to ensure stable close loop system and STC Generalised Minimum Variance (GMV) Controller was not necessary to know the exact plant transfer function in designing the core power control. The performance between proposed adaptive control and FCA will be compared via computer simulation and analysed the simulation results manifest the effectiveness and the good performance of the proposed control method for core power control.
Ahmad M. Zaki
Full Text Available This paper presents a proposed approach based on an adaptive fuzzy logic controller for precise control of the DC motor speed. In this concern, the proposed Direct Adaptive Fuzzy Logic Controller (DAFLC is estimated from two levels, where the lower level uses a Mamdani fuzzy controller and the upper level is an inverse model based on a Takagi–Sugeno (T–S method in which its output is used to adapt the parameters of the fuzzy controller in the lower level. The proposed controller is implemented using an Arduino DUE kit. From the practical results, it is proved that the proposed adaptive controller improves, successfully both the performance response and the disturbance due to the load in the speed control of the DC motor.
Chau, Minh Thuyen
This paper proposes an adaptive current control method for Hybrid Active Power Filter (HAPF). It consists of a fuzzy-neural controller, identification and prediction model and cost function. The fuzzy-neural controller parameters are adjusted according to the cost function minimum criteria. For this reason, the proposed control method has a capability on-line control clings to variation of the load harmonic currents. Compared to the single fuzzy logic control method, the proposed control method shows the advantages of better dynamic response, compensation error in steady-state is smaller, able to online control is better and harmonics cancelling is more effective. Simulation and experimental results have demonstrated the effectiveness of the proposed control method.
Gama, Felipe O. S.; O. Salazar, Andrés
Wireless control systems can sense, control and act on the information exchanged between the wireless sensor nodes in a control loop. However, the exchanged information becomes susceptible to the degenerative effects produced by the multipath propagation. In order to minimize the destructive effects characteristic of wireless channels, several techniques have been investigated recently. Among them, wavelet coding is a good alternative for wireless communications for its robustness to the effects of multipath and its low computational complexity. This work proposes an adaptive wavelet coding whose parameters of code rate and signal constellation can vary according to the fading level and evaluates the use of this transmission system in a control loop implemented by wireless sensor nodes. The performance of the adaptive system was evaluated in terms of bit error rate (BER) versus Eb/N0 and spectral efficiency, considering a time-varying channel with flat Rayleigh fading, and in terms of processing overhead on a control system with wireless communication. The results obtained through computational simulations and experimental tests show performance gains obtained by insertion of the adaptive wavelet coding in a control loop with nodes interconnected by wireless link. These results enable the use of this technique in a wireless link control loop. PMID:29236048
Felipe O. S. Gama
Full Text Available Wireless control systems can sense, control and act on the information exchanged between the wireless sensor nodes in a control loop. However, the exchanged information becomes susceptible to the degenerative effects produced by the multipath propagation. In order to minimize the destructive effects characteristic of wireless channels, several techniques have been investigated recently. Among them, wavelet coding is a good alternative for wireless communications for its robustness to the effects of multipath and its low computational complexity. This work proposes an adaptive wavelet coding whose parameters of code rate and signal constellation can vary according to the fading level and evaluates the use of this transmission system in a control loop implemented by wireless sensor nodes. The performance of the adaptive system was evaluated in terms of bit error rate (BER versus E b / N 0 and spectral efficiency, considering a time-varying channel with flat Rayleigh fading, and in terms of processing overhead on a control system with wireless communication. The results obtained through computational simulations and experimental tests show performance gains obtained by insertion of the adaptive wavelet coding in a control loop with nodes interconnected by wireless link. These results enable the use of this technique in a wireless link control loop.
tem based on the stability theory of fractional-order dynamic systems. The presented schemes, which contain only a single-state variable, are simple and flexible. Numerical simulations are used to demonstrate the feasibility of the presented methods. Keywords. Fractional order; adaptive scheme; control; synchronization.
Full Text Available The accuracy of physical parameters of a tunable MEMS capacitor, as the major part of MEMS AC voltage reference, is of great importance to achieve an accurate output voltage free of the malfunctioning noise and disturbance. Even though strenuous endeavors are made to fabricate MEMS tunable capacitors with desiderated accurate physical characteristics and ameliorate exactness of physical parameters’ values, parametric uncertainties ineluctably emerge in fabrication process attributable to imperfections in micromachining process. First off, this paper considers applying an adaptive sliding mode controller design in the MEMS AC voltage reference source so that it is capable of giving off a well-regulated output voltage in defiance of jumbling parametric uncertainties in the plant dynamics and also aggravating external disturbance imposed on the system. Secondly, it puts an investigatory comparison with the designed model reference adaptive controller and the pole-placement state feedback one into one’s prospective. Not only does the tuned adaptive sliding mode controller show remarkable robustness against slow parameter variation and external disturbance being compared to the pole-placement state feedback one, but also it immensely gets robust against the external disturbance in comparison with the conventional adaptive controller. The simulation results are promising.
The paper developed artificial intelligence technique adaptive neuro-fuzzy controller for air conditioning systems at different pressures. The first order Sugeno fuzzy .... condenser heat rejection rate, refrigerant mass flow rate, compressor power, electric power input to the compressor motor and the coefficient of performance.
This paper describes an adaptive feed forward system that corrects repetitive errors in the amplitude and phase of the RF field of a pulsed accelerator. High-frequency disturbances that are beyond the effective bandwidth of the RF-field feedback control system can be eliminated with a feed forward system. Many RF-field disturbances for a pulsed accelerator are repetitive, occurring at the same relative time in every pulse. This design employs digital signal processing hardware to adaptively determine and track the control signals required to eliminate the repetitive errors in the feedback control system. In order to provide the necessary high-frequency response, the adaptive feed forward hardware provides the calculated control signal prior to the repetitive disturbance that it corrects. This system has been demonstrated to reduce the transient disturbances caused by beam pulses. Furthermore, it has been shown to negate high-frequency phase and amplitude oscillations in a high-power klystron amplifier caused by PFN ripple on the high-voltage. The design and results of the adaptive feed forward system are presented. (Author) 3 figs., 2 refs
Schakel, W.J.; Gorter, C.M.; de Winter, J.C.F.; van Arem, B.
With the increasing number of vehicles equipped with Adaptive Cruise Control (ACC), it becomes important to assess its impact on traffic flow efficiency, in particular with respect to capacity and queue discharge rate. Simulation studies and surveys suggest that ACC has both positive and negative
journal of. April 2013 physics pp. 583–592. Adaptive control and synchronization of a fractional-order chaotic system. CHUNLAI LI1,∗ and YAONAN TONG2. 1College of Physics and Electronics; 2School of Information and Communication Engineering,. Hunan Institute of Science and Technology, Yueyang 414006, China.
Full Text Available Adaptive Cruise Control (ACC) is a relatively new system designed to assist automobile drivers in maintaining a safe following distance. This paper proposes and validates a vision-based ACC system which uses a single camera to obtain the clearance...
Full Text Available Six rotor eppo drones's load change itself in the job process will reduce the aircraft flight control performance and make the resistance to environmental disturbance being poor. In order to improve the six rotor eppo unmanned aerial vehicle (UAV control performance, the UAV in the process of spraying pesticide is analyzed and the model is constructed, then the eppo UAV time-varying dynamics mathematical model is deduced, and a fuzzy adaptive PID control algorithm is proposed. Fuzzy adaptive PID algorithm has good adaptability and the parameter setting is simple, which improves the system dynamic response and steady state performance, realizing the stability of the six rotor eppo UAV flight. With measured parameters of each sensor input in to the fuzzy adaptive PID algorithm, the corresponding control quality is obtained, and the stable operation of aircraft is realized. Through using Matlab to simulate the flight system and combining the practical experiments, it shows that the dynamic performance and stability of the system is improved effetively.
Willigen, W.H. van; Schut, M.C.; Kester, L.J.H.M.
This paper is concerned with safety in (cooperative) adaptive cruise control systems. In these systems, the speed of the cars is maintained automatically, based on the preferred speed of the driver and the speed of the preceding car. Technologies that are used in these systems, such as radar and
May 5, 1995 ... Continuous use of an adaptive lung ventilation controller in critically ill patients in a multi- disciplinary intensive care unit. David M. Linton, Josef x. .... Integration of the flow signal yielded the inspired volume (\\/J and the expired volume (\\/J. Tidal volume 0JT) was calculated as the arithmetical mean of both.
Chen, Bin-Bin; Chang, Lei
By integrating the life history theory of attachment with resource control theory, the current study examines the hypothesis that insecure attachment styles reorganized in middle childhood are alternative adaptive strategies used to prepare for upcoming competition with the peer group. A sample of 654 children in the second through seventh grades…
van der Hulst, M.; Meijman, T.F.; Rothengatter, J.A.
Driving is a task that requires the timely detection of critical events and relevant changes in traffic circumstances. Adaptation of speed and safety margins allows drivers to control the time available to react to potential hazards. One of the basic safety margins in driving is the time headway
Willigenburg, van L.G.; Vollebregt, H.M.; Sman, van der R.G.M.
An adaptive optimal scheduling and controller design is presented that attempts to improve the performance of beer membrane filtration over the ones currently obtained by operators. The research was performed as part of a large European research project called EU Cafe with the aim to investigate the
Mar 7, 2014 ... networks with derivative coupling and time-delay coupling was investigated by adaptive control schemes . However ... , the synchronization of complex dynamical networks with non-derivative coupling and derivative coupling .... For any symmetric positive definite matrix. M ∈ Rn×n and x,y ∈ Rn, ...
Thor I. Fossen
Full Text Available This paper presents a cascade adaptive control scheme for marine vehicles where the non-linear equations of motion include a model of the actuator dynamics. The adaptive controller does not require the parameters of the vehicle dynamics and the actuator time constants to be known a priori. Both the velocity and position tracking errors are shown to converge to zero by applying Barbalat's lemma. Global asymptotic stability is proven for the velocity scheme while the position/attitude controller is only proven to be convergent. Furthermore, all parameter estimates are shown to be bounded. Computer simulations of an ROV speed control system and an autopilot for automatic ship steering are used to illustrate the design methodology.
Mohamed I. Mosaad
Full Text Available This paper presents an adaptive PID Load Frequency Control (LFC for power systems using Neuro-Fuzzy Inference Systems (ANFIS and Artificial Neural Networks (ANN oriented by Genetic Algorithm (GA. PID controller parameters are tuned off-line by using GA to minimize integral error square over a wide-range of load variations. The values of PID controller parameters obtained from GA are used to train both ANFIS and ANN. Therefore, the two proposed techniques could, online, tune the PID controller parameters for optimal response at any other load point within the operating range. Testing of the developed techniques shows that the adaptive PID-LFC could preserve optimal performance over the whole loading range. Results signify superiority of ANFIS over ANN in terms of performance measures.
Kutay, Ali Turker
Application of recently developed neural network based adaptive output feedback controllers to a diverse range of problems both in simulations and experiments is investigated in this thesis. The purpose is to evaluate the theory behind the development of these controllers numerically and experimentally, identify the needs for further development in practical applications, and to conduct further research in directions that are identified to ultimately enhance applicability of adaptive controllers to real world problems. We mainly focus our attention on adaptive controllers that augment existing fixed gain controllers. A recently developed approach holds great potential for successful implementations on real world applications due to its applicability to systems with minimal information concerning the plant model and the existing controller. In this thesis the formulation is extended to the multi-input multi-output case for distributed control of interconnected systems and successfully tested on a formation flight wind tunnel experiment. The command hedging method is formulated for the approach to further broaden the class of systems it can address by including systems with input nonlinearities. Also a formulation is adopted that allows the approach to be applied to non-minimum phase systems for which non-minimum phase characteristics are modeled with sufficient accuracy and treated properly in the design of the existing controller. It is shown that the approach can also be applied to augment nonlinear controllers under certain conditions and an example is presented where the nonlinear guidance law of a spinning projectile is augmented. Simulation results on a high fidelity 6 degrees-of-freedom nonlinear simulation code are presented. The thesis also presents a preliminary adaptive controller design for closed loop flight control with active flow actuators. Behavior of such actuators in dynamic flight conditions is not known. To test the adaptive controller design in
Full Text Available In this paper a multivariable robust adaptive controller is presented for a plant with bounded disturbances and unmodeled dynamics due to plant-model order mismatches. The robust stability of the closed-loop system is achieved by using the normalization technique and the least squares parameter estimation scheme with dead zones. The weighting polynomial matrices are incorporated into the control law, so that the open-loop unstable or/and nonminimum phase plants can be handled.
Milovanović, Miroslav B; Antić, Dragan S; Milojković, Marko T; Nikolić, Saša S; Perić, Staniša Lj; Spasić, Miodrag D
A new intelligent hybrid structure used for online tuning of a PID controller is proposed in this paper. The structure is based on two adaptive neural networks, both with built-in Chebyshev orthogonal polynomials. First substructure network is a regular orthogonal neural network with implemented artificial endocrine factor (OENN), in the form of environmental stimuli, to its weights. It is used for approximation of control signals and for processing system deviation/disturbance signals which are introduced in the form of environmental stimuli. The output values of OENN are used to calculate artificial environmental stimuli (AES), which represent required adaptation measure of a second network-orthogonal endocrine adaptive neuro-fuzzy inference system (OEANFIS). OEANFIS is used to process control, output and error signals of a system and to generate adjustable values of proportional, derivative, and integral parameters, used for online tuning of a PID controller. The developed structure is experimentally tested on a laboratory model of the 3D crane system in terms of analysing tracking performances and deviation signals (error signals) of a payload. OENN-OEANFIS performances are compared with traditional PID and 6 intelligent PID type controllers. Tracking performance comparisons (in transient and steady-state period) showed that the proposed adaptive controller possesses performances within the range of other tested controllers. The main contribution of OENN-OEANFIS structure is significant minimization of deviation signals (17%-79%) compared to other controllers. It is recommended to exploit it when dealing with a highly nonlinear system which operates in the presence of undesirable disturbances. Copyright © 2016 Elsevier Ltd. All rights reserved.
Zheng Fang; Weinan Gao; Lei Zhang
Unmanned aerial vehicles have enormous potential applications in military and civil fields. A Quanser’s 3‐DOF helicopter is a simplified and benchmark experimental model for validating the effectiveness of various flight control algorithms. The attitude control of the 3‐DOF helicopter is a challenging task since the helicopter is an under‐actuated system with strong coupling and model uncertainty characteristics. In this paper, an adaptive integral backstepping algorithm is proposed to realiz...
Full Text Available This paper deals with the control problem of the chaotic system subject to disturbance. The sliding mode surface is designed by singular system approach, and sufficient condition for convergence is given. Then, the adaptive sliding mode controller is designed to make the state arrive at the sliding mode surface in finite time. Finally, Lorenz system is considered as an example to show the effectiveness of the proposed method.
Full Text Available An adaptive control technology and PI-fuzzy compound control technology are proposed to control an active power filter (APF. AC side current compensation and DC capacitor voltage tracking control strategy are discussed and analyzed. Model reference adaptive controller for the AC side current compensation is derived and established based on Lyapunov stability theory; proportional and integral (PI fuzzy compound controller is designed for the DC side capacitor voltage control. The adaptive current controller based on PI-fuzzy compound system is compared with the conventional PI controller for active power filter. Simulation results demonstrate the feasibility and satisfactory performance of the proposed control strategies. It is shown that the proposed control method has an excellent dynamic performance such as small current tracking error, reduced total harmonic distortion (THD, and strong robustness in the presence of parameters variation and nonlinear load.
Sun Mei; Tian Lixin; Jiang Shumin; Xu Jun
In this paper, the problem of control for the energy resource chaotic system is considered. Two different method of control, feedback control (include linear feedback control, non-autonomous feedback control) and adaptive control methods are used to suppress chaos to unstable equilibrium or unstable periodic orbits. The Routh-Hurwitz criteria and Lyapunov direct method are used to study the conditions of the asymptotic stability of the steady states of the controlled system. The designed adaptive controller is robust with respect to certain class of disturbances in the energy resource chaotic system. Numerical simulations are presented to show these results
Nasirian, Vahidreza; Davoudi, Ali; Lewis, Frank
Summary form only given: A distributed-adaptive droop mechanism is proposed for secondary/primary control of dc microgrids. The conventional secondary control that adjusts the voltage set point for the local droop mechanism is replaced by a voltage regulator. A current regulator is also added...... sharing. The proposed controller precisely accounts for the transmission/distribution line impedances. The controller on each converter exchanges data with only its neighbor converters on a sparse communication graph spanned across the microgrid. Global dynamic model of the microgrid is derived...
Esogbue, Augustine O.
The principal objective of the research reported here is the re-design, analysis and optimization of our newly developed neural network fuzzy adaptive controller model for complex processes capable of learning fuzzy control rules using process data and improving its control through on-line adaption. The learned improvement is according to a performance objective function that provides evaluative feedback; this performance objective is broadly defined to meet long-range goals over time. Although fuzzy control had proven effective for complex, nonlinear, imprecisely-defined processes for which standard models and controls are either inefficient, impractical or cannot be derived, the state of the art prior to our work showed that procedures for deriving fuzzy control, however, were mostly ad hoc heuristics. The learning ability of neural networks was exploited to systematically derive fuzzy control and permit on-line adaption and in the process optimize control. The operation of neural networks integrates very naturally with fuzzy logic. The neural networks which were designed and tested using simulation software and simulated data, followed by realistic industrial data were reconfigured for application on several platforms as well as for the employment of improved algorithms. The statistical procedures of the learning process were investigated and evaluated with standard statistical procedures (such as ANOVA, graphical analysis of residuals, etc.). The computational advantage of dynamic programming-like methods of optimal control was used to permit on-line fuzzy adaptive control. Tests for the consistency, completeness and interaction of the control rules were applied. Comparisons to other methods and controllers were made so as to identify the major advantages of the resulting controller model. Several specific modifications and extensions were made to the original controller. Additional modifications and explorations have been proposed for further study. Some of
Full Text Available This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP technique, prescribed performance control, and a kind of smooth switching strategy into back-stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller.
Full Text Available This paper addresses a robust trajectory tracking controller for an underactuated quadrotor with external bounded disturbances and unknown inertia parameters. Different from most of the existing control algorithms, the proposed method does not adopt the dual-loop scheme in which the design is divided into position control and attitude control. Instead, command filter backstepping is employed to design the controller based on the integrated motion model such that the stability can be guaranteed strictly for the flight control system. Furthermore, adaptive compensation and robust compensation are introduced to deal with the uncertainty of the inertia parameters and the external bounded disturbances, respectively. Finally, a similar skew symmetric structure is chosen as the desired structure of the closed-loop system to facilitate the analysis of the stability of the integrated system. Stability and robust performance of the designed controller are verified by Lyapunov stability theorem. Simulations are provided to validate the proposed controller.
Alphinas, Robert A.; Hansen, Hans Henrik; Tambo, Torben
Non-adaptive proportional controllers suffer from the ability to handle a system disturbance leading to a large steady-state error and undesired transient behavior. On the other hand, they are easy to implement and tune. This article examines whether an adaptive controller based on the MIT...... and Lyapunov principle leads to a more robust and accurate regulation. Both controllers have been tested on a thermodynamic system exposed to a disturbance. The experiment shows that the adaptive controller handles the disturbance faster and more accurate....
Hao, Lina; Sun, Zhiyong; Su, Yunquan; Gao, Jianchao; Li, Zhi
IPMC is a type of electro-active polymer material, also called artificial muscle, which can generate a relatively large deformation under a relatively low input voltage (generally speaking, less than 5 V), and can be implemented in a water environment. Due to these advantages, IPMC can be used in many fields such as biomimetics, service robots, bio-manipulation, etc. Until now, most existing methods for IPMC manipulation are displacement control not directly force control, however, under most conditions, the success rate of manipulations for tiny fragile objects is limited by the contact force, such as using an IPMC gripper to fix cells. Like most EAPs, a creep phenomenon exists in IPMC, of which the generated force will change with time and the creep model will be influenced by the change of the water content or other environmental factors, so a proper force control method is urgently needed. This paper presents a novel adaptive force control method (AIPOF control—adaptive integral periodic output feedback control), based on employing a creep model of which parameters are obtained by using the FRLS on-line identification method. The AIPOF control method can achieve an arbitrary pole configuration as long as the plant is controllable and observable. This paper also designs the POF and IPOF controller to compare their test results. Simulation and experiments of micro-force-tracking tests are carried out, with results confirming that the proposed control method is viable. (paper)
Gwaltney, David A.; King, Kenneth D.; Smith, Keary J.; Monenegro, Justino (Technical Monitor)
Much has been made of the capabilities of FPGA's (Field Programmable Gate Arrays) in the hardware implementation of fast digital signal processing. Such capability also makes an FPGA a suitable platform for the digital implementation of closed loop controllers. Other researchers have implemented a variety of closed-loop digital controllers on FPGA's. Some of these controllers include the widely used proportional-integral-derivative (PID) controller, state space controllers, neural network and fuzzy logic based controllers. There are myriad advantages to utilizing an FPGA for discrete-time control functions which include the capability for reconfiguration when SRAM-based FPGA's are employed, fast parallel implementation of multiple control loops and implementations that can meet space level radiation tolerance requirements in a compact form-factor. Generally, a software implementation on a DSP (Digital Signal Processor) or microcontroller is used to implement digital controllers. At Marshall Space Flight Center, the Control Electronics Group has been studying adaptive discrete-time control of motor driven actuator systems using digital signal processor (DSP) devices. While small form factor, commercial DSP devices are now available with event capture, data conversion, pulse width modulated (PWM) outputs and communication peripherals, these devices are not currently available in designs and packages which meet space level radiation requirements. In general, very few DSP devices are produced that are designed to meet any level of radiation tolerance or hardness. The goal of this effort is to create a fully digital, flight ready controller design that utilizes an FPGA for implementation of signal conditioning for control feedback signals, generation of commands to the controlled system, and hardware insertion of adaptive control algorithm approaches. An alternative is required for compact implementation of such functionality to withstand the harsh environment
Yuan, Bau-San; Book, Wayne J.
Due to geometric nonlinearities and complex dynamics, a decentralized technique for adaptive control for multilink robot arms is attractive. Lyapunov-function theory for stability analysis provides an approach to robust stabilization. Each joint of the arm is treated as a component subsystem. The adaptive controller is made locally stable with servo signals including proportional and integral gains. This results in the bound on the dynamical interactions with other subsystems. A nonlinear controller which stabilizes the system with uniform boundedness is used to improve the robustness properties of the overall system. As a result, the robot tracks the reference trajectories with convergence. This strategy makes computation simple and therefore facilitates real-time implementation.
Singh, S. N.
An approach to control of a class of nonlinear flexible robotic systems is presented. For simplicity, a robot arm (PUMA-type) with three rotational joints is considered. The third link is assumed to be elastic. An adaptive torquer control law is derived for controlling the joint angles. This controller includes a dynamic system in the feedback path, requires only joint angle and rate for feedback, and asymptotically decomposes the elastic dynamics into two subsystems representing the transverse vibrations of the elastic link in two orthogonal planes. To damp out the elastic vibration, a force control law using modal feedback is synthesized. The combination of the torque and force control laws accomplishes joint angle control and elastic mode stabilization.
Full Text Available This paper proposes an adaptive and robust sliding mode control (SMC for the position control of Interior Permanent Magnet Synchronous Motor (IPMSM drives. A switching surface of SMC is designed using a Linear Quadratic Regulator (LQR technique to simultaneously control the tracking trajectory and load torque changes. The quadratic optimal control method is used to select the state feedback control gain that constitutes the system dynamic performance under uncertainties and disturbances. Feedback and switching gains are selected to satisfy both stability and fast convergence of the IPMSM. Matlab/Simulink is used to build the drive system. Experimental implementation of the IPMSM drive is carried out using DSP-DS1102 control board. The efficacy of the proposed position control method is validated using theoretical analysis and simulation and experimental results.
Tsai, Feng-Sheng; Hsu, Sheng-Yi; Shih, Mau-Hsiang
Adaptive tracking control of mobile robots requires the ability to follow a trajectory generated by a moving target. The conventional analysis of adaptive tracking uses energy minimization to study the convergence and robustness of the tracking error when the mobile robot follows a desired trajectory. However, in the case that the moving target generates trajectories with uncertainties, a common Lyapunov-like function for energy minimization may be extremely difficult to determine. Here, to solve the adaptive tracking problem with uncertainties, we wish to implement an interneural computing scheme in the design of a mobile robot for behavior-based navigation. The behavior-based navigation adopts an adaptive plan of behavior patterns learning from the uncertainties of the environment. The characteristic feature of the interneural computing scheme is the use of neural path pruning with rewards and punishment interacting with the environment. On this basis, the mobile robot can be exploited to change its coupling weights in paths of neural connections systematically, which can then inhibit or enhance the effect of flow elimination in the dynamics of the evolutionary neural network. Such dynamical flow translation ultimately leads to robust sensory-to-motor transformations adapting to the uncertainties of the environment. A simulation result shows that the mobile robot with the interneural computing scheme can perform fault-tolerant behavior of tracking by maintaining suitable behavior patterns at high frequency levels.
Eshak, Peter B.
Research efforts have increased in recent years toward the development of intelligent fault tolerant control laws, which are capable of helping the pilot to safely maintain aircraft control at post failure conditions. Researchers at West Virginia University (WVU) have been actively involved in the development of fault tolerant adaptive control laws in all three major categories: direct, indirect, and hybrid. The first implemented design to provide adaptation was a direct adaptive controller, which used artificial neural networks to generate augmentation commands in order to reduce the modeling error. Indirect adaptive laws were implemented in another controller, which utilized online PID to estimate and update the controller parameter. Finally, a new controller design was introduced, which integrated both direct and indirect control laws. This controller is known as hybrid adaptive controller. This last control design outperformed the two earlier designs in terms of less NNs effort and better tracking quality. The performance of online PID has an important role in the quality of the hybrid controller; therefore, the quality of the estimation will be of a great importance. Unfortunately, PID is not perfect and the online estimation process has some inherited issues; the online PID estimates are primarily affected by delays and biases. In order to ensure updating reliable estimates to the controller, the estimator consumes some time to converge. Moreover, the estimator will often converge to a biased value. This thesis conducts a sensitivity analysis for the estimation issues, delay and bias, and their effect on the tracking quality. In addition, the performance of the hybrid controller as compared to direct adaptive controller is explored. In order to serve this purpose, a simulation environment in MATLAB/SIMULINK has been created. The simulation environment is customized to provide the user with the flexibility to add different combinations of biases and delays to
Full Text Available An adaptive gain sliding observer for uncertain parameter nonlinear systems together with an adaptive gain sliding controller is proposed in this paper. It considered nonlinear, SISO affine systems, with uncertainties in steady-state functions and parameters. A further parameter term, adaptively updated, has been introduced in steady state space model of the controlled system, in order to obtain useful information despite fault detection and isolation. By using of the sliding observer with adaptive gain, the robustness to uncertainties is increased and the parameters adaptively updated can provide useful information in fault detection. Also, the state estimation error is bounded accordingly with bound limits of the uncertainties. The both of them, the sliding adaptive observer and sliding controller are designed to fulfill the attractiveness condition of its corresponding switching surface. An application to a single arm with flexible joint robot is presented. In order to alleviate chattering, a parameterized tangent hyperbolic has been used as switching function, instead of pure relay one, to the observer and the controller. Also, the gains of the switching functions, to the sliding observer and sliding controller are adaptively updated depending of estimation error and tracking error, respectively. By the using adaptive gains, the transient and tracking response can be improved.
El-Nagar, Ahmad M
In this study, an intelligent adaptive controller approach using the interval type-2 fuzzy neural network (IT2FNN) is presented. The proposed controller consists of a lower level proportional - integral (PI) controller, which is the main controller and an upper level IT2FNN which tuning on-line the parameters of a PI controller. The proposed adaptive PI controller based on IT2FNN (API-IT2FNN) is implemented practically using the Arduino DUE kit for controlling the speed of a nonlinear DC motor-generator system. The parameters of the IT2FNN are tuned on-line using back-propagation algorithm. The Lyapunov theorem is used to derive the stability and convergence of the IT2FNN. The obtained experimental results, which are compared with other controllers, demonstrate that the proposed API-IT2FNN is able to improve the system response over a wide range of system uncertainties. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Incremona, Gian Paolo; Cucuzzella, Michele; Ferrara, Antonella
This paper deals with the design of adaptive suboptimal second-order sliding mode (ASSOSM) control laws for grid-connected microgrids. Due to the presence of the inverter, of unpredicted load changes, of switching among different renewable energy sources, and of electrical parameters variations, the microgrid model is usually affected by uncertain terms which are bounded, but with unknown upper bounds. To theoretically frame the control problem, the class of second-order systems in Brunovsky canonical form, characterised by the presence of matched uncertain terms with unknown bounds, is first considered. Four adaptive strategies are designed, analysed and compared to select the most effective ones to be applied to the microgrid case study. In the first two strategies, the control amplitude is continuously adjusted, so as to arrive at dominating the effect of the uncertainty on the controlled system. When a suitable control amplitude is attained, the origin of the state space of the auxiliary system becomes attractive. In the other two strategies, a suitable blend between two components, one mainly working during the reaching phase, the other being the predominant one in a vicinity of the sliding manifold, is generated, so as to reduce the control amplitude in steady state. The microgrid system in a grid-connected operation mode, controlled via the selected ASSOSM control strategies, exhibits appreciable stability properties, as proved theoretically and shown in simulation.
Full Text Available There is a trend towards using wireless technologies in networked control systems. However, the adverse properties of the radio channels make it difficult to design and implement control systems in wireless environments. To attack the uncertainty in available communication resources in wireless control systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS scheme is developed, which takes advantage of the co-design of control and wireless communications. By exploiting crosslayer design, CLAFS adjusts the sampling periods of control systems at the application layer based on information about deadline miss ratio and transmission rate from the physical layer. Within the framework of feedback scheduling, the control performance is maximized through controlling the deadline miss ratio. Key design parameters of the feedback scheduler are adapted to dynamic changes in the channel condition. An eventdriven invocation mechanism for the feedback scheduler is also developed. Simulation results show that the proposed approach is efficient in dealing with channel capacity variations and noise interference, thus providing an enabling technology for control over WLAN.
Wang, Zhen; Wu, Zhong; Du, Yijiang
During the reentry process of reusable launch vehicles (RLVs), the large range of flight envelope will not only result in high nonlinearities, strong coupling and fast time-varying characteristics of the attitude dynamics, but also result in great uncertainties in the atmospheric density, aerodynamic coefficients and environmental disturbances, etc. In order to attenuate the effects of these problems on the control performance of the reentry process, a robust adaptive backstepping control (RABC) strategy is proposed for RLV in this paper. This strategy consists of two-loop controllers designed via backstepping method. Both the outer and the inner loop adopt a robust adaptive controller, which can deal with the disturbances and uncertainties by the variable-structure term with the estimation of their bounds. The outer loop can track the desired attitude by the design of virtual control-the desired angular velocity, while the inner one can track the desired angular velocity by the design of control torque. Theoretical analysis indicates that the closed-loop system under the proposed control strategy is globally asymptotically stable. Even if the boundaries of the disturbances and uncertainties are unknown, the attitude can track the desired value accurately. Simulation results of a certain RLV demonstrate the effectiveness of the control strategy.
Zain Anwar Ali; Daobo Wang; Suhaib Masroor; M. Shafiq Loya
The paper presents an adaptive hybrid scheme which is based on fuzzy regulation, pole-placement, and tracking (RST) control algorithm for controlling the attitude and altitude of trirotor UAV. The dynamic and kinematic model of Unmanned Aerial Vehicle (UAV) is unstable and nonlinear in nature with 6 degrees of freedom (DOF); that is why the stabilization of aerial vehicle is a difficult task. To stabilize the nonlinear behavior of our UAV, an adaptive hybrid controller algorithm is used, in w...
Trintis, Ionut; Sun, Bo; Guerrero, Josep M.
This paper investigates a controller that ensures minimum operating dc-link voltage of a back-to-back converter system. The dc-link voltage adapts its reference based on the system state, reference given by an outer loop to the dc-link voltage controller. The operating dc-link voltage should...... be kept as low as possible to increase the power conversion efficiency and increase the reliability of converters. The dynamic performance of the proposed controller is investigated by simulations and experiments....
Bendtsen, Jan Dimon; Trangbæk, Klaus
We propose a multi-model adaptive control scheme for uncertain linear plants based on the concept of model unfalsification. The approach relies on examining the ability of a pre-computed set of plant-controller candidates and choosing the one that is best able to reproduce observed in- and output...... signal samples. The ability to reproduce observations is measured as an easily computable signal norm. Compared to other related approaches, our procedure is designed to be able to handle significant measurement noise and closed-loop correlations between output measurements and control signals....
Hua Changchun E-mail: firstname.lastname@example.org; Guan Xinping E-mail: email@example.com; Shi Peng
In this paper, the problem of control for a class of chaotic systems is considered. The nonlinear functions of chaotic systems are not necessarily to satisfy the Lipsichtz conditions, but bounded by a polynomial with the gains unknown. Employing adaptive method, the corresponding controller which renders the closed-loop system asymptotically stable is constructed. The designed controller is robust with respect to certain class of disturbances in the chaotic systems. Simulations on unified chaotic systems and Arneodo chaotic system are performed and the results verify the validity of the proposed techniques.
This paper proposes a novel method for superheat and capacity control of refrigeration systems. The new idea is to control the superheat by the compressor speed and capacity by the refrigerant flow. This gives a highly nonlinear transfer operator from compressor speed input to the superheat output....... A new low order nonlinear model of the evaporator is developed and used in a backstepping design of an adaptive nonlinear controller. The stability of the proposed method is validated theoretically by Lyapunov analysis and experimental results shows the performance of the system for a wide range...
Andersen, Torben Ole; Pedersen, Henrik Clemmensen; Bech, Michael Møller
, active gain feedforward shows a slightly improved performance. Computed-Torque Control shows better performance, but requires a well described system for best performance. A novel Adaptive Inverse Dynamics Controller was tested and the performance was found to be similar to that of Computed...... system were constructed and linearized. Controllers are implemented and tested on the manipulator. Pressure feedback was found to greatly improve system stability margins. Passive gain feedforward shows improved tracking performance for small changes in load pressure. For large changes in load pressure...
Prasanth, Ravi K.; Boskovic, Jovan; Mehra, Raman K.
Intelligent and adaptive control systems will significantly challenge current verification and validation (V&V) processes, tools, and methods for flight certification. Although traditional certification practices have produced safe and reliable flight systems, they will not be cost effective for next-generation autonomous unmanned air vehicles (UAVs) due to inherent size and complexity increases from added functionality. Affordable V&V of intelligent control systems is by far the most important challenge in the development of UAVs faced by both commercial and military aerospace industry in the United States. This paper presents a formal modeling framework for a class of adaptive control systems and an associated computational scheme. The class of systems considered include neural network-based flight control systems and vehicle health management systems. This class of systems and indeed all adaptive systems are hybrid systems whose continuum dynamics is nonlinear. Our computational procedure is iterative and each iteration has two sequential steps. The first step is to derive an approximating finite-state automaton whose behaviors contain the behaviors of the hybrid system. The second step is to check if the language accepted by the approximating automaton is empty (emptiness checking). The iterations are terminated if the language accepted is empty; otherwise, the approximation is refined and the iteration is continued. This procedure will never produce an "error-free" certificate when the actual system contains errors which is an important requirement in V&V of safety critical systems.
Thau, F. E.
The objective of the research has been to study the design of adaptive/learning control systems for the control of large flexible structures. In the first activity an adaptive/learning control methodology for flexible space structures was investigated. The approach was based on using a modal model of the flexible structure dynamics and an output-error identification scheme to identify modal parameters. In the second activity, a least-squares identification scheme was proposed for estimating both modal parameters and modal-to-actuator and modal-to-sensor shape functions. The technique was applied to experimental data obtained from the NASA Langley beam experiment. In the third activity, a separable nonlinear least-squares approach was developed for estimating the number of excited modes, shape functions, modal parameters, and modal amplitude and velocity time functions for a flexible structure. In the final research activity, a dual-adaptive control strategy was developed for regulating the modal dynamics and identifying modal parameters of a flexible structure. A min-max approach was used for finding an input to provide modal parameter identification while not exceeding reasonable bounds on modal displacement.
Volyanskyy, Kostyantyn Y.
Neural networks have been extensively used for adaptive system identification as well as adaptive and neuroadaptive control of highly uncertain systems. The goal of adaptive and neuroadaptive control is to achieve system performance without excessive reliance on system models. To improve robustness and the speed of adaptation of adaptive and neuroadaptive controllers several controller architectures have been proposed in the literature. In this dissertation, we develop a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture with additional terms in the update laws that are constructed using a moving window of the integrated system uncertainty. These terms can be used to identify the ideal system weights of the neural network as well as effectively suppress system uncertainty. Linear and nonlinear parameterizations of the system uncertainty are considered and state and output feedback neuroadaptive controllers are developed. Furthermore, we extend the developed framework to discrete-time dynamical systems. To illustrate the efficacy of the proposed approach we apply our results to an aircraft model with wing rock dynamics, a spacecraft model with unknown moment of inertia, and an unmanned combat aerial vehicle undergoing actuator failures, and compare our results with standard neuroadaptive control methods. Nonnegative systems are essential in capturing the behavior of a wide range of dynamical systems involving dynamic states whose values are nonnegative. A sub-class of nonnegative dynamical systems are compartmental systems. These systems are derived from mass and energy balance considerations and are comprised of homogeneous interconnected microscopic subsystems or compartments which exchange variable quantities of material via intercompartmental flow laws. In this dissertation, we develop direct adaptive and neuroadaptive control framework for stabilization, disturbance
Buescher, Kevin L.; Baum, Christopher C.; Jones, Roger D.
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data.
Buescher, K.L.; Baum, C.C.; Jones, R.D.
A control system for controlling the output of at least one plant process output parameter is implemented by adaptive model predictive control using a neural network. An improved method and apparatus provides for sampling plant output and control input at a first sampling rate to provide control inputs at the fast rate. The MPC system is, however, provided with a network state vector that is constructed at a second, slower rate so that the input control values used by the MPC system are averaged over a gapped time period. Another improvement is a provision for on-line training that may include difference training, curvature training, and basis center adjustment to maintain the weights and basis centers of the neural in an updated state that can follow changes in the plant operation apart from initial off-line training data. 46 figs.
Engel, E.; Kovalev, I. V.; Karandeev, D.
The ongoing evolution of the power system towards a Smart Grid implies an important role of intelligent technologies, but poses strict requirements on their control schemes to preserve stability and controllability. This paper presents the adaptive neuro-controller for the vector control of induction motor within Smart Gird. The validity and effectiveness of the proposed energy-saving technology of vector controlled induction motor based on adaptive neuro-controller are verified by simulation results at different operating conditions over a wide speed range of induction motor.
Johnson, Kathryn E [Boulder, CO; Fingersh, Lee Jay [Westminster, CO
An adaptive method for adjusting blade pitch angle, and controllers implementing such a method, for achieving higher power coefficients. Average power coefficients are determined for first and second periods of operation for the wind turbine. When the average power coefficient for the second time period is larger than for the first, a pitch increment, which may be generated based on the power coefficients, is added (or the sign is retained) to the nominal pitch angle value for the wind turbine. When the average power coefficient for the second time period is less than for the first, the pitch increment is subtracted (or the sign is changed). A control signal is generated based on the adapted pitch angle value and sent to blade pitch actuators that act to change the pitch angle of the wind turbine to the new or modified pitch angle setting, and this process is iteratively performed.
Wu, Zhonghua; Lu, Jingchao; Shi, Jingping; Liu, Yang; Zhou, Qing
This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs) be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP) tech...
Kallesøe, Carsten; Jensen, Tom Nørgaard; Wisniewski, Rafal
consumers are considered. Under mild assumptions on the consumption pattern and hydraulic resistances of pipes we use properties of the network graph and Kirchhoffs node and mesh laws to show that simple relations exist between the actuator pressure and critical point pressures inside the network....... Subsequently, these relations are exploited in an adaptive reference control scheme for the actuator pressure that ensures constant pressure at the critical points. Numerical experiments underpin the results. © Copyright IEEE - All rights reserved....
extinction . VI. CONCLUSIONS We have presented a method for predicting extinction in stochastic network systems by analyzing a pair-based proxy model...including games on networks (e.g., , ). Further, we expect that our method of continuously varying a parameter while tracking the path to extinction ...Adaptive Dynamics, Control, and Extinction in Networked Populations Ira B. Schwartz US Naval Research Laboratory Code 6792 Nonlinear System Dynamics
Full Text Available A direct model reference adaptive controller (MRAC is derived for an underwater vehicle with significant thruster dynamics and limited thruster power. The reference model decomposition (RMD technique is used to compensate for the thruster dynamics. A reference model adjustment (RMA technique modifying the reference model acceleration is used to avoid thruster saturation. The design methods are simulated for the yawing motion of an underwater vehicle.
Ma, Liang; Wang, Bin; Zhou, Yuanshen; Yang, Huizhen
Compared with the conventional adaptive optics (AO) system, the wavefront sensorless (WFSless) AO system need not to measure the wavefront and reconstruct it. It is simpler than the conventional AO in system architecture and can be applied to the complex conditions. Based on the analysis of principle and system model of the WFSless AO system, wavefront correction methods of the WFSless AO system were divided into two categories: model-free-based and model-based control algorithms. The WFSless AO system based on model-free-based control algorithms commonly considers the performance metric as a function of the control parameters and then uses certain control algorithm to improve the performance metric. The model-based control algorithms include modal control algorithms, nonlinear control algorithms and control algorithms based on geometrical optics. Based on the brief description of above typical control algorithms, hybrid methods combining the model-free-based control algorithm with the model-based control algorithm were generalized. Additionally, characteristics of various control algorithms were compared and analyzed. We also discussed the extensive applications of WFSless AO system in free space optical communication (FSO), retinal imaging in the human eye, confocal microscope, coherent beam combination (CBC) techniques and extended objects.
Full Text Available The paper presents the Simple Kalman filter (SKF that has been designed for the control of digital adaptive antenna arrays. The SKF has been applied to the pilot signal system and the steering vector one. The above systems based on the SKF are compared with adaptive antenna arrays controlled by the classical LMS and the Variable Step Size (VSS LMS algorithms and by the pure Kalman filter. It is shown that the pure Kalman filter is the most convenient for the control of the adaptive arrays because it does not require any a priori information about noise statistics and excels in high rate of convergence and low misadjustment. Extremely high computational requirements are drawback of this filter. Hence, if low computational power of signal processors is at the disposal, the SKF is recommended to be used. Computational requirements of the SKF are of the same order as the classical LMS algorithm exhibits. On the other hand, all the important features of the pure Kalman filter are inherited by the SKF. The paper shows that presented Kalman filters can be regarded as special gradient algorithms. That is why they can be compared with the LMS family.
Thor I. Fossen
Full Text Available Robust adaptive control of underwater vehicles in 6 DOF is analysed in the context of measurement noise. The performance of the adaptive control laws of Sadegh and Harowitz (1990 and Slotine and Benedetto (1990 are compared. Both these schemes require that all states are measured, that is the velocities and positions in surge, sway, heave, roll, pitch and yaw. However, for underwater vehicles it is difficult to measure the linear velocities whereas angular velocity measurements can be obtained by using a 3 axes angular rate sensor. This problem is addressed by designing a nonlinear observer for linear velocity state estimation. The proposed observer requires that the position and the attitude are measured, e.g. by using a hydroacoustic positioning system for linear positions, two gyros for roll and pitch and a compass for yaw. In addition angular rate measurements will be assumed available from a 3-axes rate sensor or a state estimator. It is also assumed that the measurement rate is limited to 2 Hz for all the sensors. Simulation studies with a 3 DOF AUV model are used to demonstrate the convergence and robustness of the adaptive control laws and the velocity state observer.
Nielsen, Kræn Vodder; Blanke, Mogens; Eriksson, Lars
Environmental concern has led the International Maritime Organization to restrict NO푥 emissions from marine diesel engines. Exhaust gas recirculation (EGR) systems have been introduced in order to comply to the new standards. Traditional fixed-gain feedback methods are not able to control the EGR...... is generalized to a class of first order Hammerstein systems with sensor delay and exponentially converging bounds of the control error are proven analytically. It is then shown how to apply the method to the EGR system of a two-stroke crosshead diesel engine. The controller is validated by closed loop...... system adequately in engine loading transients so alternative methods are needed. This paper presents the design, convergence proofs and experimental validation of an adaptive feedforward controller that significantly improves the performance in loading transients. First the control concept...
Full Text Available The problem of reactive power control for mains-side inverter (MSI in doubly fed induction generator (DFIG is studied in this paper. To accommodate the modelling nonlinearities and inherent uncertainties, a novel robust adaptive control algorithm for MSI is proposed by utilizing Lyapunov theory that ensures asymptotic stability of the system under unpredictable external disturbances and significant parametric uncertainties. The distinguishing benefit of the aforementioned scheme consists in its capabilities to maintain satisfactory performance under varying operation conditions without the need for manually redesigning or reprogramming the control gains in contrast to the commonly used PI/PID control. Simulations are also built to confirm the correctness and benefits of the control scheme.
Full Text Available Unmanned aerial vehicles have enormous potential applications in military and civil fields. A Quanser's 3-DOF helicopter is a simplified and benchmark experimental model for validating the effectiveness of various flight control algorithms. The attitude control of the 3-DOF helicopter is a challenging task since the helicopter is an under-actuated system with strong coupling and model uncertainty characteristics. In this paper, an adaptive integral backstepping algorithm is proposed to realize robust control of the 3-DOF helicopter. The proposed control algorithm can estimate model uncertainties online and improve the robustness of the control system. Simulation and experiment results demonstrate that the proposed algorithm performs well in tracking and under model uncertainties.
Yang, Yongheng; Zhou, Keliang; Blaabjerg, Frede
A wider spread adoption of power electronic converters interfaced renewable energy systems has brought more attention to harmonic issues to the electrical grid, and means are taken to improve it in the control. More advanced closed-loop harmonic controllers are thus demanded to enhance...... the renewable energy integration in order to be grid-friendly. However, usually being treated as a constant factor in the design of harmonic controllers, the grid frequency varies with the generation-load imbalance, and thus may lead to deterioration of the power quality. This paper explores the frequency...... sensitivity of the most popular harmonic controllers for grid-interfaced converters. The frequency adaptability of these harmonic controllers is evaluated in the presence of a variable grid frequency within a specified reasonable range, e.g., +-1% of the nominal grid frequency (50 Hz). Solutions...
Zain Anwar Ali
Full Text Available The paper presents an adaptive hybrid scheme which is based on fuzzy regulation, pole-placement, and tracking (RST control algorithm for controlling the attitude and altitude of trirotor UAV. The dynamic and kinematic model of Unmanned Aerial Vehicle (UAV is unstable and nonlinear in nature with 6 degrees of freedom (DOF; that is why the stabilization of aerial vehicle is a difficult task. To stabilize the nonlinear behavior of our UAV, an adaptive hybrid controller algorithm is used, in which RST controller tuning is performed by adaptive gains of fuzzy logic controller. Simulated results show that fuzzy based RST controller gives better robustness as compared to the classical RST controller.
Rigatos, G.; Siano, P.; Loia, V.; Tommasetti, A.; Troisi, O.
A flatness-based adaptive fuzzy control is applied to the problem of stabilization of the dynamics of a chaotic finance system, describing interaction between the interest rate, the investment demand and the price exponent. By proving that the system is differentially flat and by applying differential flatness diffeomorphisms, its transformation to the linear canonical (Brunovsky) is performed. For the latter description of the system, the design of a stabilizing state feedback controller becomes possible. A first problem in the design of such a controller is that the dynamic model of the finance system is unknown and thus it has to be identified with the use neurofuzzy approximators. The estimated dynamics provided by the approximators is used in the computation of the control input, thus establishing an indirect adaptive control scheme. The learning rate of the approximators is chosen from the requirement the system's Lyapunov function to have always a negative first-order derivative. Another problem that has to be dealt with is that the control loop is implemented only with the use of output feedback. To estimate the non-measurable state vector elements of the finance system, a state observer is implemented in the control loop. The computation of the feedback control signal requires the solution of two algebraic Riccati equations at each iteration of the control algorithm. Lyapunov stability analysis demonstrates first that an H-infinity tracking performance criterion is satisfied. This signifies elevated robustness against modelling errors and external perturbations. Moreover, the global asymptotic stability is proven for the control loop.
Bianchi Piccinini, Giulio Francesco; Rodrigues, Carlos Manuel; Leitão, Miguel; Simões, Anabela
The Adaptive Cruise Control is an Advanced Driver Assistance System (ADAS) that allows maintaining given headway and speed, according to settings pre-defined by the users. Despite the potential benefits associated to the utilization of ACC, previous studies warned against negative behavioral adaptations that might occur while driving with the system activated. Unfortunately, up to now, there are no unanimous results about the effects induced by the usage of ACC on speed and time headway to the vehicle in front. Also, few studies were performed including actual users of ACC among the subjects. This research aimed to investigate the effect of the experience gained with ACC on speed and time headway for a group of users of the system. In addition, it explored the impact of ACC usage on speed and time headway for ACC users and regular drivers. A matched sample driving simulator study was planned as a two-way (2×2) repeated measures mixed design, with the experience with ACC as between-subjects factor and the driving condition (with ACC and manually) as within-subjects factor. The results show that the usage of ACC brought a small but not significant reduction of speed and, especially, the maintenance of safer time headways, being the latter result greater for ACC users, probably as a consequence of their experience in using the system. The usage of ACC did not cause any negative behavioral adaptations to the system regarding speed and time headway. Based on this research work, the Adaptive Cruise Control showed the potential to improve road safety for what concerns the speed and the time headway maintained by the drivers. The speed of the surrounding traffic and the minimum time headway settable through the ACC seem to have an important effect on the road safety improvement achievable with the system. Copyright © 2014 Elsevier Ltd. All rights reserved.
Lei, Meizhen; Wang, Liqiang
The halbach-type linear oscillatory motor (HT-LOM) is multi-variable, highly coupled, nonlinear and uncertain, and difficult to get a satisfied result by conventional PID control. An incremental adaptive fuzzy controller (IAFC) for stroke tracking was presented, which combined the merits of PID control, the fuzzy inference mechanism and the adaptive algorithm. The integral-operation is added to the conventional fuzzy control algorithm. The fuzzy scale factor can be online tuned according to the load force and stroke command. The simulation results indicate that the proposed control scheme can achieve satisfied stroke tracking performance and is robust with respect to parameter variations and external disturbance.
Yun-Ping, Sun; Jun-Min, Li; Hui-Lin, Wang; Jiang-An, Wang
In this paper, a learning control approach is applied to the generalized projective synchronisation (GPS) of different chaotic systems with unknown periodically time-varying parameters. Using the Lyapunov–Krasovskii functional stability theory, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to make the states of two different chaotic systems asymptotically synchronised. The scheme is successfully applied to the generalized projective synchronisation between the Lorenz system and Chen system. Moreover, numerical simulations results are used to verify the effectiveness of the proposed scheme. (general)
Full Text Available This paper presents a robust adaptive fuzzy sliding mode control method for a class of uncertain nonlinear systems. The fractional order calculus is employed in the parameter updating stage. The underlying stability analysis as well as parameter update law design is carried out by Lyapunov based technique. In the simulation, two examples including a comparison with the traditional integer order counterpart are given to show the effectiveness of the proposed method. The main contribution of this paper consists in the control performance is better for the fractional order updating law than that of traditional integer order.
Nguyen, Nhan; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan
This paper describes output feedback adaptive control approaches for non-minimum phase SISO systems with relative degree 1 and non-strictly positive real (SPR) MIMO systems with uniform relative degree 1 using the optimal control modification method. It is well-known that the standard model-reference adaptive control (MRAC) cannot be used to control non-SPR plants to track an ideal SPR reference model. Due to the ideal property of asymptotic tracking, MRAC attempts an unstable pole-zero cancellation which results in unbounded signals for non-minimum phase SISO systems. The optimal control modification can be used to prevent the unstable pole-zero cancellation which results in a stable adaptation of non-minimum phase SISO systems. However, the tracking performance using this approach could suffer if the unstable zero is located far away from the imaginary axis. The tracking performance can be recovered by using an observer-based output feedback adaptive control approach which uses a Luenberger observer design to estimate the state information of the plant. Instead of explicitly specifying an ideal SPR reference model, the reference model is established from the linear quadratic optimal control to account for the non-minimum phase behavior of the plant. With this non-minimum phase reference model, the observer-based output feedback adaptive control can maintain stability as well as tracking performance. However, in the presence of the mismatch between the SPR reference model and the non-minimum phase plant, the standard MRAC results in unbounded signals, whereas a stable adaptation can be achieved with the optimal control modification. An application of output feedback adaptive control for a flexible wing aircraft illustrates the approaches.
Berkhoff, Arthur P.; Wesselink, J.M.
Model errors in multiple-input multiple-output adaptive controllers for reduction of broadband noise and vibrations may lead to unstable systems or increased error signals. In this paper, a combination of high-authority control(HAC)and low-authority control (LAC)is considered for improved
Schaller, T.; Raggenbass, A.; Reissner, J.
The increasing level of competition in the sheet-metal working industry requires measures to reduce costs. There is considerable potential in the simplification of process procedures. Automated handling and the further processing of bent semifinished products require a high level of manufacturing precision in bending processes. However deviations in angles of several degrees from the desired values can arise as a result of material variations within one batch. An adaptive control system is developed in order to avoid expensive further manufacturing steps. This calculates the parameters required for the correction of the process control from variations in process values measured online. These adjustment values must be communicated to the bending machine and set during the ongoing forming process. For larger series of parts the coefficients of the correction matrix are also continuously improved, so that an optimum adaptive correction can be achieved for the current scatter in the material properties. The adaptive correction procedure is particularly effective in combination with the three-point bending technology. The highest levels of angular precision can already be achieved after a short independent optimization phase. However, the current spectrum of parts for a certain manufacturing task and a defined quality requirement should provide the basis for decisions concerning the economy of this process arrangement.
Sun, Xiaofeng; Tian, Yanjun; Chen, Zhe
an adaptive droop control method based on online evaluation of power decouple matrix for inverter connected distributed generations in distribution system. Traditional decoupled power control is simply based on line impedance parameter, but the load characteristics also cause the power coupling, and alter......The integration of renewable energy technology is making the power distribution system more flexible, but also introducing challenges for traditional technology. With the nature of intermittent and less inertial, renewable energy-based generations need effective control methods to cooperate...... with other devices, such as storage, loads and the utility grid. The widely used power frequency (P–f) droop control is based on the precondition of inductive line impedance, but the low-voltage system is mainly resistive, and also the different load character needs to be considered. This study presents...
Rasmussen, Henrik; Larsen, Lars F. S.
are capable of adapting to variety of systems. This paper proposes a novel method for superheat and capacity control of refrigeration systems; namely by controlling the superheat by the compressor speed and capacity by the refrigerant flow. A new low order nonlinear model of the evaporator is developed......In a refrigeration process heat is absorbed in an evaporator by evaporating a flow of liquid refrigerant at low pressure and temperature. Controlling the evaporator inlet valve and the compressor in such a way that a high degree of liquid filling in the evaporator is obtained at all compressor...... capacities ensures a high energy efficiency. The level of liquid filling is indirectly measured by the superheat. Introduction of variable speed compressors and electronic expansion valves enables the use of more sophisticated control algorithms, giving a higher degree of performance and just as important...
Kaufman, Howard; Swift, David C.; Cummings, Steven T.; Shankey, Jeffrey R.
The results of controlling A PUMA 560 Robotic Manipulator and the NASA shuttle Remote Manipulator System (RMS) using a Command Generator Tracker (CGT) based Model Reference Adaptive Controller (DMRAC) are presented. Initially, the DMRAC algorithm was run in simulation using a detailed dynamic model of the PUMA 560. The algorithm was tuned on the simulation and then used to control the manipulator using minimum jerk trajectories as the desired reference inputs. The ability to track a trajectory in the presence of load changes was also investigated in the simulation. Satisfactory performance was achieved in both simulation and on the actual robot. The obtained responses showed that the algorithm was robust in the presence of sudden load changes. Because these results indicate that the DMRAC algorithm can indeed be successfully applied to the control of robotic manipulators, additional testing was performed to validate the applicability of DMRAC to simulated dynamics of the shuttle RMS.
Nguyen, Nhan T.
Autonomy of aerospace systems requires the ability for flight control systems to be able to adapt to complex uncertain dynamic environment. In spite of the five decades of research in adaptive control, the fact still remains that currently no adaptive control system has ever been deployed on any safety-critical or human-rated production systems such as passenger transport aircraft. The problem lies in the difficulty with the certification of adaptive control systems since existing certification methods cannot readily be used for nonlinear adaptive control systems. Research to address the notion of metrics for adaptive control began to appear in the recent years. These metrics, if accepted, could pave a path towards certification that would potentially lead to the adoption of adaptive control as a future control technology for safety-critical and human-rated production systems. Development of certifiable adaptive control systems represents a major challenge to overcome. Adaptive control systems with learning algorithms will never become part of the future unless it can be proven that they are highly safe and reliable. Rigorous methods for adaptive control software verification and validation must therefore be developed to ensure that adaptive control system software failures will not occur, to verify that the adaptive control system functions as required, to eliminate unintended functionality, and to demonstrate that certification requirements imposed by regulatory bodies such as the Federal Aviation Administration (FAA) can be satisfied. This presentation will discuss some of the technical issues with adaptive flight control and related V&V challenges.
Liu, Derong; Wang, Ding; Yang, Xiong; Li, Hongliang
This book covers the most recent developments in adaptive dynamic programming (ADP). The text begins with a thorough background review of ADP making sure that readers are sufficiently familiar with the fundamentals. In the core of the book, the authors address first discrete- and then continuous-time systems. Coverage of discrete-time systems starts with a more general form of value iteration to demonstrate its convergence, optimality, and stability with complete and thorough theoretical analysis. A more realistic form of value iteration is studied where value function approximations are assumed to have finite errors. Adaptive Dynamic Programming also details another avenue of the ADP approach: policy iteration. Both basic and generalized forms of policy-iteration-based ADP are studied with complete and thorough theoretical analysis in terms of convergence, optimality, stability, and error bounds. Among continuous-time systems, the control of affine and nonaffine nonlinear systems is studied using the ADP app...
During the upgrading of current electric power grid, it is expected to develop smarter, more robust and more reliable power systems integrated with distributed generations. To realize these objectives, traditional control techniques are no longer effective in either stabilizing systems or delivering optimal and robust performances. Therefore, development of advanced control methods has received increasing attention in power engineering. This work addresses two specific problems in the control of solar panel based microgrid systems. First, a new control scheme is proposed for the microgrid systems to achieve optimal energy conversion ratio in the solar panels. The control system can optimize the efficiency of the maximum power point tracking (MPPT) algorithm by implementing two layers of adaptive control. Such a hierarchical control architecture has greatly improved the system performance, which is validated through both mathematical analysis and computer simulation. Second, in the development of the microgrid transmission system, the issues related to the tele-communication delay and constant power load (CPL)'s negative incremental impedance are investigated. A reference model based method is proposed for pole and zero placements that address the challenges of the time delay and CPL in closed-loop control. The effectiveness of the proposed modeling and control design methods are demonstrated in a simulation testbed. Practical aspects of the proposed methods for general microgrid systems are also discussed.
Kilic, Erdal; Ozcalik, Hasan Riza; Yilmaz, Saban
This paper proposes a model reference adaptive speed controller based on artificial neural network for induction motor drives. The performance of traditional feedback controllers has been insufficient in speed control of induction motors due to nonlinear structure of the system, changing environmental conditions, and disturbance input effects. A successful speed control of induction motor requires a nonlinear control system. On the other hand, in recent years, it has been demonstrated that ar...
Mullakkal Babu, F.A.; Wang, M.; van Arem, B.; Happee, R.; Rosetti, R.; Wolf, D.
Current Full Range Adaptive Cruise Control (FRACC) systems switch between separate adaptive cruise control and collision avoidance systems. This can lead to jerky responses and discomfort during the transition between the two control modes. We propose a Full Range Adaptive Cruise Control (FRACC)
Kannan, Suresh K.
This thesis extends the use of neural-network-based model reference adaptive control to systems that occur as cascades. In general, these systems are not feedback linearizable. The approach taken is that of approximate feedback linearization of upper subsystems whilst treating the lower-subsystem states as virtual actuators. Similarly, lower-subsystems are also feedback linearized. Typically, approximate inverses are used for linearization purposes. Model error arising from the use of an approximate inverse is minimized using a neural-network as an adaptive element. Incorrect adaptation due to (virtual) actuator saturation and dynamics is avoided using the Pseudocontrol Hedging method. Using linear approximate inverses and linear reference models generally result in large desired pseudocontrol for large external commands. Even if the provided external command is feasible (null-controllable), there is no guarantee that the reference model trajectory is feasible. In order to mitigate this, nonlinear reference models based on nested-saturation methods are used to constrain the evolution of the reference model and thus the plant states. The method presented in this thesis lends itself to the inner-outer loop control of air vehicles, where the inner-loop controls attitude dynamics and the outer-loop controls the translational dynamics of the vehicle. The outer-loop treats the closed loop attitude dynamics as an actuator. Adaptation to uncertainty in the attitude, as well as the translational dynamics, is introduced, thus minimizing the effects of model error in all six degrees of freedom and leading to more accurate position tracking. A pole-placement approach is used to choose compensator gains for the tracking error dynamics. This alleviates timescale separation requirements, allowing the outer loop bandwidth to be closer to that of the inner loop, thus increasing position tracking performance. A poor model of the attitude dynamics and a basic kinematics model is
Fereidouni, Alireza; Masoum, Mohammad A S; Moghbel, Moayed
In this paper, an adaptive configuration for PID type fuzzy logic controller (FLC) is proposed to improve the performances of both conventional PID (C-PID) controller and conventional PID type FLC (C-PID-FLC). The proposed configuration is called adaptive because its output scaling factors (SFs) are dynamically tuned while the controller is functioning. The initial values of SFs are calculated based on its well-tuned counterpart while the proceeding values are generated using a proposed stochastic hybrid bacterial foraging particle swarm optimization (h-BF-PSO) algorithm. The performance of the proposed configuration is evaluated through extensive simulations for different operating conditions (changes in reference, load disturbance and noise signals). The results reveal that the proposed scheme performs significantly better over the C-PID controller and the C-PID-FLC in terms of several performance indices (integral absolute error (IAE), integral-of-time-multiplied absolute error (ITAE) and integral-of-time-multiplied squared error (ITSE)), overshoot and settling time for plants with and without dead time. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.
Chunjie Zhou; Shuang Huang; Quan Yin; Duc Cuong Quach
In this paper, we present an improved Direct Adaptive Fuzzy (IDAF) controller applied to general control DC motor speed system. In particular, an IDAF algorithm is designed to control an uncertain DC motor speed to track a given reference signal. In fact, the quality of the control system depends significantly on the amount of fuzzy rules-fuzzy sets and the updating coefficient of the adaptive rule. This can be observed clearly by the system error when the reference input is constant and out ...
Xuan Phu Do
Full Text Available This paper presents a new direct adaptive fuzzy controller and its effectiveness is verified by investigating the damping force tracking control of magnetorheological (MR fluid based damper (MR damper in short system. In the formulation of the proposed controller, a model of interval type 2 fuzzy controller is combined with the direct adaptive control to achieve high performance in vibration control. In addition, H∞ (H infinity tracking technique is used in building a model of the direct adaptive fuzzy controller in which an enhanced iterative algorithm is combined with the fuzzy model. After establishing a closed-loop control structure to achieve high control performance, a cylindrical MR damper is adopted and damping force tracking results are obtained and discussed. In addition, in order to demonstrate the effectiveness of the proposed control strategy, two existing controllers are modified and tested for comparative work. It has been demonstrated from simulation and experiment that the proposed control scheme provides much better control performance in terms of damping force tracking error. This leads to excellent vibration control performance of the semiactive MR damper system associated with the proposed controller.
Shen, Qikun; Shi, Peng
This book provides recent theoretical developments in and practical applications of fault diagnosis and fault tolerant control for complex dynamical systems, including uncertain systems, linear and nonlinear systems. Combining adaptive control technique with other control methodologies, it investigates the problems of fault diagnosis and fault tolerant control for uncertain dynamic systems with or without time delay. As such, the book provides readers a solid understanding of fault diagnosis and fault tolerant control based on adaptive control technology. Given its depth and breadth, it is well suited for undergraduate and graduate courses on linear system theory, nonlinear system theory, fault diagnosis and fault tolerant control techniques. Further, it can be used as a reference source for academic research on fault diagnosis and fault tolerant control, and for postgraduates in the field of control theory and engineering. .
Morishita, Shin; Ura, Tamaki
Four applications of electrorheological (ER) fluid to vibration control actuators and an adaptive neural-net control system suitable for the controller of ER actuators are described: a shock absorber system for automobiles, a squeeze film damper bearing for rotational machines, a dynamic damper for multidegree-of-freedom structures, and a vibration isolator. An adaptive neural-net control system composed of a forward model network for structural identification and a controller network is introduced for the control system of these ER actuators. As an example study of intelligent vibration control systems, an experiment was performed in which the ER dynamic damper was attached to a beam structure and controlled by the present neural-net controller so that the vibration in several modes of the beam was reduced with a single dynamic damper.
Full Text Available This paper proposes adaptive Maximum Power Point Tracking (MPPT controller for Permanent Magnet Synchronous Generator (PMSG wind turbine and direct power control for grid side inverter for transformer less integration of wind energy. PMSG wind turbine with two back to back voltage source converters are considered more efficient, used to make real and reactive power control. The optimal control strategy has introduced for integrated control of PMSG Maximum Power Extraction, DC link voltage control and grid voltage support controls. Simulation model using MATLAB Simulink has developed to investigate the performance of proposed control techniques for PMSG wind turbine steady and variable wind conditions. This paper shows that the direct driven grid connected PMSG system has excellent performances and confirms the feasibility of the proposed techniques. While the wind turbine market continues to be dominated by conventional gear-driven wind turbine systems, the direct drive is attracting attention. PM machines are more attractive and superior with higher efficiency and energy yield, higher reliability, and power-to-weight ratio compared with electricity-excited machines.
Aslam Butt, Waseem; Yan, Lin; Amezquita S., Kendrick
In this article, non-linear adaptive dynamic surface air speed and flight path angle control designs are presented for the longitudinal dynamics of a flexible hypersonic flight vehicle. The tracking performance of the control design is enhanced by introducing a novel integral term that caters to avoiding a large initial control signal. To ensure feasibility, the design scheme incorporates magnitude and rate constraints on the actuator commands. The uncertain non-linear functions are approximated by an efficient use of the neural networks to reduce the computational load. A detailed stability analysis shows that all closed-loop signals are uniformly ultimately bounded and the ? tracking performance is guaranteed. The robustness of the design scheme is verified through numerical simulations of the flexible flight vehicle model.
In the existing results on chaos control and synchronization based on the adaptive controlling technique (ACT), a uniform Lipschitz condition on a given dynamical system is always assumed in advance. However, without this uniform Lipschitz condition, the ACT might be failed in both theoretical analysis and in numerical experiment. This Letter shows how to utilize the ACT to get a rigorous control for the system which is not uniformly Lipschitz but only locally Lipschitz, and even for the system which has unbounded trajectories. In fact, the ACT is proved to possess some limitation, which is actually induced by the nonlinear degree of the original system. Consequently, a piecewise ACT is proposed so as to improve the performance of the existing techniques
Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua
In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.
Full Text Available In this paper, the problem of the position and attitude tracking of an autonomous underwater vehicle (AUV in the horizontal plane, under the presence of ocean current disturbances is discussed. The effect of the gradual variation of the parameters is taken into account. The effectiveness of the adaptive controller is compared with a feedback linearization method and fuzzy gain control approach. The proposed strategy has been tested through simulations. Also, the performance of the propos-ed method is compared with other strategies given in some other studies. The boundedness and asymptotic converge-nce properties of the control algorithm and its semi-global stability are analytically proven using Lyapunov stability theory and Barbalat’s lemma.
This book provides a comprehensive discussion of nonlinear multi-modal structural vibration problems, and shows how vibration suppression can be applied to such systems by considering a sample set of relevant control techniques. It covers the basic principles of nonlinear vibrations that occur in flexible and/or adaptive structures, with an emphasis on engineering analysis and relevant control techniques. Understanding nonlinear vibrations is becoming increasingly important in a range of engineering applications, particularly in the design of flexible structures such as aircraft, satellites, bridges, and sports stadia. There is an increasing trend towards lighter structures, with increased slenderness, often made of new composite materials and requiring some form of deployment and/or active vibration control. There are also applications in the areas of robotics, mechatronics, micro electrical mechanical systems, non-destructive testing and related disciplines such as structural health monitoring. Two broader ...
Martín-Sánchez, Juan M
This book is a didactic explanation of the developments of predictive, adaptive predictive and optimized adaptive control, including the latest methodology of adaptive predictive expert (ADEX) control, and their practical applications. It is focused on the stability perspective, used in the introduction of these methodologies, and is divided into six parts, with exercises and real-time simulations provided for the reader as appropriate. ADEX Optimized Adaptive Controllers and Systems begins with the conceptual and intuitive knowledge of the technology and derives the stability conditions to be verified by the driver block and the adaptive mechanism of the optimized adaptive controller to guarantee achievement of desired control performance. The second and third parts are centered on the design of the driver block and adaptive mechanism, which verify these stability conditions. The authors then proceed to detail the stability theory that supports predictive, adaptive predictive and optimized adaptive control m...
Mayer, D.; Pfeiffer, T.; Vrbata, J.; Melz, T.
Tuned vibration absorbers have become common for passive vibration reduction in many industrial applications. Lightly damped absorbers (also called neutralizers) can be used to suppress narrowband disturbances by tuning them to the excitation frequency. If the resonance is adapted in-operation, the performance of those devices can be significantly enhanced, or inertial mass can be decreased. However, the integration of actuators, sensors and control electronics into the system raises new design challenges. In this work, the development of adaptive-passive systems for vibration reduction at an industrial scale is presented. As an example, vibration reduction of a ship engine was studied in a full scale test. Simulations were used to study the feasibility and evaluate the system concept at an early stage. Several ways to adjust the resonance of the neutralizer were evaluated, including piezoelectric actuation and common mechatronic drives. Prototypes were implemented and tested. Since vibration absorbers suffer from high dynamic loads, reliability tests were used to assess the long-term behavior under operational conditions and to improve the components. It was proved that the adaptive systems are capable to withstand the mechanical loads in an industrial application. Also a control strategy had to be implemented in order to track the excitation frequency. The most mature concepts were integrated into the full scale test. An imbalance exciter was used to simulate the engine vibrations at a realistic level experimentally. The neutralizers were tested at varying excitation frequencies to evaluate the tracking capabilities of the control system. It was proved that a significant vibration reduction is possible.
Zirkohi, Majid Moradi
The purpose of this paper is to improve theVelocity Update Relaxation Particle Swarm Optimization algorithm (VURPSO). The improved algorithm is called Adaptive VURPSO (AVURPSO) algorithm. Then, an optimal design of a Proportional-Integral-Derivative (PID) controller is obtained using the AVURPSO algorithm. An adaptive momentum factor is used to regulate a trade-off between the global and the local exploration abilities in the proposed algorithm. This operation helps the system to reach the optimal solution quickly and saves the computation time. Comparisons on the optimal PID controller design confirm the superiority of AVURPSO algorithm to the optimization algorithms mentioned in this paper namely the VURPSO algorithm, the Ant Colony algorithm, and the conventional approach. Comparisons on the speed of convergence confirm that the proposed algorithm has a faster convergence in a less computation time to yield a global optimum value. The proposed AVURPSO can be used in the diverse areas of optimization problems such as industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning. The proposed AVURPSO algorithm is efficiently used to design an optimal PID controller.
Fafoutis, Xenofon; Dragoni, Nicola
ODMAC (On-Demand Media Access Control) is a recently proposed MAC protocol designed to support individual duty cycles for Energy Harvesting — Wireless Sensor Networks (EH-WSNs). Individual duty cycles are vital for EH-WSNs, because they allow nodes to adapt their energy consumption to the ever......-changing environmental energy sources. In this paper, we present an improved and extended version of ODMAC and we analyze it by means of an analytical model that can approximate several performance metrics in an arbitrary network topology. The simulations and the analytical experiments show ODMAC's ability to satisfy...
METİN, Muzaffer; GÜÇLÜ, Rahmi
In this study, a conventional PID type fuzzy controller and parameter adaptive fuzzy controller are designed to control vibrations actively of a light rail transport vehicle which modeled as 6 degree-of-freedom system and compared performances of these two controllers. Rail vehicle model consists of a passenger seat and its suspension system, vehicle body, bogie, primary and secondary suspensions and wheels. The similarity between mathematical model and real system is shown by compar...
Xiao, Nan; Gao, Wei; Song, Zongxi
With the rapid development of adaptive optics technology, it is widely used in the fields of astronomical telescope imaging, laser beam shaping, optical communication and so on. As the key component of adaptive optics systems, the deformable mirror plays a role in wavefront correction. In order to achieve the high speed and high precision of deformable mirror system tracking control, it is necessary to find out the influence of each link on the system performance to model the system and design the controller. This paper presents a method about the piezoelectric deformable mirror driving control system.
Naus, G.J.L.; Ploeg, J.; Molengraft, M.J.G. van de; Heemels, W.P.M.H.; Steinbuch, M.
The combination of different desirable characteristics and situation-dependent behavior cause the design of adaptive cruise control (ACC) systems to be time consuming and tedious. This chapter presents a systematic approach for the design and tuning of an ACC, based on model predictive control
Naus, G.J.L.; Ploeg, J.; Molengraft, M.J.G. van de; Heemels, W.P.M.H.; Steinbuch, M.
The combination of different characteristics and situation-dependent behavior cause the design of adaptive cruise control (ACC) systems to be time consuming. This paper presents a systematic approach for the design of a parameterized ACC, based on explicit model predictive control. A unique feature
Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.
Bolder, J.J.; Witvoet, G.; De Baar, M.R.; Steinbuch, M.; Van de Wouw, N.; Haring, M.A.M.; Westerhof, E.; Doelman, N.J.
The systematic design of a robust adaptive control strategy for the sawtooth period using electron cyclotron current drive (ECCD) is presented. Recent developments in extremum seeking control (ESC) are employed to derive an optimized controller structure and offer practical tuning guidelines for its parameters. In this technique a cost function in terms of the desired sawtooth period is optimized online by changing the ECCD deposition location based on online estimations of the gradient of the cost function. The controller design does not require a detailed model of the sawtooth instability. Therefore, the proposed ESC is widely applicable to any sawtoothing plasma or plasma simulation and is inherently robust against uncertainties or plasma variations. Moreover, it can handle a broad class of disturbances. This is demonstrated by time-domain simulations, which show successful tracking of time-varying sawtooth period references throughout the whole operating space, even in the presence of variations in plasma parameters, disturbances and slow launcher mirror dynamics. Due to its simplicity and robustness the proposed ESC is a valuable sawtooth control candidate for any experimental tokamak plasma, and may even be applicable to other fusion-related control problems. (paper)
Yang Hui; Tang Ming; Zhang Haifeng
Most studies on adaptive networks concentrate on the properties of steady state, but neglect transient dynamics. In this study, we pay attention to the emergence of community structure in the transient process and the effects of community-based control strategies on epidemic spreading. First, by normalizing the modularity, we investigate the evolution of community structure during the transient process, and find that a strong community structure is induced by the rewiring mechanism in the early stage of epidemic dynamics, which, remarkably, delays the outbreak of disease. We then study the effects of control strategies started at different stages on the prevalence. Both immunization and quarantine strategies indicate that it is not ‘the earlier, the better’ for the implementation of control measures. And the optimal control effect is obtained if control measures can be efficiently implemented in the period of a strong community structure. For the immunization strategy, immunizing the susceptible nodes on susceptible–infected links and immunizing susceptible nodes randomly have similar control effects. However, for the quarantine strategy, quarantining the infected nodes on susceptible–infected links can yield a far better result than quarantining infected nodes randomly. More significantly, the community-based quarantine strategy performs better than the community-based immunization strategy. This study may shed new light on the forecast and the prevention of epidemics among humans. (paper)
Full Text Available In this paper a hybrid controller combining a linear model following controller (LMFC and fuzzy logic control (FLC for speed vector controlled permanent magnet synchronous motor (PMSM is described on this study. The FLC is introduced at the adaptive mechanism level. First, an LMFC system is designed to allow the plant states to be controlled to follow the states produced by a reference model. In the nominal conditions, the model following is perfect and the adaptive mechanism based on the fuzzy logic is idle. Secondly, when parameter variations or external disturbances occur, an augmented signal will be generated by FLC mechanism to preserve the desired model following control performance. The effectiveness and robustness of the proposed controller is demonstrated by some simulation results.
Manoonpong, Poramate; Parlitz, Ulrich; Wörgötter, Florentin
basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based...... on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models...... that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines....
Zhu, Zhen-Cai; Li, Xiang; Shen, Gang; Zhu, Wei-Dong
This paper concerns wire rope tension control of a double-rope winding hoisting system (DRWHS), which consists of a hoisting system employed to realize a transportation function and an electro-hydraulic servo system utilized to adjust wire rope tensions. A dynamic model of the DRWHS is developed in which parameter uncertainties and external disturbances are considered. A comparison between simulation results using the dynamic model and experimental results using a double-rope winding hoisting experimental system is given in order to demonstrate accuracy of the dynamic model. In order to improve the wire rope tension coordination control performance of the DRWHS, a robust nonlinear adaptive backstepping controller (RNABC) combined with a nonlinear disturbance observer (NDO) is proposed. Main features of the proposed combined controller are: (1) using the RNABC to adjust wire rope tensions with consideration of parameter uncertainties, whose parameters are designed online by adaptive laws derived from Lyapunov stability theory to guarantee the control performance and stability of the closed-loop system; and (2) introducing the NDO to deal with uncertain external disturbances. In order to demonstrate feasibility and effectiveness of the proposed controller, experimental studies have been conducted on the DRWHS controlled by an xPC rapid prototyping system. Experimental results verify that the proposed controller exhibits excellent performance on wire rope tension coordination control compared with a conventional proportional-integral (PI) controller and adaptive backstepping controller. Copyright © 2017 ISA. All rights reserved.
Poyneer, L; Veran, J P
In many scenarios, an Adaptive Optics (AO) control system operates in the presence of temporally non-white noise. We use a Kalman filter with a state space formulation that allows suppression of this colored noise, hence improving residual error over the case where the noise is assumed to be white. We demonstrate the effectiveness of this new filter in the case of the estimated Gemini Planet Imager tip-tilt environment, where there are both common-path and non-common path vibrations. We discuss how this same framework can also be used to suppress spatial aliasing during predictive wavefront control assuming frozen flow in a low-order AO system without a spatially filtered wavefront sensor, and present experimental measurements from Altair that clearly reveal these aliased components.
Lim, Tae W.
Dynamic characteristics of airframe structures are expected to vary as aircraft flight conditions change. Accurate knowledge of the changing dynamic characteristics is crucial to enhancing the performance of the active noise control system using feedback control. This research investigates the development of an adaptive, on-line state estimator using a neural network concept to conduct active noise control. In this research, an algorithm has been developed that can be used to estimate displacement and velocity responses at any locations on the structure from a limited number of acceleration measurements and input force information. The algorithm employs band-pass filters to extract from the measurement signal the frequency contents corresponding to a desired mode. The filtered signal is then used to train a neural network which consists of a linear neuron with three weights. The structure of the neural network is designed as simple as possible to increase the sampling frequency as much as possible. The weights obtained through neural network training are then used to construct the transfer function of a mode in z-domain and to identify modal properties of each mode. By using the identified transfer function and interpolating the mode shape obtained at sensor locations, the displacement and velocity responses are estimated with reasonable accuracy at any locations on the structure. The accuracy of the response estimates depends on the number of modes incorporated in the estimates and the number of sensors employed to conduct mode shape interpolation. Computer simulation demonstrates that the algorithm is capable of adapting to the varying dynamic characteristics of structural properties. Experimental implementation of the algorithm on a DSP (digital signal processing) board for a plate structure is underway. The algorithm is expected to reach the sampling frequency range of about 10 kHz to 20 kHz which needs to be maintained for a typical active noise control
Chen, Li-Chiou; Jeka, John; Clark, Jane E
A reliable and adaptive relationship between action and perception is necessary for postural control. Our understanding of how this adaptive sensorimotor control develops during infancy is very limited. This study examines the dynamic visual-postural relationship during early development. Twenty healthy infants were divided into 4 developmental groups (each n=5): sitting onset, standing alone, walking onset, and 1-year post-walking. During the experiment, the infant sat independently in a virtual moving-room in which anterior-posterior oscillations of visual motion were presented using a sum-of-sines technique with five input frequencies (from 0.12 to 1.24 Hz). Infants were tested in five conditions that varied in the amplitude of visual motion (from 0 to 8.64 cm). Gain and phase responses of infants' postural sway were analyzed. Our results showed that infants, from a few months post-sitting to 1 year post-walking, were able to control their sitting posture in response to various frequency and amplitude properties of the visual motion. Infants showed an adult-like inverted-U pattern for the frequency response to visual inputs with the highest gain at 0.52 and 0.76 Hz. As the visual motion amplitude increased, the gain response decreased. For the phase response, an adult-like frequency-dependent pattern was observed in all amplitude conditions for the experienced walkers. Newly sitting infants, however, showed variable postural behavior and did not systemically respond to the visual stimulus. Our results suggest that visual-postural entrainment and sensory re-weighting are fundamental processes that are present after a few months post sitting. Sensorimotor refinement during early postural development may result from the interactions of improved self-motion control and enhanced perceptual abilities. Copyright © 2016 Elsevier B.V. All rights reserved.
Yadav Arun K.
Full Text Available In today’s world automotive industries are still putting efforts towards more autonomous vehicles (AVs. The main concern of introducing the autonomous technology is safety of driver. According to a survey 90% of accidents happen due to mistake of driver. The adaptive cruise control system (ACC is a system which combines cruise control with a collision avoidance system. The ACC system is based on laser and radar technologies. This system is capable of controlling the velocity of vehicle automatically to match the velocity of car, bus or truck in front of vehicle. If the lead vehicle gets slow down or accelerate, than ACC system automatically matches that velocity. The proposed paper is focusing on more accurate methods of detecting the preceding vehicle by using a radar and lidar sensors by considering the vehicle side slip and by controlling the distance between two vehicles. By using this approach i.e. logic for calculation of former vehicle distance and controlling the throttle valve of ACC equipped vehicle, an improvement in driving stability was achieved. The own contribution results with fuel efficient driving and with more safer and reliable driving system, but still some improvements are going on to make it more safe and reliable.
Tomashevich, Stanislav; Belyavskyi, Andrey; Andrievsky, Boris
In the paper, the results of the Passification Method with the Implicit Reference Model (IRM) approach are applied for designing the simple adaptive controller for quadrotor attitude. The IRM design technique makes it possible to relax the matching condition, known for habitual MRAC systems, and leads to simple adaptive controllers, ensuring fast tuning the controller gains, high robustness with respect to nonlinearities in the control loop, to the external disturbances and the unmodeled plant dynamics. For experimental evaluation of the adaptive systems performance, the 2DOF laboratory setup has been created. The testbed allows to safely test new control algorithms in the laboratory area with a small space and promptly make changes in cases of failure. The testing results of simple adaptive control of quadrotor attitude are presented, demonstrating efficacy of the applied simple adaptive control method. The experiments demonstrate good performance quality and high adaptation rate of the simple adaptive control system.
Cao, Jian; Su, Yumin; Zhao, Jinxin
Underwater vehicles operating in complex ocean conditions present difficulties in determining accurate dynamic models. To guarantee robustness against parameter uncertainty, an adaptive controller for dive-plane control, based on Lyapunov theory and back-stepping techniques, was proposed. In the closed-loop system, asymptotic tracking of the reference depth and pitch angle trajectories was accomplished. Simulation results were presented which show effective dive-plane control in spite of the uncertainties in the system parameters.
Wang, Yannian; Wu, Peizhi; Liu, Chengtao
To improve the stability of the large axial compressor, an efficient and special intelligent hydraulic servo control system is designed and implemented. The adaptive fuzzy PID control algorithm is used to control the position of the hydraulic servo cylinder steadily, which overcomes the drawback that the PID parameters should be adjusted based on the different applications. The simulation and the test results show that the system has a better dynamic property and a stable state performance.
Sayed Hamed Hosseini
Full Text Available This paper presents a two-loop approach for velocity and stator currents control of an Interior-type Permanent Magnet Synchronous Motor (IPMSM. In the outer loop, the reference torque obtained from a conventional PI controller gives two-axis stator reference currents based on Maximum-Torque per Ampere (MTPA strategy. In the inner loop, an adaptive fractional order sliding mode controller is designed to reach the two-axis stator currents to their reference values obtained from the MTPA method. To achieve this idea, fractional order sliding surfaces and an adaptive controller with adjustable parameters are employed. The adaptive controller is designed to increase the robustness of the proposed method against the uncertainties in stator resistance and inductances. A Lyapunov based adaptation mechanism is proposed for adjustment of the controller parameters. The optimal value of the fractional orders are obtained by optimization of an integral time absolute error performance index. The simulation results show the robustness of the proposed method against the uncertainties in stator resistance and stator inductances.
Full Text Available An adaptive sliding mode control (ASMC law is proposed in decentralized scheme for trajectory tracking control of a new concept space robot. Each joint of the system is a free ball joint capable of rotating with three degrees of freedom (DOF. A cluster of control moment gyroscopes (CMGs is mounted on each link and the base to actuate the system. The modified Rodrigues parameters (MRPs are employed to describe the angular displacements, and the equations of motion are derived using Kane’s equations. The controller for each link or the base is designed separately in decentralized scheme. The unknown disturbances, inertia parameter uncertainties and nonlinear uncertainties are classified as a “lumped” matched uncertainty with unknown upper bound, and a continuous sliding mode control (SMC law is proposed, in which the control gain is tuned by the improved adaptation laws for the upper bound on norm of the uncertainty. A general amplification function is designed and incorporated in the adaptation laws to reduce the control error without conspicuously increasing the magnitude of the control input. Uniformly ultimate boundedness of the closed loop system is proved by Lyapunov’s method. Simulation results based on a three-link system verify the effectiveness of the proposed controller.
Khooban, Mohammad-Hassan; Niknam, Taher; Blaabjerg, Frede; Davari, Pooya; Dragicevic, Tomislav
The goal of this study is to introduce a novel robust load frequency control (LFC) strategy for micro-grid(s) (MG(s)) in islanded mode operation. Admittedly, power generators in MG(s) cannot supply steady electric power output and sometimes cause unbalance between supply and demand. Battery energy storage system (BESS) is one of the effective solutions to these problems. Due to the high cost of the BESS, a new idea of Vehicle-to-Grid (V2G) is that a battery of Electric-Vehicle (EV) can be applied as a tantamount large-scale BESS in MG(s). As a result, a new robust control strategy for an islanded micro-grid (MG) is introduced that can consider electric vehicles׳ (EV(s)) effect. Moreover, in this paper, a new combination of the General Type II Fuzzy Logic Sets (GT2FLS) and the Modified Harmony Search Algorithm (MHSA) technique is applied for adaptive tuning of proportional-integral (PI) controller. Implementing General Type II Fuzzy Systems is computationally expensive. However, using a recently introduced α-plane representation, GT2FLS can be seen as a composition of several Interval Type II Fuzzy Logic Systems (IT2FLS) with a corresponding level of α for each. Real-data from an offshore wind farm in Sweden and solar radiation data in Aberdeen (United Kingdom) was used in order to examine the performance of the proposed novel controller. A comparison is made between the achieved results of Optimal Fuzzy-PI (OFPI) controller and those of Optimal Interval Type II Fuzzy-PI (IT2FPI) controller, which are of most recent advances in the area at hand. The Simulation results prove the successfulness and effectiveness of the proposed controller. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.
Alavandar, Srinivasan; Nigam, M J
Control of an industrial robot includes nonlinearities, uncertainties and external perturbations that should be considered in the design of control laws. In this paper, some new hybrid adaptive neuro-fuzzy control algorithms (ANFIS) have been proposed for manipulator control with uncertainties. These hybrid controllers consist of adaptive neuro-fuzzy controllers and conventional controllers. The outputs of these controllers are applied to produce the final actuation signal based on current position and velocity errors. Numerical simulation using the dynamic model of six DOF puma robot arm with uncertainties shows the effectiveness of the approach in trajectory tracking problems. Performance indices of RMS error, maximum error are used for comparison. It is observed that the hybrid adaptive neuro-fuzzy controllers perform better than only conventional/adaptive controllers and in particular hybrid controller structure consisting of adaptive neuro-fuzzy controller and critically damped inverse dynamics controller.
Kinnaird, Catherine R.; Ferris, Daniel P.
Locomotor adaptation in humans is not well understood. To provide insight into the neural reorganization that occurs following a significant disruption to one's learned neuromuscular map relating a given motor command to its resulting muscular action, we tied the mechanical action of a robotic exoskeleton to the electromyography (EMG) profile of the soleus muscle during walking. The powered exoskeleton produced an ankle dorsiflexion torque proportional to soleus muscle recruitment thus limiting the soleus' plantar flexion torque capability. We hypothesized that neurologically intact subjects would alter muscle activation patterns in response to the antagonistic exoskeleton by decreasing soleus recruitment. Subjects practiced walking with the exoskeleton for two 30-min sessions. The initial response to the perturbation was to “fight” the resistive exoskeleton by increasing soleus activation. By the end of training, subjects had significantly reduced soleus recruitment resulting in a gait pattern with almost no ankle push-off. In addition, there was a trend for subjects to reduce gastrocnemius recruitment in proportion to the soleus even though only the soleus EMG was used to control the exoskeleton. The results from this study demonstrate the ability of the nervous system to recalibrate locomotor output in response to substantial changes in the mechanical output of the soleus muscle and associated sensory feedback. This study provides further evidence that the human locomotor system of intact individuals is highly flexible and able to adapt to achieve effective locomotion in response to a broad range of neuromuscular perturbations. PMID:23307949
Seraji, H.; Lee, T.; Delpech, M.
The implementation and experimental validation of a direct adaptive control scheme on a PUMA 560 industrial robot is discussed. The design theory for direct adaptive control of manipulators is outlined and the test facility and software are described. Results are presented from the experiments on the simultaneous control of all of the six joint angles and control of the end-effector position and orientation of the robot. Also, the possible applications of the direct adaptive control scheme are considered.
Zolghadri Jahromi, M.; Abolhassan Tash, F.
Mold variations in continuous casting are believed to be the main cause of surface defects in the final product. Although a Pid controller is well capable of controlling the level under normal conditions, it cannot prevent large variations of mold level when a disturbance occurs in the form of nozzle unclogging. In this paper, dual controller architecture is presented, a Pid controller is used as the main controller of the plant and an adaptive neuro-fuzzy controller is used as an auxiliary controller to help the Pid during disturbed phases. The control is passed back to the Pid controller after the disturbance is being dealt with. Simulation results prove the effectiveness of this control strategy in reducing mold level variations during the unclogging period
Kirkeby, Carsten Thure; Græsbøll, Kaare; Nielsen, Søren Saxmose
consequences of continuously adapting the sampling interval in response to the estimated true prevalence in the herd. The key results were that the true prevalence was greatly affected by the hygiene level and to some extent by the test-frequency. Furthermore, the choice of prevalence that will be tolerated...... through a variety of test-strategies, but are challenged by the lack of perfect tests. Frequent testing increases the sensitivity but the costs of testing are a cause of concern for farmers. Here, we used a herd simulation model using milk ELISA tests to evaluate the epidemiological and economic...... in a control scenario had a major impact on the true prevalence in the normal hygiene setting, but less so when the hygiene was poor. The net revenue is not greatly affected by the test-strategy, because of the general variation in net revenues between farms. An exception to this is the low hygiene herd, where...
Full Text Available The aim of this review is to present the actual status of development in adaptive solar control by use of thermotropic and organic thermochromic materials. Such materials are suitable for application in smart windows. In detail polymer blends, hydrogels, resins, and thermoplastic films with a reversible temperature-dependent switching behavior are described. A comparative evaluation of the concepts for these energy efficient materials is given as well. Furthermore, the change of strategy from ordinary shadow systems to intrinsic solar energy reflection materials based on phase transition components and a first remark about their realization is reported. Own current results concerning extruded films and high thermally stable casting resins with thermotropic properties make a significant contribution to this field.
Dobrokhodov, Vladimir; Kitsios, Ioannis; Kaminer, Isaac; Jones, Kevin D.; Xargay, Enric; Hovakimyan, Naira; Cao, Chengyu; Lizarraga, Mariano I.; Gregory, Irene M.
The paper addresses initial steps involved in the development and flight implementation of new metrics driven L1 adaptive flight control system. The work concentrates on (i) definition of appropriate control driven metrics that account for the control surface failures; (ii) tailoring recently developed L1 adaptive controller to the design of adaptive flight control systems that explicitly address these metrics in the presence of control surface failures and dynamic changes under adverse flight conditions; (iii) development of a flight control system for implementation of the resulting algorithms onboard of small UAV; and (iv) conducting a comprehensive flight test program that demonstrates performance of the developed adaptive control algorithms in the presence of failures. As the initial milestone the paper concentrates on the adaptive flight system setup and initial efforts addressing the ability of a commercial off-the-shelf AP with and without adaptive augmentation to recover from control surface failures.
Full Text Available Automatic steering control is the key factor and essential condition in the realization of the automatic navigation control of agricultural vehicles. In order to get satisfactory steering control performance, an adaptive sliding mode control method based on a nonlinear integral sliding surface is proposed in this paper for agricultural vehicle steering control. First, the vehicle steering system is modeled as a second-order mathematic model; the system uncertainties and unmodeled dynamics as well as the external disturbances are regarded as the equivalent disturbances satisfying a certain boundary. Second, a transient process of the desired system response is constructed in each navigation control period. Based on the transient process, a nonlinear integral sliding surface is designed. Then the corresponding sliding mode control law is proposed to guarantee the fast response characteristics with no overshoot in the closed-loop steering control system. Meanwhile, the switching gain of sliding mode control is adaptively adjusted to alleviate the control input chattering by using the fuzzy control method. Finally, the effectiveness and the superiority of the proposed method are verified by a series of simulation and actual steering control experiments.
.... In particular, the extension of the method to adaptive echo cancellation was investigated. Progress was also made on adaptive algorithms for the rejection of periodic disturbances without measurement of the disturbance or of its...
Thommyppillai, M.; Evangelou, S.; Sharp, R. S.
Adaptive linear optimal preview control theory is applied to a simple but non-linear car model, with parameters chosen to make the rear axle saturate first in any quasi-steady manoeuvre. The tendency of such a car to spin above a critical speed, which is a function of its running state, causes control to be especially difficult when operating near to the limit of the rear-axle force system. As in previous work, trim states and optimal gains are computed off-line for a given speed and a full range of lateral accelerations. Gain-scheduling with interpolation over trims and gain sets is used to keep the control appropriate to the running conditions, as they change. Simulations of manoeuvres are used to test and demonstrate the system capability. It is shown that utilising the rear-axle lateral-slip ratio as the scheduling variable, in the case of this rear-heavy car, gives excellent tracking, even when the tyres are run close to full saturation. It is implied by this and previous work that the general case can be treated effectively by monitoring both front- and rear-axle slips and scheduling on a worst-case basis.
Full Text Available Adaptive vehicle speed control is critical for developing Advanced Driver Assistance Systems (ADAS. Vehicle speed control considering variable road geometry has become a hotspot in ADAS research. In this paper, first, an exploration of intrinsic relationship between vehicle operation and road geometry is made. Secondly, a collaborative vehicle coupling model, a road geometry model, and an AVSC, which can respond to variable road geometry in advance, are developed. Then, based on H∞ control method and the minimum energy principle, a performance index is specified by a cost function for the proposed AVSC, which can explicitly consider variable road geometry in its optimization process. The proposed AVSC is designed by the Hamilton-Jacobi Inequality (HJI. Finally, simulations are carried out by combining the vehicle model with the road geometry model, in an aim of minimizing the performance index of the AVSC. Analyses of the simulation results indicate that the proposed AVSC can automatically and effectively regulate speed according to variable road geometry. It is believed that the proposed AVSC can be used to improve the economy, comfort, and safety effects of current ADAS.
Arena, Paolo; Patané, Luca; Distefano, Francesco; Bucolo, Sebastiano; Aiello, Orazio
In this work a biologically inspired network of spiking neurons is used for robot navigation control. The two tasks taken into account are obstacle avoidance and landmark-based navigation. The system learns the correlation among unconditioned stimuli (pre-wired sensors) and conditioned stimuli (high level sensors) through Spike Timing Dependent Plasticity (STDP). In order to improve the robot behaviours not only the synaptic weight but also the synaptic delay is subject to learning. Modulating the synaptic delay the robot is able to store the landmark position, like in a short time memory, and to use this information to smooth the turning actions prolonging the landmark effects also when it is no more visible. Simulations are carried out in a dynamic simulation environment and the robotic system considered is a cockroach-inspired hexapod robot. The locomotion signals are generated by a Central Pattern Generator and the spiking network is devoted to control the heading of the robot acting on the amplitude of the leg steps. Several scenarios have been proposed, for instance a T-shaped labyrinth, used in laboratory experiments with mice to demonstrate classical and operant conditioning, has been considered. Finally the proposed adaptive navigation control structure can be extended in a modular way to include other features detected by new sensors included in the correlation-based learning process.
Full Text Available Attitude dynamic model of unmanned aerial vehicles (UAVs is multi-input multioutput (MIMO, strong coupling, and nonlinear. Model uncertainties and external gust disturbances should be considered during designing the attitude control system for UAVs. In this paper, feedback linearization and model reference adaptive control (MRAC are integrated to design the attitude control system for a fixed wing UAV. First of all, the complicated attitude dynamic model is decoupled into three single-input single-output (SISO channels by input-output feedback linearization. Secondly, the reference models are determined, respectively, according to the performance indexes of each channel. Subsequently, the adaptive control law is obtained using MRAC theory. In order to demonstrate the performance of attitude control system, the adaptive control law and the proportional-integral-derivative (PID control law are, respectively, used in the coupling nonlinear simulation model. Simulation results indicate that the system performance indexes including maximum overshoot, settling time (2% error range, and rise time obtained by MRAC are better than those by PID. Moreover, MRAC system has stronger robustness with respect to the model uncertainties and gust disturbance.
This paper addresses the design of predictive networked controller with adaptation of a communication delay. The networked control system contains random delays from sensor to controller and from controller to actuator. The proposed predictive controller includes an adaptation loop which decreases the influence of communication delay on the control performance. Also, the predictive controller contains a filter which improves the robustness of the control system. The perfo...
2School of Computer Science and Engineering, Xinjiang University of Finance and Economics,. Urumchi 830012 ... The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is ... Keywords. Chaos control; adaptive control; adaptive identifier; fuzzy neural network; back-.
Huang, Jiangshuai; Wang, Qing-Guo
In this paper, we propose a decentralized adaptive control scheme for a class of interconnected strict-feedback nonlinear systems without a priori knowledge of subsystems' control directions. To address this problem, a novel Nussbaum-type function is proposed and a key theorem is drawn which involves quantifying the interconnections of multiple Nussbaum-type functions of the subsystems with different control directions in a single inequality. Global stability of the closed-loop system and asymptotic stabilization of subsystems' output are proved and a simulation example is given to illustrate the effectiveness of the proposed control scheme. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Nguyen, Nhan T.; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan
This paper presents a new adaptive control approach that involves a performance optimization objective. The problem is cast as a multi-objective optimal control. The control synthesis involves the design of a performance optimizing controller from a subset of control inputs. The effect of the performance optimizing controller is to introduce an uncertainty into the system that can degrade tracking of the reference model. An adaptive controller from the remaining control inputs is designed to reduce the effect of the uncertainty while maintaining a notion of performance optimization in the adaptive control system.
Full Text Available This paper focuses on high precision leveling control of an underwater heavy load platform, which is viewed as an underwater parallel robot on the basis of its work pattern. The kinematic of platform with deformation is analyzed and the dynamics model of joint space is established. An adaptive backstepping controller according to Lyapunov's function is proposed for leveling control of platform based on joint space. Furthermore, the “lowest point fixed angle error” leveling scheme called “chase” is chosen for leveling control of platform. The digital simulation and practical experiment of single joint space actuator are carried out, and the results show high precision servo control of joint space. On the basis of this, the platform leveling control simulation relies on the hardware-in-loop system. The results indicate that the proposed controller can effectively restrain the influence from system parameter uncertainties and external disturbance to realize high precision leveling control of the underwater platform.
Full Text Available The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning, a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.
Khooban, Mohammad-Hassan; Niknam, Taher; Blaabjerg, Frede
The goal of this study is to introduce a novel robust load frequency control (LFC) strategy for micro-grid(s) (MG(s)) in islanded mode operation. Admittedly, power generators in MG(s) cannot supply steady electric power output and sometimes cause unbalance between supply and demand. Battery energy...... micro-grid (MG) is introduced that can consider electric vehicles׳ (EV(s)) effect. Moreover, in this paper, a new combination of the General Type II Fuzzy Logic Sets (GT2FLS) and the Modified Harmony Search Algorithm (MHSA) technique is applied for adaptive tuning of proportional-integral (PI...... storage system (BESS) is one of the effective solutions to these problems. Due to the high cost of the BESS, a new idea of Vehicle-to-Grid (V2G) is that a battery of Electric-Vehicle (EV) can be applied as a tantamount large-scale BESS in MG(s). As a result, a new robust control strategy for an islanded...
Ahn, Dae Ryong; Yang, Ji Hyun; Lee, Sang Hun [Kookmin University, Seoul (Korea, Republic of)
Recent development in science and technology has enabled vehicles to be equipped with advanced autonomous functions. ADAS (Advanced Driver Assistance Systems) are examples of such advanced autonomous systems added. Advanced systems have several operational modes and it has been observed that drivers could be unaware of the mode they are in during vehicle operation, which can be a contributing factor of traffic accidents. In this study, possible mode confusions in a simulated environment when vehicles are equipped with an adaptive cruise control system were investigated. The mental model of the system was designed and verified using the formal analysis method. Then, the user interface was designed on the basis of those of the current cruise control systems. A set of human-in-loop experiments was conducted to observe possible mode confusions and redesign the user interface to reduce them. In conclusion, the clarity and transparency of the user interface was proved to be as important as the correctness and compactness of the mental model when reducing mode confusions.
Ahn, Dae Ryong; Yang, Ji Hyun; Lee, Sang Hun
Recent development in science and technology has enabled vehicles to be equipped with advanced autonomous functions. ADAS (Advanced Driver Assistance Systems) are examples of such advanced autonomous systems added. Advanced systems have several operational modes and it has been observed that drivers could be unaware of the mode they are in during vehicle operation, which can be a contributing factor of traffic accidents. In this study, possible mode confusions in a simulated environment when vehicles are equipped with an adaptive cruise control system were investigated. The mental model of the system was designed and verified using the formal analysis method. Then, the user interface was designed on the basis of those of the current cruise control systems. A set of human-in-loop experiments was conducted to observe possible mode confusions and redesign the user interface to reduce them. In conclusion, the clarity and transparency of the user interface was proved to be as important as the correctness and compactness of the mental model when reducing mode confusions
Modjaev, A. D.; Leonova, N. M.
Recent years, a new scientific branch connected with the activities in social sphere management developing intensively and it is called "Social Cybernetics". In the framework of this scientific branch, theory and methods of management of social sphere are formed. Considerable attention is paid to the management, directly in real time. However, the decision of such management tasks is largely constrained by the lack of or insufficiently deep study of the relevant sections of the theory and methods of management. The article discusses the use of cybernetic principles in solving problems of control in social systems. Applying to educational activities a model of composite interrelated objects representing the behaviour of students at various stages of educational process is introduced. Statistical processing of experimental data obtained during the actual learning process is being done. If you increase the number of features used, additionally taking into account the degree and nature of variability of levels of current progress of students during various types of studies, new properties of students' grouping are discovered. L-clusters were identified, reflecting the behaviour of learners with similar characteristics during lectures. It was established that the characteristics of the clusters contain information about the dynamics of learners' behaviour, allowing them to be used in additional lessons. The ways of solving the problem of adaptive control based on the identified dynamic characteristics of the learners are planned.
This paper presents the optimal control modification for linear uncertain plants. The Lyapunov analysis shows that the modification parameter has a limiting value depending on the nature of the uncertainty. The optimal control modification exhibits a linear asymptotic property that enables it to be analyzed in a linear time invariant framework for linear uncertain plants. The linear asymptotic property shows that the closed-loop plants in the limit possess a scaled input-output mapping. Using this property, we can derive an analytical closed-loop transfer function in the limit as the adaptive gain tends to infinity. The paper revisits the Rohrs counterexample problem that illustrates the nature of non-robustness of model-reference adaptive control in the presence of unmodeled dynamics. An analytical approach is developed to compute exactly the modification parameter for the optimal control modification that stabilizes the plant in the Rohrs counterexample. The linear asymptotic property is also used to address output feedback adaptive control for non-minimum phase plants with a relative degree 1.
Ramesh, A. V.; Utku, S.; Wada, B. K.
The basic theory is presented for the geometry, stiffness, and damping control of adaptive structures, with emphasis on adaptive truss structures. Necessary and sufficient conditions are given for stress-free geometry control in statically determinate and indeterminate adaptive discrete structures. Two criteria for selecting the controls are proposed, and their use in real-time control is illustrated by numerical simulation results. It is shown that the stiffness and damping control of adaptive truss structures for vibration suppression is possible by elongation and elongation rate dependent feedback forces from the active elements.
Amjad Jalil Humaidi
Full Text Available L1 adaptive controller has proven to provide fast adaptation with guaranteed transients in a large variety of systems. It is commonly used for controlling systems with uncertain time-varying unknown parameters. The effectiveness of L1 adaptive controller for position control of single axis has been examined and compared with Model Reference Adaptive Controller (MRAC. The Linear servo motor is one of the main constituting elements of the x-y table which is mostly used in automation application. It is characterized by time-varying friction and disturbance
R.. Parameter-Adaptive Control Algorithms - A Tutorial." Automatica. Vol 18. No. 5. 1982. pp 513-528. 12. Wittenmark. B.. and Karl J Astrom ...1.3.2 Adaptive Controls. Research into adaptive control was motivated in the 1950s by high performance aircraft requirements (10:471). Astrom stated...February 1985. N 0 *N 10. Astrom . K.J.. -Theory and Application of Adaptive Control- A Survey,- Automatica. Vol 19. NO. 5. 1983. pp 471-486. 11. Isermann
VanZwieten, Tannen; Zhu, J. Jim; Adami, Tony; Berry, Kyle; Grammar, Alex; Orr, Jeb S.; Best, Eric A.
Recently, a robust and practical adaptive control scheme for launch vehicles [  has been introduced. It augments a classical controller with a real-time loop-gain adaptation, and it is therefore called Adaptive Augmentation Control (AAC). The loop-gain will be increased from the nominal design when the tracking error between the (filtered) output and the (filtered) command trajectory is large; whereas it will be decreased when excitation of flex or sloshing modes are detected. There is a need to determine the range and rate of the loop-gain adaptation in order to retain (exponential) stability, which is critical in vehicle operation, and to develop some theoretically based heuristic tuning methods for the adaptive law gain parameters. The classical launch vehicle flight controller design technics are based on gain-scheduling, whereby the launch vehicle dynamics model is linearized at selected operating points along the nominal tracking command trajectory, and Linear Time-Invariant (LTI) controller design techniques are employed to ensure asymptotic stability of the tracking error dynamics, typically by meeting some prescribed Gain Margin (GM) and Phase Margin (PM) specifications. The controller gains at the design points are then scheduled, tuned and sometimes interpolated to achieve good performance and stability robustness under external disturbances (e.g. winds) and structural perturbations (e.g. vehicle modeling errors). While the GM does give a bound for loop-gain variation without losing stability, it is for constant dispersions of the loop-gain because the GM is based on frequency-domain analysis, which is applicable only for LTI systems. The real-time adaptive loop-gain variation of the AAC effectively renders the closed-loop system a time-varying system, for which it is well-known that the LTI system stability criterion is neither necessary nor sufficient when applying to a Linear Time-Varying (LTV) system in a frozen-time fashion. Therefore, a
Ye, Linqi; Zong, Qun; Tian, Bailing; Zhang, Xiuyun; Wang, Fang
In this paper, the nonminimum phase problem of a flexible hypersonic vehicle is investigated. The main challenge of nonminimum phase is the prevention of dynamic inversion methods to nonlinear control design. To solve this problem, we make research on the relationship between nonminimum phase and backstepping control, finding that a stable nonlinear controller can be obtained by changing the control loop on the basis of backstepping control. By extending the control loop to cover the internal dynamics in it, the internal states are directly controlled by the inputs and simultaneously serve as virtual control for the external states, making it possible to guarantee output tracking as well as internal stability. Then, based on the extended control loop, a simplified control-oriented model is developed to enable the applicability of adaptive backstepping method. It simplifies the design process and releases some limitations caused by direct use of the no simplified control-oriented model. Next, under proper assumptions, asymptotic stability is proved for constant commands, while bounded stability is proved for varying commands. The proposed method is compared with approximate backstepping control and dynamic surface control and is shown to have superior tracking accuracy as well as robustness from the simulation results. This paper may also provide a beneficial guidance for control design of other complex systems. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
. Dar es Salaam. Durban. Bloemfontein. Antananarivo. Cape Town. Ifrane ... program strategy. A number of CCAA-supported projects have relevance to other important adaptation-related themes such as disaster preparedness and climate.
Full Text Available Three conventional control constant instantaneous power control, sinusoidal current control, and synchronous reference frame techniques for extracting reference currents for shunt active power filters have been optimized using Fuzzy Logic control and Adaptive Tabu search Algorithm and their performances have been compared. Critical analysis of Comparison of the compensation ability of different control strategies based on THD and speed will be done, and suggestions will be given for the selection of technique to be used. The simulated results using MATLAB model are presented, and they will clearly prove the value of the proposed control method of aircraft shunt APF. The waveforms observed after the application of filter will be having the harmonics within the limits and the power quality will be improved.
This book presents a mechatronic approach to Active Noise Control (ANC). It describes the required elements of system theory, engineering acoustics, electroacoustics and adaptive signal processing in a comprehensive, consistent and systematic manner using a unified notation. Furthermore, it includes a design methodology for ANC-systems, explains its application and describes tools to be used for ANC-system design. From the research point of view, the book presents new approaches to sound source localization in weakly damped interiors. One is based on the inverse finite element method, the other is based on a sound intensity probe with an active free field. Furthermore, a prototype of an ANC-system able to reach the physical limits of local (feed-forward) ANC is described. This is one example for applied research in ANC-system design. Other examples are given for (i) local ANC in a semi-enclosed subspace of an aircraft cargo hold and (ii) for the combination of audio entertainment with ANC.
Bieniawski, Stefan; Wolpert, David H.
Product Distribution (PO) theory was recently developed as a broad framework for analyzing and optimizing distributed systems. Here we demonstrate its use for adaptive distributed control of Multi-Agent Systems (MASS), i.e., for distributed stochastic optimization using MAS s. First we review one motivation of PD theory, as the information-theoretic extension of conventional full-rationality game theory to the case of bounded rational agents. In this extension the equilibrium of the game is the optimizer of a Lagrangian of the (Probability dist&&on on the joint state of the agents. When the game in question is a team game with constraints, that equilibrium optimizes the expected value of the team game utility, subject to those constraints. One common way to find that equilibrium is to have each agent run a Reinforcement Learning (E) algorithm. PD theory reveals this to be a particular type of search algorithm for minimizing the Lagrangian. Typically that algorithm i s quite inefficient. A more principled alternative is to use a variant of Newton's method to minimize the Lagrangian. Here we compare this alternative to RL-based search in three sets of computer experiments. These are the N Queen s problem and bin-packing problem from the optimization literature, and the Bar problem from the distributed RL literature. Our results confirm that the PD-theory-based approach outperforms the RL-based scheme in all three domains.
Jin, Moonseob; Luder, Ryan; Sanchez, Lucas; Hart, Michael
By sensing and compensating wavefront aberration, adaptive optics (AO) systems have proven themselves crucial in large astronomical telescopes, retinal imaging, and holographic coherent imaging. Commercial AO systems for laboratory use are now available in the market. One such is the ThorLabs AO kit built around a Boston Micromachines deformable mirror. However, there are limitations in applying these systems to research and pedagogical projects since the software is written with limited flexibility. In this paper, we describe a MATLAB-based software suite to interface with the ThorLabs AO kit by using the MATLAB Engine API and Visual Studio. The software is designed to offer complete access to the wavefront sensor data, through the various levels of processing, to the command signals to the deformable mirror and fast steering mirror. In this way, through a MATLAB GUI, an operator can experiment with every aspect of the AO system's functioning. This is particularly valuable for tests of new control algorithms as well as to support student engagement in an academic environment. We plan to make the code freely available to the community.
Henselmans, Inge; Sanderman, Robbert; Helgeson, Vicki S; de Vries, J; Smink, Ans; Ranchor, Adelita V
OBJECTIVES: Although cognitive adaptation theory suggests that personal control acts as a stress buffer when facing adversity, maladaptive outcomes might occur when control is disconfirmed. The moderating effect of disappointing news on the adaptiveness of personal control over cure in women with
Henselmans, Inge; Sanderman, Robbert; Helgeson, Vicki S.; de Vries, Jakob; Smink, Ans; Ranchor, Adelita V.
OBJECTIVES: Although cognitive adaptation theory suggests that personal control acts as a stress buffer when facing adversity, maladaptive outcomes might occur when control is disconfirmed. The moderating effect of disappointing news on the adaptiveness of personal control over cure in women with
Full Text Available A non-identifier-based adaptive PI controller is designed using a gradient approach to improve the performance of a control system when device aging and environmental factors degrade the efficiency of the process. The design approach is based on the model reference adaptive control technique. The controller drives the difference (error between the process response and desired model output to zero asymptotically at a rate constrained by the desired characteristics of the model. The tuning rules are designed and justified for a non-linear process with dominant dynamics of second order. The advantage of this method for tracking and regulation compared to adaptive MIT control was validated in real time by conducting experiments on a laboratory air flow control system using the dSPACE interface in the SIMULINK software. The experimental results show that the process with adaptive PI controller has better dynamic performance and robustness than that with traditional adaptive MIT controller.
Full Text Available In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.
Dadras, Sara; Momeni, Hamid Reza
An adaptive sliding mode control (ASMC) technique is introduced in this paper for a chaotic dynamical system (Genesio-Tesi system). Using the sliding mode control technique, a sliding surface is determined and the control law is established. An adaptive sliding mode control law is derived to make the states of the Genesio-Tesi system asymptotically track and regulate the desired state. The designed control scheme can control the uncertain chaotic behaviors to a desired state without oscillating very fast and guarantee the property of asymptotical stability. An illustrative simulation result is given to demonstrate the effectiveness of the proposed adaptive sliding mode control design.
Full Text Available In this paper, the flight formation control problem of a group of quadrotor unmanned aerial vehicles (UAVs with parametric uncertainties and external disturbances is studied. Unit-quaternions are used to represent the attitudes of the quadrotor UAVs. Separating the model into a translational subsystem and a rotational subsystem, an intermediary control input is introduced to track a desired velocity and extract desired orientations. Then considering the internal parametric uncertainties and external disturbances of the quadrotor UAVs, the priori-bounded intermediary adaptive control input is designed for velocity tracking and formation keeping, by which the bounded control thrust and the desired orientation can be extracted. Thereafter, an adaptive control torque input is designed for the rotational subsystem to track the desired orientation. With the proposed control scheme, the desired velocity is tracked and a desired formation shape is built up. Global stability of the closed-loop system is proven via Lyapunov-based stability analysis. Numerical simulation results are presented to illustrate the effectiveness of the proposed control scheme.
Rasmussen, Henrik; Vadstrup, P.; Børsting, H.
This paper presents an adaptive observer for extimating the rotor position and speed of a permanent magnet synchronous motors (PMSM). The observer compensates for voltage offsets and permanent magnet strength variations. The adaptation structure for estimating the strength of the permanent magnet...
Jelsma, Otto; van Merrienboer, Jeroen J.G.; van Merrienboer, J.J.G.; Bijlstra, Jim P.; Bijlstra, J.P.
This paper presents a detailed description of the ADAPT (Apply Delayed Automatization for Positive Transfer) design model. ADAPT is based upon production system models of learning and provides guidelines for developing instructional systems that offer transfer of leamed skills. The model suggests
Full Text Available This paper presents the implementation of an adaptive supervisory sliding fuzzy cerebellar model articulation controller (FCMAC in the speed sensorless vector control of an induction motor (IM drive system. The proposed adaptive supervisory sliding FCMAC comprised a supervisory controller, integral sliding surface, and an adaptive FCMAC. The integral sliding surface was employed to eliminate steady-state errors and enhance the responsiveness of the system. The adaptive FCMAC incorporated an FCMAC with a compensating controller to perform a desired control action. The proposed controller was derived using the Lyapunov approach, which guarantees learning-error convergence. The implementation of three intelligent control schemes—the adaptive supervisory sliding FCMAC, adaptive sliding FCMAC, and adaptive sliding CMAC—were experimentally investigated under various conditions in a realistic sensorless vector-controlled IM drive system. The root mean square error (RMSE was used as a performance index to evaluate the experimental results of each control scheme. The analysis results indicated that the proposed adaptive supervisory sliding FCMAC substantially improved the system performance compared with the other control schemes.
Bargatze, L. F.
Active Data Archive Product Tracking (ADAPT) is a collection of software routines that permits one to generate XML metadata files to describe and register data products in support of the NASA Heliophysics Virtual Observatory VxO effort. ADAPT is also a philosophy. The ADAPT concept is to use any and all available metadata associated with scientific data to produce XML metadata descriptions in a consistent, uniform, and organized fashion to provide blanket access to the full complement of data stored on a targeted data server. In this poster, we present an application of ADAPT to describe all of the data products that are stored by using the Common Data File (CDF) format served out by the CDAWEB and SPDF data servers hosted at the NASA Goddard Space Flight Center. These data servers are the primary repositories for NASA Heliophysics data. For this purpose, the ADAPT routines have been used to generate data resource descriptions by using an XML schema named Space Physics Archive, Search, and Extract (SPASE). SPASE is the designated standard for documenting Heliophysics data products, as adopted by the Heliophysics Data and Model Consortium. The set of SPASE XML resource descriptions produced by ADAPT includes high-level descriptions of numerical data products, display data products, or catalogs and also includes low-level "Granule" descriptions. A SPASE Granule is effectively a universal access metadata resource; a Granule associates an individual data file (e.g. a CDF file) with a "parent" high-level data resource description, assigns a resource identifier to the file, and lists the corresponding assess URL(s). The CDAWEB and SPDF file systems were queried to provide the input required by the ADAPT software to create an initial set of SPASE metadata resource descriptions. Then, the CDAWEB and SPDF data repositories were queried subsequently on a nightly basis and the CDF file lists were checked for any changes such as the occurrence of new, modified, or deleted
Chen, Hung Yi; Huang, Shiuh Jer
The twin-roll strip casting process is a steel-strip production method which combines continuous casting and hot rolling processes. The production line from molten liquid steel to the final steel-strip is shortened and the production cost is reduced significantly as compared to conventional continuous casting. The quality of strip casting process depends on many process parameters, such as molten steel level in the pool, solidification position, and roll gap. Their relationships are complex and the strip casting process has the properties of nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. In this paper, a model-free adaptive neural network controller is developed to overcome this problem. The proposed control strategy is based on a neural network structure combined with a sliding-mode control scheme. An adaptive rule is employed to on-line adjust the weights of radial basis functions by using the reaching condition of a specified sliding surface. This surface has the on-line learning ability to respond to the system's nonlinear and time-varying behaviors. Since this model-free controller has a simple control structure and small number of control parameters, it is easy to implement. Simulation results, based on a semi experimental system dynamic model and parameters, are executed to show the control performance of the proposed intelligent controller. In addition, the control performance is compared with that of a traditional Pid controller
Wen, John T.; Kreutz, Kenneth; Bayard, David S.
A class of joint-level control laws for all-revolute robot arms is introduced. The analysis is similar to the recently proposed energy Liapunov function approach except that the closed-loop potential function is shaped in accordance with the underlying joint space topology. By using energy Liapunov functions with the modified potential energy, a much simpler analysis can be used to show closed-loop global asymptotic stability and local exponential stability. When Coulomb and viscous friction and model parameter errors are present, a sliding-mode-like modification of the control law is proposed to add a robustness-enhancing outer loop. Adaptive control is also addressed within the same framework. A linear-in-the-parameters formulation is adopted, and globally asymptotically stable adaptive control laws are derived by replacing the model parameters in the nonadaptive control laws by their estimates.
Daniel A. Braun
Full Text Available When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e. online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning.
Braun, Daniel A.; Aertsen, Ad; Paz, Rony; Vaadia, Eilon; Rotter, Stefan; Mehring, Carsten
When faced with unpredictable environments, the human motor system has been shown to develop optimized adaptation strategies that allow for online adaptation during the control process. Such online adaptation is to be contrasted to slower over-trial learning that corresponds to a trial-by-trial update of the movement plan. Here we investigate the interplay of both processes, i.e., online adaptation and over-trial learning, in a visuomotor experiment performed by macaques. We show that simple non-adaptive control schemes fail to perform in this task, but that a previously suggested adaptive optimal feedback control model can explain the observed behavior. We also show that over-trial learning as seen in learning and aftereffect curves can be explained by learning in a radial basis function network. Our results suggest that both the process of over-trial learning and the process of online adaptation are crucial to understand visuomotor learning. PMID:21720526
The scope of this effort includes the theoretical development of a multi-input, multi-output (MIMO) Model Reference Control (MRC) algorithm, (i.e., model following control law), Model Reference Adaptive Control (MRAC) algorithm and the formulation of a nonlinear model of a typical electric power plant. Previous single-input, single-output MRAC algorithm designs have been generalized to MIMO MRAC designs using the MIMO MRC algorithm. This MRC algorithm, which has been developed using Command Generator Tracker methodologies, represents the steady state behavior (in the adaptive sense) of the MRAC algorithm. The MRC algorithm is a fundamental component in the MRAC design and stability analysis. An enhanced MRC algorithm, which has been developed for systems with more controls than regulated outputs, alleviates the MRC stability constraint of stable plant transmission zeroes. The nonlinear power plant model is based on the Cromby model with the addition of a governor valve management algorithm, turbine dynamics and turbine interactions with extraction flows. An application of the MRC algorithm to a linearization of this model demonstrates its applicability to power plant systems. In particular, the generated power changes at 7% per minute while throttle pressure and temperature, reheat temperature and drum level are held constant with a reasonable level of control. The enhanced algorithm reduces significantly control fluctuations without modifying the output response.
Han, S. H.
A new adaptive digital control scheme for the robotic manipulator is proposed in this paper. Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion for robotic manipulators. In the proposed scheme, adaptation laws are derived from the improved Lyapunov second stability analysis based on the adaptive feedforward and feedback controller and PI type time-varying control elements. The control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot. (author)
Li, Shi-ping; Yu, Yan; Jiao, Zhen-gang; Gu, Shu-sheng
Direct Torque Control technology is a high-performance communication control method, it uses the space voltage vector method, and then to the inverter switch state control, to obtain high torque dynamic performance. But none of the switching states is able to generate the exact voltage vector to produce the desired changes in torque and flux in most of the switching instances. This causes a high ripple in torque. To solve this problem, a fuzzy implementation of Direct Torque Control of Induction machine is presented here. Error of stator flux, error of motor electromagnetic torque and position of angle of flux are taken as fuzzy variables. In order to further solve nonlinear problem of variation parameters in direct torque control system, the paper proposes a fuzzy parameter PID adaptive control method which is suitable for the direct torque control of an asynchronous motor. The generation of its fuzzy control is obtained by analyzing and optimizing PID control step response and combining expert's experience. For this reason, it carries out fuzzy work to PID regulator of motor speed to achieve to regulate PID parameters. Therefore the control system gets swifter response velocity, stronger robustness and higher precision of velocity control. The computer simulated results verify the validity of this novel method.
Bai, Jianbo; Li, Yang; Chen, Jianhao
The paper proposes an adaptive control method for a water chiller unit in a HVAC system. Based on the minimum variance evaluation, the adaptive control method was used to realize better control of the water chiller unit. To verify the performance of the adaptive control method, the proposed method was compared with an a conventional PID controller, the simulation results showed that adaptive control method had superior control performance to that of the conventional PID controller.
Chang Weider; Yan Junjuh
A robust adaptive PID controller design motivated from the sliding mode control is proposed for a class of uncertain chaotic systems in this paper. Three PID control gains, K p , K i , and K d , are adjustable parameters and will be updated online with an adequate adaptation mechanism to minimize a previously designed sliding condition. By introducing a supervisory controller, the stability of the closed-loop PID control system under with the plant uncertainty and external disturbance can be guaranteed. Finally, a well-known Duffing-Holmes chaotic system is used as an illustrative to show the effectiveness of the proposed robust adaptive PID controller
Full Text Available Living creatures, like walking animals, have found fascinating solutions for the problem of locomotion control. Their movements show the impression of elegance including versatile, energy-efficient, and adaptable locomotion. During the last few decades, roboticists have tried to imitate such natural properties with artificial legged locomotion systems by using different approaches including machine learning algorithms, classical engineering control techniques, and biologically-inspired control mechanisms. However, their levels of performance are still far from the natural ones. By contrast, animal locomotion mechanisms seem to largely depend not only on central mechanisms (central pattern generators, CPGs and sensory feedback (afferent-based control but also on internal forward models (efference copies. They are used to a different degree in different animals. Generally, CPGs organize basic rhythmic motions which are shaped by sensory feedback while internal models are used for sensory prediction and state estimations. According to this concept, we present here adaptive neural locomotion control consisting of a CPG mechanism with neuromodulation and local leg control mechanisms based on sensory feedback and adaptive neural forward models with efference copies. This neural closed-loop controller enables a walking machine to perform a multitude of different walking patterns including insect-like leg movements and gaits as well as energy-efficient locomotion. In addition, the forward models allow the machine to autonomously adapt its locomotion to deal with a change of terrain, losing of ground contact during stance phase, stepping on or hitting an obstacle during swing phase, leg damage, and even to promote cockroach-like climbing behavior. Thus, the results presented here show that the employed embodied neural closed-loop system can be a powerful way for developing robust and adaptable machines.
Miley, G.H.; Park, G.T.; Kim, B.S.
Possible applications of an adaptive control method to a pressurized-water reactor nuclear power plant are investigated. The self-tuning technique with a reference model concept is employed. This control algorithm is developed by combining the self-tuning controller with the model reference adaptive control. This approach overcomes the difficulties in choosing the appropriate weighting polynomials in the cost function of the self-tuning control
Full Text Available Management of urban stormwater to mitigate Combined Sewer Overflows (CSOs is a priority for many cities; yet, few truly innovative approaches have been proposed to address the problem. Recent advances in information technology are now, however, providing cost-effective opportunities to achieve better performance of conventional stormwater infrastructure through a Continuous Monitoring and Adaptive Control (CMAC approach. The primary objective of this study was to demonstrate that a CMAC approach can be applied to a conventional rainwater harvesting system in New York City to improve performance by minimizing discharge to the combined sewer during rainfall events, reducing water use for irrigation of local vegetation, and optimizing vegetation health. To achieve this objective, a hydrologic and hydraulic model was developed for a planned and designed rainwater harvesting system to explore multiple potential scenarios prior to the system’s actual construction. Model results indicate that the CMAC rainwater harvesting system is expected to provide significant performance improvements over conventional rainwater harvesting systems. The CMAC system is expected to capture and retain 76.6% of roof runoff per year on average, as compared to just 14.8% and 41.3% for conventional moisture and timer based systems, respectively. Similarly, the CMAC system is expected to use 81.4% and 18.0% less harvested rainwater than conventional moisture and timer based irrigation approaches, respectively. The flexibility of the CMAC approach to meet competing objectives is promising for widespread implementation in New York City and other heavily urbanized areas challenged by stormwater management issues.
Wicks, Michael C
The major impact of the research reported herein is the development of an adaptive algorithm that specifically addresses the rejection of discretes in the cell under test competing with all targets...
Spaccapietra, S.; Rinderle, S.B.; Reichert, M.U.
For several reasons enterprises are frequently subject to organizational change. Respective adaptations may concern business processes, but also other components of an enterprise architecture. In particular, changes of organizational structures often become necessary. The information about
Full Text Available For the control of the liquid level of liquid ammonia in thermal power plant’s ammonia vaporization room, traditional PID controller parameter tuning is difficult to adapt to complex control systems, the setting of the traditional PID controller parameters is difficult to adapt to the complex control system. For the disadvantage of bad parameter setting, poor performance and so on the fuzzy adaptive PID control is proposed. Fuzzy adaptive PID control combines the advantages of traditional PID technology and fuzzy control. By using the fuzzy controller to intelligent control the object, the performance of the PID controller is further improved, and the control precision of the system is improved. The simulation results show that the fuzzy adaptive PID controller not only has the advantages of high accuracy of PID controller, but also has the characteristics of fast and strong adaptability of fuzzy controller. It realizes the optimization of PID parameters which are in the optimal state, and the maximum increase production efficiency, so that are more suitable for nonlinear dynamic system.
Nairobi, Kenya. 28 Adapting Fishing Policy to Climate Change with the Aid of Scientific and Endogenous Knowledge. Cap Verde, Gambia,. Guinea, Guinea Bissau,. Mauritania and Senegal. Environment and Development in the Third World. (ENDA-TM). Dakar, Senegal. 29 Integrating Indigenous Knowledge in Climate Risk ...
Full Text Available The problem of spacecraft attitude control is solved using an adaptive neuro-fuzzy inference system (ANFIS. An ANFIS produces a control signal for one of the three axes of a spacecraft’s body frame, so in total three ANFISs are constructed for 3-axis attitude control. The fuzzy inference system of the ANFIS is initialized using a subtractive clustering method. The ANFIS is trained by a hybrid learning algorithm using the data obtained from attitude control simulations using state-dependent Riccati equation controller. The training data set for each axis is composed of state errors for 3 axes (roll, pitch, and yaw and a control signal for one of the 3 axes. The stability region of the ANFIS controller is estimated numerically based on Lyapunov stability theory using a numerical method to calculate Jacobian matrix. To measure the performance of the ANFIS controller, root mean square error and correlation factor are used as performance indicators. The performance is tested on two ANFIS controllers trained in different conditions. The test results show that the performance indicators are proper in the sense that the ANFIS controller with the larger stability region provides better performance according to the performance indicators.
Varotto, S.F.; Farah, H.; Toledo, Tomer; van Arem, B.; Hoogendoorn, S.P.
Automated vehicles and driving assistance systems such as adaptive cruise control (ACC) are expected to reduce traffic congestion, accidents, and levels of emissions. Field operational tests have found that drivers may prefer to deactivate ACC in dense traffic flow conditions and before changing
Varotto, S.F.; Farah, H.; Toledo, T; van Arem, B.; Hoogendoorn, S.P.
Automated vehicles and driving assistance systems such as Adaptive Cruise Control (ACC) are expected to reduce traffic congestion, accidents and levels of emissions. Field Operational Tests have found that drivers may prefer to deactivate ACC in dense traffic flow conditions and before changing
Patre, Parag; Joshi, Suresh M.
Decentralized adaptive control is considered for systems consisting of multiple interconnected subsystems. It is assumed that each subsystem s parameters are uncertain and the interconnection parameters are not known. In addition, mismatch can exist between each subsystem and its reference model. A strictly decentralized adaptive control scheme is developed, wherein each subsystem has access only to its own state but has the knowledge of all reference model states. The mismatch is estimated online for each subsystem and the mismatch estimates are used to adaptively modify the corresponding reference models. The adaptive control scheme is extended to the case with actuator failures in addition to mismatch.
Full Text Available The present paper addresses an attitude tracking control problem of a ducted fan microaerial vehicle. The proposed indirect adaptive controller can greatly reduce tracking error in the initial stage of the adaptive learning process by using an error compensation strategy and can achieve good capability to eliminate the adverse effect of measurement noises on the convergence of adjustable parameters. Moreover, the learning rate adaptation strategy is proposed to further minimize the adverse effect of large learning rates on the convergence of adjustable parameters. The experimental tests have illustrated the effectiveness of the proposed adaptive controller.
Schuch, Stefanie; Koch, Iring
Conflict adaptation can be measured by the "congruency sequence effect", denoting the reduction of congruency effects after incongruent trials (where response conflict occurs) relative to congruent trials (without response conflict). Recently, it has been reported that conflict adaptation is larger in negative mood than in positive mood (van Steenbergen et al., Psychological Science 21:1629-1634, 2010). We conducted two experiments further investigating this important finding. Two different interference paradigms were applied to measure conflict adaptation: Experiment 1 was a Flanker task, Experiment 2 was a Stroop-like task. To get as pure a measure of conflict adaptation as possible, we minimized the influence of trial-to-trial priming effects by excluding all kinds of stimulus repetitions. Mood states were induced by presenting film clips with emotional content prior to the interference task. Three mood states were manipulated between subjects: amused, anxious, and sad. Across both interference paradigms, we consistently found conflict adaptation in negative, but not in positive mood. Taken together with van Steenbergen et al. (Psychological Science 21:1629-1634, 2010) findings, the results suggest that the negative-mood-triggered increase in conflict adaptation is a general phenomenon that occurs independently of the particular mood-induction procedure and interference paradigm involved.
Iqbal, Muhammad; Rehan, Muhammad; Hong, Keum-Shik; Khaliq, Abdul; Saeed-ur-Rehman
This paper addresses the design of adaptive feedback controllers for two problems (namely, stabilization and synchronization) of chaotic systems with unknown parameters by considering input saturation constraints. A novel generalized sector condition is developed to deal with the saturation nonlinearities for synthesizing the nonlinear and the adaptive controllers for the stabilization and synchronization control objectives. By application of the proposed sector condition and rigorous regional stability analysis, control and adaptation laws are formulated to guarantee local stabilization of a nonlinear system under actuator saturation. Further, simple control and adaptation laws are developed to synchronize two chaotic systems under uncertain parameters and input saturation nonlinearity. Numerical simulation results for Rössler and FitzHugh–Nagumo models are provided to demonstrate the effectiveness of the proposed adaptive stabilization and synchronization control methodologies
Liu Yue; Zhou Shuo
To improve the dynamic performance of permanent magnet synchronous motor(PMSM) drive system, a adaptive nonsingular terminal sliding model control((NTSMC) strategy was proposed. The proposed control strategy presents an adaptive variable-rated exponential reaching law which the L1 norm of state variables is introduced. Exponential and constant approach speed can adaptively adjust according to the state variables’ distance to the equilibrium position.The proposed scheme can shorten the reachin...
Full Text Available Driving characteristics of human drivers, such as driving safety, comfort, handiness, and efficiency, which are interrelated and contradictory, are synthetically considered to maintain a safe inter-vehicle distance in this article. For the multi-objective coordination control problem, the safety, handiness, comfort, and efficiency indicators are established via driving states and manipulated variable. Furthermore, a multi-performance indicator coordination mechanism is proposed via the invariant set and quadratic boundedness theory. A headway control algorithm for adaptive cruise control is established under the dynamic output feedback control framework. Finally, feasibility and effectiveness of the proposed algorithm are verified via closed-loop simulations under the following, cut-out, and cut-in typical operating conditions.
Milan Manojle Šunjevarić
Full Text Available In this paper, an overview of the algorithms for access control in mobile wireless networks is presented. A review of adaptive control methods of accepting a call in WCDMA networks is discussed, based on the overview of the algorithms used for this purpose, and their comparison. Appropriate comments and conculsions in comparison with the basic characteristics of these algorithms are given. The OVSF codes are explained as well as how the allocation method influences the capacity and probability of blocking.. Introduction We are witnessing a steady increase in the number of demands placed upon modern wireless networks. New applications and an increasing number of users as well as user activities growth in recent years reinforce the need for an efficient use of the spectrum and its proper distribution among different applications and classes of services. Besides humans, the last few years saw different computers, machines, applications, and, in the future, many other devices, RFID applications, and finally networked objects, as a new kind of wireless networks "users". Because of the exceptional rise in the number of users, the demands placed upon modern wireless networks are becoming larger, and spectrum management plays an important role. For these reasons, choosing an appropriate call admission control algorithm is of great importance. Multiple access and resource management in wireless networks Radio resource management of mobile networks is a set of algorithms to manage the use of radio resources with the aim is to maximize the total capacity of wireless systems with equal distribution of resources to users. Management of radio resources in cellular networks is usually located in the base station controller, the base station and the mobile terminal, and is based on decisions made on appropriate measurement and feedback. It is often defined as the maximum volume of traffic load that the system can provide for some of the requirements for the
Crespo, Luis G.; Matsutani, Megumi; Annaswamy, Anuradha M.
This paper presents the "Adaptive Control Technology for Safe Flight (ACTS)" architecture, which consists of a non-adaptive controller that provides satisfactory performance under nominal flying conditions, and an adaptive controller that provides robustness under off nominal ones. The design and implementation procedures of both controllers are presented. The aim of these procedures, which encompass both theoretical and practical considerations, is to develop a controller suitable for flight. The ACTS architecture is applied to the Generic Transport Model developed by NASA-Langley Research Center. The GTM is a dynamically scaled test model of a transport aircraft for which a flight-test article and a high-fidelity simulation are available. The nominal controller at the core of the ACTS architecture has a multivariable LQR-PI structure while the adaptive one has a direct, model reference structure. The main control surfaces as well as the throttles are used as control inputs. The inclusion of the latter alleviates the pilot s workload by eliminating the need for cancelling the pitch coupling generated by changes in thrust. Furthermore, the independent usage of the throttles by the adaptive controller enables their use for attitude control. Advantages and potential drawbacks of adaptation are demonstrated by performing high fidelity simulations of a flight-validated controller and of its adaptive augmentation.
Couraud, M.; Cattaert, D.; Paclet, F.; Oudeyer, P. Y.; de Rugy, A.
Objective. To compensate for a limb lost in an amputation, myoelectric prostheses use surface electromyography (EMG) from the remaining muscles to control the prosthesis. Despite considerable progress, myoelectric controls remain markedly different from the way we normally control movements, and require intense user adaptation. To overcome this, our goal is to explore concurrent machine co-adaptation techniques that are developed in the field of brain-machine interface, and that are beginning to be used in myoelectric controls. Approach. We combined a simplified myoelectric control with a perturbation for which human adaptation is well characterized and modeled, in order to explore co-adaptation settings in a principled manner. Results. First, we reproduced results obtained in a classical visuomotor rotation paradigm in our simplified myoelectric context, where we rotate the muscle pulling vectors used to reconstruct wrist force from EMG. Then, a model of human adaptation in response to directional error was used to simulate various co-adaptation settings, where perturbations and machine co-adaptation are both applied on muscle pulling vectors. These simulations established that a relatively low gain of machine co-adaptation that minimizes final errors generates slow and incomplete adaptation, while higher gains increase adaptation rate but also errors by amplifying noise. After experimental verification on real subjects, we tested a variable gain that cumulates the advantages of both, and implemented it with directionally tuned neurons similar to those used to model human adaptation. This enables machine co-adaptation to locally improve myoelectric control, and to absorb more challenging perturbations. Significance. The simplified context used here enabled to explore co-adaptation settings in both simulations and experiments, and to raise important considerations such as the need for a variable gain encoded locally. The benefits and limits of extending this
Ben Youssef, C.; Roux, G.; Dahhou, B. [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France)]|[Institut National des Sciences Appliquees (INSA), 31 - Toulouse (France)
This paper deals with a multivariable adaptive predictive control scheme via on-line estimation of the specific reaction rates of a multistage bioreactor. Good simulation results demonstrate significant robustness of the estimator and efficiency of the adaptive control law. (authors) 11 refs.
Li, Mingshen; Gui, Yonghao; Guerrero, Josep M.
This paper presents an adaptive synchronization for current-controlled grid-connected inverter based on a time domain virtual oscillator controller (VOC). Inspired by the phenomenon of dynamics of adaptive oscillator under the perturbation effect. Firstly, the fast learning rule of the oscillator...
Berkhoff, Arthur P.
Model errors in adaptive controllers for reduction of broadband noise and vibrations may lead to unstable systems or increased error signals. Previous work has shown that the addition of a low-authority controller that increases damping in the system may lead to improved performance of an adaptive,
Zhou, Qi; Shi, Peng; Tian, Yang; Wang, Mingyu
In this paper, an approximation-based adaptive tracking control approach is proposed for a class of multiinput multioutput nonlinear systems. Based on the method of neural network, a novel adaptive controller is designed via backstepping design process. Furthermore, by introducing Nussbaum function, the issue of unknown control directions is handled. In the backstepping design process, the dynamic surface control technique is employed to avoid differentiating certain nonlinear functions repeatedly. Moreover, in order to reduce the number of adaptation laws, we do not use the neural networks to directly approximate the unknown nonlinear functions but the desired control signals. Finally, we provide two examples to illustrate the effectiveness of the proposed approach.
An adaptive augmenting control algorithm for the Space Launch System has been developed at the Marshall Space Flight Center as part of the launch vehicles baseline flight control system. A prototype version of the SLS flight control software was hosted on a piloted aircraft at the Armstrong Flight Research Center to demonstrate the adaptive controller on a full-scale realistic application in a relevant flight environment. Concerns regarding adverse interactions between the adaptive controller and a proposed manual steering mode were investigated by giving the pilot trajectory deviation cues and pitch rate command authority.
Ashraf Ahmed Fahmy
Full Text Available This paper presents an adaptive functional-based Neuro-fuzzy-PID incremental (NFPID controller structure that can be tuned either offline or online according to required controller performance. First, differential membership functions are used to represent the fuzzy membership functions of the input-output space of the three term controller. Second, controller rules are generated based on the discrete proportional, derivative, and integral function for the fuzzy space. Finally, a fully differentiable fuzzy neural network is constructed to represent the developed controller for either offline or online controller parameter adaptation. Two different adaptation methods are used for controller tuning, offline method based on controller transient performance cost function optimization using Bees Algorithm, and online method based on tracking error minimization using back-propagation with momentum algorithm. The proposed control system was tested to show the validity of the controller structure over a fixed PID controller gains to control SCARA type robot arm.
National Aeronautics and Space Administration — The proposed research focuses on the development of a new adaptive control methodology for active control of wing aerodynamic shape to effect distributed aerodynamic...
Full Text Available In this paper, robust adaptive tracking control is proposed for the underwater robot in the presence of parametric uncertainties and unknown external disturbances. Backstepping control of the system dynamics is introduced to develop full state feedback tracking control. Using parameter adaptation, backstepping control and variable structure based techniques, the robust adaptive tracking control is presented for underwater robots to handle the uncertainties, saturation and dead-zone characteristics of actuators. Actuator nonlinearities comprising of dead-zone and saturation are explicitly considered in the tracking control design. Under the proposed tracking control, semi-global uniform boundedness of the closed-loop signals is guaranteed via Lyapunov analysis. Numerical simulation results are given to illustrate the effectiveness of the proposed robust adaptive tracking control.
Crespo, Luis G.; Matsutani, Megumi; Annaswamy, Anuradha M.
This paper focuses on the analysis and tuning of a controller based on the Adaptive Control Technology for Safe Flight (ACTS) architecture. The ACTS architecture consists of a nominal, non-adaptive controller that provides satisfactory performance under nominal flying conditions, and an adaptive controller that provides robustness under off-nominal ones. A framework unifying control verification and gain tuning is used to make the controller s ability to satisfy the closed-loop requirements more robust to uncertainty. In this paper we tune the gains of both controllers using this approach. Some advantages and drawbacks of adaptation are identified by performing a global robustness assessment of both the adaptive controller and its non-adaptive counterpart. The analyses used to determine these characteristics are based on evaluating the degradation in closed-loop performance resulting from uncertainties having increasing levels of severity. The specific adverse conditions considered can be grouped into three categories: aerodynamic uncertainties, structural damage, and actuator failures. These failures include partial and total loss of control effectiveness, locked-in-place control surface deflections, and engine out conditions. The requirements considered are the peak structural loading, the ability of the controller to track pilot commands, the ability of the controller to keep the aircraft s state within the reliable flight envelope, and the handling/riding qualities of the aircraft. The nominal controller resulting from these tuning strategies was successfully validated using the NASA GTM Flight Test Vehicle.
Full Text Available Walking animals produce adaptive interlimb coordination during locomotion in accordance with their situation. Interlimb coordination is generated through the dynamic interactions of the neural system, the musculoskeletal system, and the environment, although the underlying mechanisms remain unclear. Recently, investigations of the adaptation mechanisms of living beings have attracted attention, and bio-inspired control systems based on neurophysiological findings regarding sensorimotor interactions are being developed for legged robots. In this review, we introduce adaptive interlimb coordination for legged robots induced by various factors (locomotion speed, environmental situation, body properties, and task. In addition, we show characteristic properties of adaptive interlimb coordination, such as gait hysteresis and different time-scale adaptations. We also discuss the underlying mechanisms and control strategies to achieve adaptive interlimb coordination and the design principle for the control system of legged robots.
A non-identifier-based adaptive PI controller is designed using a gradient approach to improve the performance of a control system when device aging and environmental factors degrade the efficiency of the process. The design approach is based on the model reference adaptive control technique. The controller drives the difference (error) between the process response and desired model output to zero asymptotically at a rate constrained by the desired characteristics of the model. The tuning r...
Gonzalo Garcia; Shahriar Keshmiri
The main purpose of this paper is to develop an onboard adaptive and robust flight control system that improves control, stability, and survivability of a small unmanned aerial system in off-nominal or out-of-envelope conditions. The aerodynamics of aircraft associated with hazardous and adverse onboard conditions is inherently nonlinear and unsteady. The presented flight control system improves functionalities required to adapt the flight control in the presence of aircraft model uncertainti...
Gao, Hui; Song, Yongduan; Wen, Changyun
In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.In this paper, an adaptive controller is developed for a class of multi-input and multioutput nonlinear systems with neural networks (NNs) used as a modeling tool. It is shown that all the signals in the closed-loop system with the proposed adaptive neural controller are globally uniformly bounded for any external input in . In our control design, the upper bound of the NN modeling error and the gains of external disturbance are characterized by unknown upper bounds, which is more rational to establish the stability in the adaptive NN control. Filter-based modification terms are used in the update laws of unknown parameters to improve the transient performance. Finally, fault-tolerant control is developed to accommodate actuator failure. An illustrative example applying the adaptive controller to control a rigid robot arm shows the validation of the proposed controller.
Full Text Available Divisive normalization in primary visual cortex has been linked to adaptation to natural image statistics in accordance to Barlow's redundancy reduction hypothesis. Using recent advances in natural image modeling, we show that the previously studied static model of divisive normalization is rather inefficient in reducing local contrast correlations, but that a simple temporal contrast adaptation mechanism of the half-saturation constant can substantially increase its efficiency. Our findings reveal the experimentally observed temporal dynamics of divisive normalization to be critical for redundancy reduction.
Full Text Available A novel control approach is proposed for trajectory tracking of a wheeled mobile robot with unknown wheels’ slipping. The longitudinal and lateral slipping are considered and processed as three time-varying parameters. The adaptive unscented Kalman filter is then designed to estimate the slipping parameters online, an adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the adaptive unscented Kalman filter context. Considering the practical physical constrains, a stable tracking control law for this robot system is proposed by the backstepping method. Asymptotic stability is guaranteed by Lyapunov stability theory. Control gains are determined online by applying pole placement method. Simulation and real experiment results show the effectiveness and robustness of the proposed control method.
In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identiﬁer (FNNI) is the principal controller. The FNNI is used for ...
Han, Jaeyoung; Jung, Mooncheong; Yu, Sangseok; Yi, Sun
In this study, a model reference adaptive controller is developed to regulate the outlet air flow rate of centrifugal compressor for automotive supercharger. The centrifugal compressor is developed using the analytical based method to predict the transient behavior of operating and the designed model is validated with experimental data to confirm the system accuracy. The model reference adaptive control structure consists of a compressor model and a MRAC(model reference adaptive control) mechanism. The feedback control do not robust with variation of system parameter but the applied adaptive control is robust even if the system parameter is changed. As a result, the MRAC was regulated to reference air flow rate. Also MRAC was found to be more robust control compared with the feedback control even if the system parameter is changed.
Long, Lijun; Zhao, Jun
In this paper, the problem of adaptive neural output-feedback control is addressed for a class of multi-input multioutput (MIMO) switched uncertain nonlinear systems with unknown control gains. Neural networks (NNs) are used to approximate unknown nonlinear functions. In order to avoid the conservativeness caused by adoption of a common observer for all subsystems, an MIMO NN switched observer is designed to estimate unmeasurable states. A new switched observer-based adaptive neural control technique for the problem studied is then provided by exploiting the classical average dwell time (ADT) method and the backstepping method and the Nussbaum gain technique. It effectively handles the obstacle about the coexistence of multiple Nussbaum-type function terms, and improves the classical ADT method, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied. It is shown that the technique proposed is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop system under a class of switching signals with ADT, and the tracking errors converge to a small neighborhood of the origin. The effectiveness of the approach proposed is illustrated by its application to a two inverted pendulum system.
Wang Dan; Zhang Shuocheng; Jing Lan; Zhang Wei; Ma Yunhai
The authors introduced two kinds of fiber adapters that apply to the engineering HIRFL-CSR. Including design of two adapters, operational principle, and hardware construction, field of application. How to control equipment which have the standard RS232 or RS485 interface at long distance by two adapters. Replace the RS485 bus with the fiber and the 485-Fiber Adapter, solved the problem of communication disturb. The requirements of control in the national great science engineering HIRFL-CSR are fulfilled. (authors)
Yan, J.-J.; Lin, J.-S.; Liao, T.-L.
This study addresses the adaptive synchronization of a modified Chua's circuit system with both unknown system parameters and the nonlinearity in the control input. An adaptive switching surface is newly adopted such that it becomes easy to ensure the stability of the error dynamics in the sliding mode. Based on this adaptive switching surface, an adaptive sliding mode controller (ASMC) is derived to guarantee the occurrence of the sliding motion, even when the system is undergoing input nonlinearity. This method can also be easily extended to a general class of Chua's circuits. An illustrative example is given to show the applicability of the proposed ASMC design
Jain, Aakanksha; Pasare, Chandrashekhar
Activation of cells in the adaptive immune system is a highly orchestrated process dictated by multiples cues from the innate immune system. Although the fundamental principles of innate control of adaptive immunity are well established, it is not fully understood how innate cells integrate qualitative pathogenic information to generate tailored protective adaptive immune responses. In this review, we discuss complexities involved in the innate control of adaptive immunity that extend beyond TCR engagement, costimulation, and priming cytokine production but are critical for the generation of protective T cell immunity. Copyright © 2017 by The American Association of Immunologists, Inc.
Full Text Available There is a belief that complexity and chaos are essential for adaptability. But life deals with complexity every moment, without the chaos that engineers fear so, by invoking goal-directed behaviour. Goals can be programmed. That is why living organisms give us hope to achieve adaptability in robots. In this paper a method for the description of a goal-directed, or programmed, behaviour, interacting with uncertainty of environment, is described. We suggest reducing the structural (goals, intentions and stochastic components (probability to realise the goal of individual behaviour to random variables with nominal values to apply probabilistic approach. This allowed us to use a Normalized Entropy Index to detect the system state by estimating the contribution of each agent to the group behaviour. The number of possible group states is 27. We argue that adaptation has a limited number of possible paths between these 27 states. Paths and states can be programmed so that after adjustment to any particular case of task and conditions, adaptability will never involve chaos. We suggest the application of the model to operation of robots or other devices in remote and/or dangerous places.
Spanias, J. A.; Simon, A. M.; Finucane, S. B.; Perreault, E. J.; Hargrove, L. J.
Objective. The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee’s neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach. We present a powered lower limb prosthesis that was configured to acquire the user’s neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user’s intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user’s model of neural data during ambulation. Main results. Our proposed algorithm accurately and consistently identified the user’s intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm—with a 6.66% [3.16%] relative decrease in error rate. Significance. This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user’s intent with low error rates.
L M WANG
Aug 16, 2017 ... Abstract. A novel model-free adaptive sliding mode strategy is proposed for a generalized projective synchronization (GPS) between two entirely unknown fractional-order chaotic systems subject to the external disturbances. To solve the difficulties from the little knowledge about the master–slave system ...
Hamanaka, M.; Yamada, K.
Online character recognition which can adapt to handwriting quality is proposed. In character recognition, it is difficult to recognize both clearly and roughly written characters accurately. For Japanese characters, the number of strokes is often slightly varied when characters are written
Sampson, J. N.
The Lasso shrinkage procedure achieved its popularity, in part, by its tendency to shrink estimated coefficients to zero, and its ability to serve as a variable selection procedure. Using data-adaptive weights, the adaptive Lasso modified the original procedure to increase the penalty terms for those variables estimated to be less important by ordinary least squares. Although this modified procedure attained the oracle properties, the resulting models tend to include a large number of "false positives" in practice. Here, we adapt the concept of local false discovery rates (lFDRs) so that it applies to the sequence, λn, of smoothing parameters for the adaptive Lasso. We define the lFDR for a given λn to be the probability that the variable added to the model by decreasing λn to λn-δ is not associated with the outcome, where δ is a small value. We derive the relationship between the lFDR and λn, show lFDR =1 for traditional smoothing parameters, and show how to select λn so as to achieve a desired lFDR. We compare the smoothing parameters chosen to achieve a specified lFDR and those chosen to achieve the oracle properties, as well as their resulting estimates for model coefficients, with both simulation and an example from a genetic study of prostate specific antigen.
Full Text Available This paper investigates the distributed consensus-based robust adaptive formation control for nonholonomic mobile robots with partially known dynamics. Firstly, multirobot formation control problem has been converted into a state consensus problem. Secondly, the practical control strategies, which incorporate the distributed kinematic controllers and the robust adaptive torque controllers, are designed for solving the formation control problem. Thirdly, the specified reference trajectory for the geometric centroid of the formation is assumed as the trajectory of a virtual leader, whose information is available to only a subset of the followers. Finally, numerical results are provided to illustrate the effectiveness of the proposed control approaches.
Theisen, Lukas Roy Svane; Galeazzi, Roberto; Blanke, Mogens
This paper treats L1 adaptive hovering control of an unmanned surface vehicle in a station-keeping mode where a region of zero control authority and under-actuation are main challenges. Low-speed and reversing dynamics are identied from full scale sea trials, and parameter uncertainty is estimated....... With signicant parameter variation, an L1 adaptive controller is employed for heading control. The L1 family of controllers allows for several topologies and an architecture is suggested that suits heading control of a vessel, the requirements of which dier from that of previous L1 literature. The control design...
Full Text Available In this paper, an adaptive fuzzy sliding mode controller is proposed to control a two-stage single-phase photovoltaic (PV grid-connected inverter. Two key technologies are discussed in the presented PV system. An incremental conductance method with adaptive step is adopted to track the maximum power point (MPP by controlling the duty cycle of the controllable power switch of the boost DC-DC converter. An adaptive fuzzy sliding mode controller with an integral sliding surface is developed for the grid-connected inverter where a fuzzy system is used to approach the upper bound of the system nonlinearities. The proposed strategy has strong robustness for the sliding mode control can be designed independently and disturbances can be adaptively compensated. Simulation results of a PV grid-connected system verify the effectiveness of the proposed method, demonstrating the satisfactory robustness and performance.
Fei, Juntao; Zhu, Yunkai
In this paper, an adaptive fuzzy sliding mode controller is proposed to control a two-stage single-phase photovoltaic (PV) grid-connected inverter. Two key technologies are discussed in the presented PV system. An incremental conductance method with adaptive step is adopted to track the maximum power point (MPP) by controlling the duty cycle of the controllable power switch of the boost DC-DC converter. An adaptive fuzzy sliding mode controller with an integral sliding surface is developed for the grid-connected inverter where a fuzzy system is used to approach the upper bound of the system nonlinearities. The proposed strategy has strong robustness for the sliding mode control can be designed independently and disturbances can be adaptively compensated. Simulation results of a PV grid-connected system verify the effectiveness of the proposed method, demonstrating the satisfactory robustness and performance.
Frost, Susan A.; Balas, Mark J.; Wright, Alan D.
Adaptive control techniques are well suited to nonlinear applications, such as wind turbines, which are difficult to accurately model and which have effects from poorly known operating environments. The turbulent and unpredictable conditions in which wind turbines operate create many challenges for their operation. In this paper, we design an adaptive collective pitch controller for a high-fidelity simulation of a utility scale, variable-speed horizontal axis wind turbine. The objective of the adaptive pitch controller in Region 3 is to regulate generator speed and reject step disturbances. The control objective is accomplished by collectively pitching the turbine blades. We use an extension of the Direct Model Reference Adaptive Control (DMRAC) approach to track a reference point and to reject persistent disturbances. The turbine simulation models the Controls Advanced Research Turbine (CART) of the National Renewable Energy Laboratory in Golden, Colorado. The CART is a utility-scale wind turbine which has a well-developed and extensively verified simulator. The adaptive collective pitch controller for Region 3 was compared in simulations with a bas celliansesical Proportional Integrator (PI) collective pitch controller. In the simulations, the adaptive pitch controller showed improved speed regulation in Region 3 when compared with the baseline PI pitch controller and it demonstrated robustness to modeling errors.
Poursamad, Amir; Markazi, Amir H.D.
This paper describes an adaptive fuzzy sliding-mode control algorithm for controlling unknown or uncertain, multi-input multi-output (MIMO), possibly chaotic, dynamical systems. The control approach encompasses a fuzzy system and a robust controller. The fuzzy system is designed to mimic an ideal sliding-mode controller, and the robust controller compensates the difference between the fuzzy controller and the ideal one. The parameters of the fuzzy system, as well as the uncertainty bound of the robust controller, are tuned adaptively. The adaptive laws are derived in the Lyapunov sense to guarantee the asymptotic stability and tracking of the controlled system. The effectiveness of the proposed method is shown by applying it to some well-known chaotic systems.
Full Text Available An adaptive global sliding mode control (AGSMC using RBF neural network (RBFNN is proposed for the system identification and tracking control of micro-electro-mechanical system (MEMS gyroscope. Firstly, a new kind of adaptive identification method based on the global sliding mode controller is designed to update and estimate angular velocity and other system parameters of MEMS gyroscope online. Moreover, the output of adaptive neural network control is used to adjust the switch gain of sliding mode control dynamically to approach the upper bound of unknown disturbances. In this way, the switch item of sliding mode control can be converted to the output of continuous neural network which can weaken the chattering in the sliding mode control in contrast to the conventional fixed gain sliding mode control. Simulation results show that the designed control system can get satisfactory tracking performance and effective estimation of unknown parameters of MEMS gyroscope.
Full Text Available The attitude tracking problem of spacecraft in the presence of unknown disturbance is investigated. By using the adaptive control technique and the Lyapunov stability theory, a chattering-free adaptive sliding mode control law is proposed for the attitude tracking problem of spacecraft with unknown disturbance. Simulation results are employed to demonstrate the effectiveness of the proposed control design technique in this paper.
Raither, Wolfram; Heymanns, Matthias; Bergamini, Andrea; Ermanni, Paolo
A novel semi-passive morphing airfoil concept based on variable bending-twist coupling induced by adaptive shear center location and torsional stiffness is presented. Numerical parametric studies and upscaling show that the concept relying on smart materials permits effective twist control while offering the potential of being lightweight and energy efficient. By means of an experimental characterization of an adaptive beam and a scaled adaptive wing structure, effectiveness and producibility of the structural concept are demonstrated.
Raither, Wolfram; Heymanns, Matthias; Ermanni, Paolo; Bergamini, Andrea
A novel semi-passive morphing airfoil concept based on variable bending–twist coupling induced by adaptive shear center location and torsional stiffness is presented. Numerical parametric studies and upscaling show that the concept relying on smart materials permits effective twist control while offering the potential of being lightweight and energy efficient. By means of an experimental characterization of an adaptive beam and a scaled adaptive wing structure, effectiveness and producibility of the structural concept are demonstrated. (paper)
there is a corresponding need for control components to work reliably in harsh environments and at higher temperatures. The high temperature actuator control...suppliers. The high temperature actuator control module was identified as the critical component for a distributed engine control system, which
Salehpoor, Karim; Shahinpoor, Mohsen; Mojarrad, Mehran
Reversible change in optical properties of ionic polymeric gels, 2-acrylamido-2-methylpropane sulfonic acid (PAMPS) and polyacrylic acid plus sodium acrylate cross-linked with bisacrylamide (PAAM), under the effect of an electric field is reported. The shape of a cylindrical piece of the gel, with flat top and bottom surfaces, changed when affected by an electric field. The top surface became curved and the sense of the curvature (whether concave or convex) depended on the polarity of the applied electric field. The curvature of the surface changed from concave to convex and vice versa by changing the polarity of the electric field. By the use of an optical apparatus, focusing capability of the curved surface was verified and the focal length of the deformed gel was measured. The effect of the intensity of the applied electric field on the surface curvature and thus, on the focal length of the gel are tested. Different mechanisms are discussed; either of them or their combination may explain the surface deformation and curvature. Practical difficulties in the test procedure and the future potential of the electrically adaptive and active optical lenses are also discussed. These adaptive lenses may be considered as smart adaptive lenses for contact lens or other optical applications requiring focal point undulation.
Yin, Xiuxing; Pan, Li
A direct adaptive robust tracking control is proposed for trajectory tracking of 6 DOF industrial robot in the presence of parametric uncertainties, external disturbances and uncertain nonlinearities. The controller is designed based on the dynamic characteristics in the working space of the end-effector of the 6 DOF robot. The controller includes robust control term and model compensation term that is developed directly based on the input reference or desired motion trajectory. A projection-type parametric adaptation law is also designed to compensate for parametric estimation errors for the adaptive robust control. The feasibility and effectiveness of the proposed direct adaptive robust control law and the associated projection-type parametric adaptation law have been comparatively evaluated based on two 6 DOF industrial robots. The test results demonstrate that the proposed control can be employed to better maintain the desired trajectory tracking even in the presence of large parametric uncertainties and external disturbances as compared with PD controller and nonlinear controller. The parametric estimates also eventually converge to the real values along with the convergence of tracking errors, which further validate the effectiveness of the proposed parametric adaption law. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
This paper presents a comparison between an Interval Type-2 Fuzzy Gain Adaptive IP (IT2FGAIP) controller and a conventional IP controller used for speed control with a direct stator flux orientation control of a doubly fed induction motor. In particular, the introduction part of the paper presents a Direct Stator Flux Orientation ...
Naus, G.J.L.; Ploeg, J.; Molengraft, M.J.G. van de; Steinbuch, M.
This paper presents the synthesis, the implementation and the performance-based tuning of an Adaptive Cruise Control (ACC) Stop-&-Go (S&G) design. A Model Predictive Control (MPC) framework is adopted to design the controller. Performance of the controller is evaluated, distinguishing between
Artificial Intelligence (AI)-based controllers such as fuzzy logic PD, fuzzy logic PD + I, self-tuning fuzzy logic PID (STF-PID) controller and fuzzy logic-based sliding mode adaptive controller (FLSMAC) are designed for stable autopilot system and are compared with conventional PI controller. The target of throttle, speed and ...
Zhai, Ding; An, Liwei; Ye, Dan; Zhang, Qingling
This paper investigates the adaptive static output feedback (SOF) control problem for continuous-time linear systems with stochastic sensor failures. A multi-Markovian variable is introduced to denote the failure scaling factors for each sensor. Different from the existing results, the failure parameters are stochastically jumping and their bounds of are unknown. An adaptive reliable SOF control method is proposed, where the controller parameters are updated automatically to compensate for the failure effects on systems. A novel cubic absolute Lyapunov function is proposed to design adaptive laws only using measured output with sensor failures, and the convergence of jumping adaptive parameters is ensured by a trajectory initialization approach. The resultant designs can guarantee the asymptotic stability with an adaptive performance of closed-loop systems regardless of sensor failures. Finally, the simulation results on the "Raptor-90" helicopter are given to show the effectiveness of the proposed approaches.
Full Text Available This paper investigates the problem of synchronization for two different stochastic chaotic systems with unknown parameters and uncertain terms. The main work of this paper consists of the following aspects. Firstly, based on the Lyapunov theory in stochastic differential equations and the theory of sliding mode control, we propose a simple sliding surface and discuss the occurrence of the sliding motion. Secondly, we design an adaptive sliding mode controller to realize the asymptotical synchronization in mean squares. Thirdly, we design an adaptive sliding mode controller to realize the almost surely synchronization. Finally, the designed adaptive sliding mode controllers are used to achieve synchronization between two pairs of different stochastic chaos systems (Lorenz-Chen and Chen-Lu in the presence of the uncertainties and unknown parameters. Numerical simulations are given to demonstrate the robustness and efficiency of the proposed robust adaptive sliding mode controller.
Chen, Ziting; Li, Zhijun; Chen, C L Philip
An adaptive neural control strategy for multiple input multiple output nonlinear systems with various constraints is presented in this paper. To deal with the nonsymmetric input nonlinearity and the constrained states, the proposed adaptive neural control is combined with the backstepping method, radial basis function neural network, barrier Lyapunov function (BLF), and disturbance observer. By ensuring the boundedness of the BLF of the closed-loop system, it is demonstrated that the output tracking is achieved with all states remaining in the constraint sets and the general assumption on nonsingularity of unknown control coefficient matrices has been eliminated. The constructed adaptive neural control has been rigorously proved that it can guarantee the semiglobally uniformly ultimate boundedness of all signals in the closed-loop system. Finally, the simulation studies on a 2-DOF robotic manipulator system indicate that the designed adaptive control is effective.
Sawai, Shujiro; Matsuda, Seiji
Canard based controller using an adaptive notch filter is proposed to control the attitude of launch vehicles including the ISAS's sounding rocket `S-520'. As the characteristics of launch vehicles are time variant in nature, conventional time invariant controller is not suitable for this purpose. Here, adaptive notch filter is proposed to treat the time variant nature. This adaptive filter acts to null out the structural bending mode, which often causes the instability of the attitude controller. The proposed adaptation law requires only limited calculation cost. It means that it is easy to install to the real flight system. The hardware module which aims to control the attitude of the sounding rocket `S-520' is designed and verified not only by the numerical simulations, but also by the hardware tests.
Gutierrez Zea, Luis Benigno
In this thesis, an architecture for the adaptive mode transition control of unmanned aerial vehicles (UAV) is presented. The proposed architecture consists of three levels: the highest level is occupied by mission planning routines where information about way points the vehicle must follow is processed. The middle level uses a trajectory generation component to coordinate the task execution and provides set points for low-level stabilizing controllers. The adaptive mode transitioning control algorithm resides at the lowest level of the hierarchy consisting of a mode transitioning controller and the accompanying adaptation mechanism. The mode transition controller is composed of a mode transition manager, a set of local controllers, a set of active control models, a set point filter, a state filter, an automatic trimming mechanism and a dynamic compensation filter. Local controllers operate in local modes and active control models operate in transitions between two local modes. The mode transition manager determines the actual mode of operation of the vehicle based on a set of mode membership functions and activates a local controller or an active control model accordingly. The adaptation mechanism uses an indirect adaptive control methodology to adapt the active control models. For this purpose, a set of plant models based on fuzzy neural networks is trained based on input/output information from the vehicle and used to compute sensitivity matrices providing the linearized models required by the adaptation algorithms. The effectiveness of the approach is verified through software-in-the-loop simulations, hardware-in-the-loop simulations and flight testing.
Makihara, Kanjuro; Kuroishi, Chikako; Fukunaga, Hisao
We propose a novel fuzzy-based method of adaptive multimodal vibration suppression with limited structural data. The adaptive control consists of fuzzy inference and a semi-active switching approach. We demonstrate it to be applicable to multimodal vibration suppression for vibrating structures, where a single piezoelectric actuator suppresses two modal vibrations simultaneously. Our fuzzy-based semi-active control requires only the structural information of natural frequencies for real-time adaptive feedback, whereas common adaptive controls require highly precise structural models or complete equations of motion. We conduct experiments in semi-active vibration suppression using the proposed fuzzy-based control, and compare the suppression performance of our fuzzy-based approach with conventional controls. The experiments indicate that the proposed fuzzy-based control demonstrates good adaptability when experiencing sudden changes in disturbance excitation, and also demonstrates high suppression performance. The fuzzy-based control can adapt to a wide range of disturbance conditions, both within and outside the range of vibration excitations assumed when the controller is designed. (paper)
National Aeronautics and Space Administration — SSCI proposes to develop and test a framework referred to as the ADVANCE (Algorithm Design and Validation for Adaptive Nonlinear Control Enhancement), within which...
Van Katwijk, R.T.
The objective of this thesis is to create a distributed, multi-agent, approach to traffic control. This PhD thesis' focus is on the control of a network instrumented by traffic signals.A thorough literature study has been performed, reviewing the current state of the art in traffic signal control.
Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.
Energy storage systems (ESSs) are desired and widely applied for power smoothing especially in systems with renewable generation and pulsed loads. High-pass-filter (HPF) is commonly applied in those applications in which the HPF extracts the high frequency fluctuating power and uses...... that as the power reference for ESS. The cut-off frequency, as the critical parameter, actually decides the power/energy compensated by ESS. Practically the state-of-charge (SoC) of the ESS has to be limited for safety and life-cycle considerations. In this paper an adaptive cut-off frequency design is proposed...
Full Text Available This paper investigates the problem of finite-time tracking control for nonholonomic mechanical systems with affine constraints. The control scheme is provided by flexibly incorporating terminal sliding-mode control with the method of relay switching control and related adaptive technique. The proposed relay switching controller ensures that the output tracking error converges to zero in a finite time. As an application, a boat on a running river is given to show the effectiveness of the control scheme.
Mizumoto, Ikuro; Nakamura, Takuto; Kumon, Makoto; Takagi, Taro
The paper deals with an adaptive control system design problem for a four rotor helicopter or quadrotor. A simple adaptive control design scheme with a parallel feedforward compensator (PFC) in the internal loop of the considered quadrotor will be proposed based on the backstepping strategy. As is well known, the backstepping control strategy is one of the advanced control strategy for nonlinear systems. However, the control algorithm will become complex if the system has higher order relative degrees. We will show that one can skip some design steps of the backstepping method by introducing a PFC in the inner loop of the considered quadrotor, so that the structure of the obtained controller will be simplified and a high gain based adaptive feedback control system will be designed. The effectiveness of the proposed method will be confirmed through numerical simulations.
Mizumoto, Ikuro; Nakamura, Takuto; Kumon, Makoto; Takagi, Taro [Dept. of Mechanical Systems Engineering, Kumamoto University 2-39-1 Kurokami, Kumamoto, 860-8555 (Japan)
The paper deals with an adaptive control system design problem for a four rotor helicopter or quadrotor. A simple adaptive control design scheme with a parallel feedforward compensator (PFC) in the internal loop of the considered quadrotor will be proposed based on the backstepping strategy. As is well known, the backstepping control strategy is one of the advanced control strategy for nonlinear systems. However, the control algorithm will become complex if the system has higher order relative degrees. We will show that one can skip some design steps of the backstepping method by introducing a PFC in the inner loop of the considered quadrotor, so that the structure of the obtained controller will be simplified and a high gain based adaptive feedback control system will be designed. The effectiveness of the proposed method will be confirmed through numerical simulations.
Yao, Wei; Fang, Jiakun; Zhao, Ping
the characteristics of the conventional PID, but adjust the parameters of PID controller online using identified Jacobian information from RBFNN. Hence, it has strong adaptability to the variation of the system operating condition. The effectiveness of the proposed controller is tested on a two-machine five-bus power......In this paper, a nonlinear adaptive damping controller based on radial basis function neural network (RBFNN), which can infinitely approximate to nonlinear system, is proposed for thyristor controlled series capacitor (TCSC). The proposed TCSC adaptive damping controller can not only have...... system and a four-machine two-area power system under different operating conditions in comparison with the lead-lag damping controller tuned by evolutionary algorithm (EA). Simulation results show that the proposed damping controller achieves good robust performance for damping the low frequency...
Zeng, Songwei; Hu, Haigen; Xu, Lihong; Li, Guanghui
This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production.
Zeng, Songwei; Hu, Haigen; Xu, Lihong; Li, Guanghui
This paper presents a hybrid control strategy, combining Radial Basis Function (RBF) network with conventional proportional, integral, and derivative (PID) controllers, for the greenhouse climate control. A model of nonlinear conservation laws of enthalpy and matter between numerous system variables affecting the greenhouse climate is formulated. RBF network is used to tune and identify all PID gain parameters online and adaptively. The presented Neuro-PID control scheme is validated through simulations of set-point tracking and disturbance rejection. We compare the proposed adaptive online tuning method with the offline tuning scheme that employs Genetic Algorithm (GA) to search the optimal gain parameters. The results show that the proposed strategy has good adaptability, strong robustness and real-time performance while achieving satisfactory control performance for the complex and nonlinear greenhouse climate control system, and it may provide a valuable reference to formulate environmental control strategies for actual application in greenhouse production. PMID:22778587
Hackl, Christoph M
This book introduces non-identifier-based adaptive control (with and without internal model) and its application to the current, speed and position control of mechatronic systems such as electrical synchronous machines, wind turbine systems, industrial servo systems, and rigid-link, revolute-joint robots. In mechatronics, there is often only rough knowledge of the system. Due to parameter uncertainties, nonlinearities and unknown disturbances, model-based control strategies can reach their performance or stability limits without iterative controller design and performance evaluation, or system identification and parameter estimation. The non-identifier-based adaptive control presented is an alternative that neither identifies the system nor estimates its parameters but ensures stability. The adaptive controllers are easy to implement, compensate for disturbances and are inherently robust to parameter uncertainties and nonlinearities. For controller implementation only structural system knowledge (like relativ...
Full Text Available Abstract An adaptive radial basis function (RBF neural network control system for three-phase active power filter (APF is proposed to eliminate harmonics. Compensation current is generated to track command current so as to eliminate the harmonic current of non-linear load and improve the quality of the power system. The asymptotical stability of the APF system can be guaranteed with the proposed adaptive neural network strategy. The parameters of the neural network can be adaptively updated to achieve the desired tracking task. The simulation results demonstrate good performance, for example showing small current tracking error, reduced total harmonic distortion (THD, improved accuracy and strong robustness in the presence of parameters variation and nonlinear load. It is shown that the adaptive RBF neural network control system for three-phase APF gives better control than hysteresis control.
Guo, R; Vincent, U E; Idowu, B A
In this paper, a simple adaptive control is proposed for the synchronization of chaotic dynamics of resistive-capacitive-inductive-shunted Josephson junctions (RCLSJ). The synchronization problem is investigated based on a drive-response system configuration consisting of two identical RCLSJ with and without identical system parameters. In addition, the synchronization when the system parameters are unknown is considered based on adaptive parameter control estimation. Sufficient conditions for global asymptotic synchronization are given and numerical simulations are employed to demonstrate the efficiency of the adaptive control scheme. In the presence of noise, we also show that the synchronization is robust and discuss the implication of our adaptive control technique in rapid single flux quantum (RSFQ) devices.
Dirkx, Kim; Kester, Liesbeth; Kirschner, Paul A.
Dirkx, K. J. H., Kester, L., & Kirschner, P. A. (2010, 25 February). Optimizing adaptive learning through testing, diagnostic reflection and learner-controlled information selection. Presentation at the Learning & Cognition meeting, Heerlen, The Netherlands: Open University of the Netherlands.
McIntyre, M. L; Dixon, W. E; Dawson, D. M; Xian, B
.... Motivated by task objectives that are more effectively described by on-line, state-dependent trajectories, two adaptive tracking controllers are developed in this paper that accommodate on-line path planning objective...
the original reference model may not be appropriate. Under this circumstance, an adaptive reference model which can also provide satisfactory performance is designed. Simulations of a flight control example are given to illustrate the effectiveness of the proposed scheme.
Andersen, Karsten Holm; Nymand, Morten
This paper presents an adaptive slope compensation method for peak current mode control of digital controlled DC-DC converters, which controls the quality factor of the complex conjugated poles at half the switching frequency. Using quality factor control enables optimization of the dynamic...... performance and stability of current mode control. The presented method adapt to DC-DC converter operating conditions by estimating the rising and falling inductor current slopes, to apply a current slope compensation value to obtain a constant quality factor. The experimental results verifies the theoretical...
This paper studies the problem of controlling a parabolic solar collectors, which consists of forcing the outlet oil temperature to track a set reference despite possible environmental disturbances. An approximate model is proposed to simplify the controller design. The presented controller is an indirect adaptive law designed on the fuzzy model with soft-sensing of the solar irradiance intensity. The proposed approximate model allows the achievement of a simple low dimensional set of nonlinear ordinary differential equations that reproduces the dynamical behavior of the system taking into account its infinite dimension. Stability of the closed loop system is ensured by resorting to Lyapunov Control functions for an indirect adaptive controller.
Zhou, Keliang; Yang, Yongheng; Blaabjerg, Frede
Repetitive control offers an accurate current control scheme for grid-tied converters to feed high quality sinusoidal current into the grid. However, with grid frequency being treated as a constant value, conventional repetitive controller fail to produce high quality feeding current...... in the presence of practical time-varying grid frequency. This paper explores frequency adaptive repetitive control strategy for grid-interfaced converters, which employs fractional delay filter to adapt to the change of grid frequency. Case studies with experimental results of three-phase grid......-connected converter systems are provided to verify the proposed controller....
Prunescu, Remus Mihail; Blanke, Mogens; Sin, Gürkan
for pH level regulation: one is a classical PI controller; the other an L1 adaptive output feedback controller. Model-based feed-forward terms are added to the controllers to enhance their performances. A new tuning method of the L1 adaptive controller is also proposed. Further, a new performance...... function is formulated and tailored to this type of processes and is used to monitor the performances of the process in closed loop. The L1 design is found to outperform the PI controller in all tests....
Dragicevic, Tomislav; Guerrero, Josep M.; Vasquez, Juan Carlos
of batteries and loads to form an autonomous dc Microgrid (MG). To overcome the control challenge associated with coordination of multiple batteries within one stand-alone MG, a double-layer hierarchical control strategy was proposed; 1) The unit-level primary control layer was established by an adaptive...... tracking (MPPT) for renewable energy sources (RESs), with which a smooth on-line overlap was designed; 2) the supervisory control layer was designed to use the low bandwidth communication interface between the central controller and sources in order to collect data needed for adaptive calculation...
Park, Gee Yong; Yoon, Ji Sup; Hong, Dong Hee; Jeong, Jae Hoo
In this paper, the robust control scheme with the improved control performance within the boundary layer is proposed. In the control scheme, the robust controller based on the traditional variable structure control method is modified to have the adaptation within the boundary layer. From this controller, the width of the boundary layer where the robust control input is smoothened out can be given by an appropriate value. But the improve control performance within the boundary layer can be achieved without the so-called control chattering because the role of adaptive control is to compensate for the uncovered portions of the robust control occurred from the continuous approximation within the boundary layer. Simulation tests for circular navigation of an underwater wall-ranging robot developed for inspection of wall surfaces in the research reactor, TRIGA MARK III, confirm the performance improvement
Full Text Available This paper considers the containment control problem for uncertain nonlinear multiagent systems under directed graphs. The followers are governed by nonlinear systems with unknown dynamics while the multiple leaders are neighbors of a subset of the followers. Fuzzy logic systems (FLSs are used to identify the unknown dynamics and a distributed state feedback containment control protocol is proposed. This result is extended to the output feedback case, where observers are designed to estimate the unmeasurable states. Then, an output feedback containment control scheme is presented. The developed state feedback and output feedback containment controllers guarantee that the states of all followers converge to the convex hull spanned by the dynamic leaders. Based on Lyapunov stability theory, it is proved that the containment control errors are uniformly ultimately bounded (UUB. An example is provided to show the effectiveness of the proposed control method.
The Franklin Institute, Vol. 327, No. 5, 1990, pp. 785-804. Garg, D., and Young, R .: Coordinated Control of Cooperating Robotic Manipulators... Shanidze Z., and Rondeli, E.: Global Stability of Solutions of Nonlinear Control Systems, International Journal of Systems Science, Vol. 20, No. 10...October 1989, pp. 1909-1924. Garg, D., Shanidze , Z. and Rondeli, E.: On the Global Stability of the Solutions of Nonlinear Control Systems, Proceedings of
Linse, Dennis J.; Stengel, Robert F.
A system identification model that combines generalized-spline function approximation with a nonlinear control system is described. The complete control system contains three main elements: a nonlinear-inverse-dynamic control law that depends on a comprehensive model of the plant, a state estimator whose outputs drive the control law, and a function approximation scheme that models the system dynamics. The system-identification task, which combines an extended Kalman filter with a function approximator modeled as an artificial neural network, is considered. The results of an application of the identification techniques to a nonlinear transport aircraft model are presented.
Minxiu Yan; Liping Fan
Fuel cell is a device that converts the chemical energy from a fuel into electricity through a chemical reaction with oxygen or another oxidizing agent. The paper describes a mathematical model of proton exchange membrane fuel cells by analyzing the working mechanism of the proton exchange membrane fuel cell. Furthermore, an adaptive fuzzy sliding mode controller is designed for the constant power output of PEMFC system. Simulation results prove that adaptive fuzzy sliding mode control has be...
Gai, Wendong; Wang, Honglun; Zhang, Jing; Li, Yuxia
An adaptive neural network dynamic inversion with prescribed performance method is proposed for aircraft flight control. The aircraft nonlinear attitude angle model is analyzed. And we propose a new attitude angle controller design method based on prescribed performance which describes the convergence rate and overshoot of the tracking error. Then the model error is compensated by the adaptive neural network. Subsequently, the system stability is analyzed in detail. Finally, the proposed meth...
Wu, Zhonghua; Lu, Jingchao; Rajput, Jahanzeb; Shi, Jingping; Ma, Wen
This paper presents an adaptive neural control for the longitudinal dynamics of a morphing aircraft. Based on the functional decomposition, it is reasonable to decompose the longitudinal dynamics into velocity and altitude subsystems. As for the velocity subsystem, the adaptive control is proposed via dynamic inversion method using neural network. To deal with input constraints, the additional compensation system is employed to help engine recover from input saturation rapidly. The highlight ...
Ghamati, Mina; Balochian, Saeed
In this paper two adaptive sliding mode controls for synchronizing the state trajectories of the Genesio–Tesi system with unknown parameters and external disturbance are proposed. A switching surface is introduced and based on this switching surface, two adaptive sliding mode control schemes are presented to guarantee the occurrence of the sliding motion. The stability and robustness of the two proposed schemes are proved using Lyapunov stability theory. The effectiveness of our introduced schemes is provided by numerical simulations
A. S. Yushenko
Full Text Available The current steady-rising interest in using the unmanned multi-rotor aerial vehicles (UMAV designed to solve a wide range of tasks is, mainly, due to their simple design and high weight-carrying capacity as compared to classical helicopter options. Unfortunately, to solve a problem of multi-copter control is complicated because of essential nonlinearity and environmental perturbations. The most widely spread PID controllers and linear-quadratic regulators do not quite well cope with this task. The need arises for the prompt adjustment of PID controller coefficients in the course of operation or their complete re-tuning in cases of changing parameters of the control object.One of the control methods under changing conditions is the use of the sliding mode. This technology enables us to reach the stabilization and proper operation of the controlled system even under accidental external exposures and when there is a lack of the reasonably accurate mathematical model of the control object. The sliding principle is to ensure the system motion in the immediate vicinity of the sliding surface in the phase space. On the other hand, the sliding mode has some essential disadvantages. The most significant one is the high-frequency jitter of the system near the sliding surface. The sliding mode also implies the complete knowledge of the system dynamics. Various methods have been proposed to eliminate these drawbacks. For example, A.G. Aissaoui’s, H. Abid’s and M. Abid’s paper describes the application of fuzzy logic to control a drive and in Lon-Chen Hung’s and Hung-Yuan Chung’s paper an artificial neural network is used for the manipulator control.This paper presents a method of the quad-copter control with the aid of a neural network controller. This method enables us to control the system without a priori information on parameters of the dynamic model of the controlled object. The main neural network is a MIMO (“Multiple Input Multiple
Greene, George W; Banquy, Xavier; Lee, Dong Woog; Lowrey, Daniel D; Yu, Jing; Israelachvili, Jacob N
Articular cartilage is a highly efficacious water-based tribological system that is optimized to provide low friction and wear protection at both low and high loads (pressures) and sliding velocities that must last over a lifetime. Although many different lubrication mechanisms have been proposed, it is becoming increasingly apparent that the tribological performance of cartilage cannot be attributed to a single mechanism acting alone but on the synergistic action of multiple "modes" of lubrication that are adapted to provide optimum lubrication as the normal loads, shear stresses, and rates change. Hyaluronic acid (HA) is abundant in cartilage and synovial fluid and widely thought to play a principal role in joint lubrication although this role remains unclear. HA is also known to complex readily with the glycoprotein lubricin (LUB) to form a cross-linked network that has also been shown to be critical to the wear prevention mechanism of joints. Friction experiments on porcine cartilage using the surface forces apparatus, and enzymatic digestion, reveal an "adaptive" role for an HA-LUB complex whereby, under compression, nominally free HA diffusing out of the cartilage becomes mechanically, i.e., physically, trapped at the interface by the increasingly constricted collagen pore network. The mechanically trapped HA-LUB complex now acts as an effective (chemically bound) "boundary lubricant"--reducing the friction force slightly but, more importantly, eliminating wear damage to the rubbing/shearing surfaces. This paper focuses on the contribution of HA in cartilage lubrication; however, the system as a whole requires both HA and LUB to function optimally under all conditions.
Large-scale power cuts in both North America and Europe emphasised the need to maintain an adequate supply of high-quality electricity. This book offers information on the relatively low-cost of doing so using self-regulating control mechanisms. It is of interest to the practising power/control engineer and to academics needing industrial inputs.
Yang, Yongheng; Zhou, Keliang; Wang, Huai
SHC scheme consists of multiple parallel recursive (nk±m)-order (k = 0, 1, 2, . . ., and m ≤ n/2) harmonic control modules with independent control gains, which can be optimally weighted in accordance with the harmonic distribution. The hybrid SHC thus offers an optimal trade-off among cost...
This thesis concerns speed control of current vector controlled induction motor drives (CVC drives). The CVC drive is an existing prototype drive developed by Danfoss A/S, Transmission Division. Practical tests have revealed that the open loop dynamical properties of the CVC drive are highly...
Guo, J.; Lin, Z.; Cao, M.; Yan, G.
The study investigates the leader-follower formation control problem, for which the objective is to control a group of robots such that they move as a rigid formation with a prescribed constant velocity. It is assumed in the study that there are two leader robots, who are the only robots in the
As soon as the collaboration between the SPIRAL project and the Control Group has been defined, the first implementation of the SPIRAL control system started following various directions. Both the global hardware and software architectures has been specified and some practical works have been undertaken such as the Ethernet network installation or the first SPIRAL oriented software design and coding. (authors)
Y. J. Zhang
Full Text Available This paper studies the adaptive fuzzy control problem of the molten steel level for a class of twin roll strip casting systems. Based on fuzzy logic systems (FLSs and the mean value theorem, a novel adaptive tracking controller with parameter updated laws is effectively designed. It is proved that all the closed-loop signals are uniformly bounded and the system tracking errors can asymptotically converge to zero by using the Lyapunov stability analysis. Simulation results of semi-experimental system dynamic model and parameters are provided to demonstrate the validity of the proposed adaptive fuzzy design approach.
Wang, Xia; Zhao, Jun
A switched adaptive controller is designed for robot manipulators with friction and changing loads. The nonlinear friction is depicted by a nonlinear friction model, and a switched nonlinear system is used to model the parameter jump caused by load change. Hyperstability theory is used in the designing procedure, which provides more options for adaptive laws than Lyapunov theory. In the presence of friction and changing loads, asymptotic tracking is achieved under arbitrary switching, which is not able to accomplish by a non-switched adaptive controller. The proposed method is validated by a simulation of a 2 degree of freedom manipulator.
Full Text Available Adaptive synchronization control is proposed for a new complex dynamical network model with nonidentical nodes and nonderivative and derivative couplings. The distributed adaptive learning laws of periodically time-varying and constant parameters and distributed adaptive control are designed. The new method which can obtain the synchronization error of closed-loop complex network system is asymptotic convergence in the sense of square error norm. What is more, the coupling matrix is not assumed to be symmetric or irreducible. Finally, a simulation example shows the feasibility and effectiveness of the approach.
Hopkins, David James [Livermore, CA
A control system and method for actively reducing vibration in a spindle housing caused by unbalance forces on a rotating spindle, by measuring the force-induced spindle-housing motion, determining control signals based on synchronous demodulation, and provide compensation for the measured displacement to cancel or otherwise reduce or attenuate the vibration. In particular, the synchronous demodulation technique is performed to recover a measured spindle housing displacement signal related only to the rotation of a machine tool spindle, and consequently rejects measured displacement not related to spindle motion or synchronous to a cycle of revolution. Furthermore, the controller actuates at least one voice-coil (VC) motor, to cancel the original force-induced motion, and adapts the magnitude of voice coil signal until this measured displacement signal is brought to a null. In order to adjust the signal to a null, it must have the correct phase relative to the spindle angle. The feedback phase signal is used to adjust a common (to both outputs) commutation offset register (offset relative to spindle encoder angle) to force the feedback phase signal output to a null. Once both of these feedback signals are null, the system is compensating properly for the spindle-induced motion.
Van Oort, E.R.; Sonneveldt, L.; Chu, Q.P.; Mulder, J.A.
A new adaptive nonlinear flight controller is designed for a high fidelity, six degrees of freedom F-16 model for the entire flight envelope. The design is based on a modular approach which separates the design of the control law and the online identifier. The control law design is based on
El Fadil, H.; Giri, F.; Guerrero, Josep M.
This paper deals with the problem of controlling energy generation systems including fuel cells (FCs) and interleaved boost power converters. The proposed nonlinear adaptive controller is designed using sliding mode control (SMC) technique based on the system nonlinear model. The latter accounts...
Full Text Available The method of stability estimation of adaptive control system with signal adjustment based on Lyapunov’s direct method that allows to take into account the nonstationarity of the basic system and non-linearity in the form of limitation on control action restriction as well as error control is stated.
Full Text Available This paper proposes a novel intelligent control scheme using type-2 fuzzy neural network (type-2 FNN system. The control scheme is developed using a type-2 FNN controller and an adaptive compensator. The type-2 FNN combines the type-2 fuzzy logic system (FLS, neural network, and its learning algorithm using the optimal learning algorithm. The properties of type-1 FNN system parallel computation scheme and parameter convergence are easily extended to type-2 FNN systems. In addition, a robust adaptive control scheme which combines the adaptive type-2 FNN controller and compensated controller is proposed for nonlinear uncertain systems. Simulation results are presented to illustrate the effectiveness of our approach.
Full Text Available This paper proposes an adaptive iterative learning control strategy integrated with saturation-based robust control for uncertain robot system in presence of modelling uncertainties, unknown parameter, and external disturbance under alignment condition. An important merit is that it achieves adaptive switching of gain matrix both in conventional PD-type feedforward control and robust adaptive control in the iteration domain simultaneously. The analysis of convergence of proposed control law is based on Lyapunov's direct method under alignment initial condition. Simulation results demonstrate the faster learning rate and better robust performance with proposed algorithm by comparing with other existing robust controllers. The actual experiment on three-DOF robot manipulator shows its better practical effectiveness.