Joint control algorithm in access network

Institute of Scientific and Technical Information of China (English)

2008-01-01

To deal with long probing delay and inaccurate probing results in the endpoint admission control method,a joint local and end-to-end admission control algorithm is proposed,which introduces local probing of access network besides end-to-end probing.Through local probing,the algorithm accurately estimated the resource status of the access network.Simulation shows that this algorithm can improve admission control performance and reduce users' average waiting time when the access network is heavily loaded.

A semi-active suspension control algorithm for vehicle comprehensive vertical dynamics performance

Science.gov (United States)

Nie, Shida; Zhuang, Ye; Liu, Weiping; Chen, Fan

2017-08-01

Comprehensive performance of the vehicle, including ride qualities and road-holding, is essentially of great value in practice. Many up-to-date semi-active control algorithms improve vehicle dynamics performance effectively. However, it is hard to improve comprehensive performance for the conflict between ride qualities and road-holding around the second-order resonance. Hence, a new control algorithm is proposed to achieve a good trade-off between ride qualities and road-holding. In this paper, the properties of the invariant points are analysed, which gives an insight into the performance conflicting around the second-order resonance. Based on it, a new control algorithm is proposed. The algorithm employs a novel frequency selector to balance suspension ride and handling performance by adopting a medium damping around the second-order resonance. The results of this study show that the proposed control algorithm could improve the performance of ride qualities and suspension working space up to 18.3% and 8.2%, respectively, with little loss of road-holding compared to the passive suspension. Consequently, the comprehensive performance can be improved by 6.6%. Hence, the proposed algorithm is of great potential to be implemented in practice.

Algorithm of composing the schedule of construction and installation works

Science.gov (United States)

Nehaj, Rustam; Molotkov, Georgij; Rudchenko, Ivan; Grinev, Anatolij; Sekisov, Aleksandr

2017-10-01

An algorithm for scheduling works is developed, in which the priority of the work corresponds to the total weight of the subordinate works, the vertices of the graph, and it is proved that for graphs of the tree type the algorithm is optimal. An algorithm is synthesized to reduce the search for solutions when drawing up schedules of construction and installation works, allocating a subset with the optimal solution of the problem of the minimum power, which is determined by the structure of its initial data and numerical values. An algorithm for scheduling construction and installation work is developed, taking into account the schedule for the movement of brigades, which is characterized by the possibility to efficiently calculate the values of minimizing the time of work performance by the parameters of organizational and technological reliability through the use of the branch and boundary method. The program of the computational algorithm was compiled in the MatLAB-2008 program. For the initial data of the matrix, random numbers were taken, uniformly distributed in the range from 1 to 100. It takes 0.5; 2.5; 7.5; 27 minutes to solve the problem. Thus, the proposed method for estimating the lower boundary of the solution is sufficiently accurate and allows efficient solution of the minimax task of scheduling construction and installation works.

Automatic control algorithm effects on energy production

Science.gov (United States)

Mcnerney, G. M.

1981-01-01

A computer model was developed using actual wind time series and turbine performance data to simulate the power produced by the Sandia 17-m VAWT operating in automatic control. The model was used to investigate the influence of starting algorithms on annual energy production. The results indicate that, depending on turbine and local wind characteristics, a bad choice of a control algorithm can significantly reduce overall energy production. The model can be used to select control algorithms and threshold parameters that maximize long term energy production. The results from local site and turbine characteristics were generalized to obtain general guidelines for control algorithm design.

Automatic boiling water reactor control rod pattern design using particle swarm optimization algorithm and local search

Energy Technology Data Exchange (ETDEWEB)

Wang, Cheng-Der, E-mail: jdwang@iner.gov.tw [Nuclear Engineering Division, Institute of Nuclear Energy Research, No. 1000, Wenhua Rd., Jiaan Village, Longtan Township, Taoyuan County 32546, Taiwan, ROC (China); Lin, Chaung [National Tsing Hua University, Department of Engineering and System Science, 101, Section 2, Kuang Fu Road, Hsinchu 30013, Taiwan (China)

2013-02-15

Highlights: ► The PSO algorithm was adopted to automatically design a BWR CRP. ► The local search procedure was added to improve the result of PSO algorithm. ► The results show that the obtained CRP is the same good as that in the previous work. -- Abstract: This study developed a method for the automatic design of a boiling water reactor (BWR) control rod pattern (CRP) using the particle swarm optimization (PSO) algorithm. The PSO algorithm is more random compared to the rank-based ant system (RAS) that was used to solve the same BWR CRP design problem in the previous work. In addition, the local search procedure was used to make improvements after PSO, by adding the single control rod (CR) effect. The design goal was to obtain the CRP so that the thermal limits and shutdown margin would satisfy the design requirement and the cycle length, which is implicitly controlled by the axial power distribution, would be acceptable. The results showed that the same acceptable CRP found in the previous work could be obtained.

PID-Controller Tuning Optimization with Genetic Algorithms in Servo Systems

Directory of Open Access Journals (Sweden)

Arturo Y. Jaen-Cuellar

2013-09-01

Full Text Available Performance improvement is the main goal of the study of PID control and much research has been conducted for this purpose. The PID filter is implemented in almost all industrial processes because of its well-known beneficial features. In general, the whole system's performance strongly depends on the controller's efficiency and hence the tuning process plays a key role in the system's behaviour. In this work, the servo systems will be analysed, specifically the positioning control systems. Among the existent tuning methods, the Gain-Phase Margin method based on Frequency Response analysis is the most adequate for controller tuning in positioning control systems. Nevertheless, this method can be improved by integrating an optimization technique. The novelty of this work is the development of a new methodology for PID control tuning by coupling the Gain-Phase Margin method with the Genetic Algorithms in which the micro-population concept and adaptive mutation probability are applied. Simulations using a positioning system model in MATLAB and experimental tests in two CNC machines and an industrial robot are carried out in order to show the effectiveness of the proposal. The obtained results are compared with both the classical Gain-Phase Margin tuning and with a recent PID controller optimization using Genetic Algorithms based on real codification. The three methodologies are implemented using software.

Control algorithms and applications of the wavefront sensorless adaptive optics

Science.gov (United States)

Ma, Liang; Wang, Bin; Zhou, Yuanshen; Yang, Huizhen

2017-10-01

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.

Evaluation of train-speed control algorithms

Energy Technology Data Exchange (ETDEWEB)

Slavik, M.M. [BKS Advantech (Pty.) Ltd., Pretoria (South Africa)

2000-07-01

A relatively simple and fast simulator has been developed and used for the preliminary testing of train cruise-control algorithms. The simulation is done in software on a PC. The simulator is used to gauge the consequences and feasibility of a cruise-control strategy prior to more elaborate testing and evaluation. The tool was used to design and pre-test a train-cruise control algorithm called NSS, which does not require knowledge of exact train mass, vertical alignment, or actual braking force. Only continuous measurements on the speed of the train and electrical current are required. With this modest input, the NSS algorithm effected speed changes smoothly and efficiently for a wide range of operating conditions. (orig.)

Optimal Pid Controller Design Using Adaptive Vurpso Algorithm

Science.gov (United States)

Zirkohi, Majid Moradi

2015-04-01

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.

Seismic active control by a heuristic-based algorithm

International Nuclear Information System (INIS)

Tang, Yu.

1996-01-01

A heuristic-based algorithm for seismic active control is generalized to permit consideration of the effects of control-structure interaction and actuator dynamics. Control force is computed at onetime step ahead before being applied to the structure. Therefore, the proposed control algorithm is free from the problem of time delay. A numerical example is presented to show the effectiveness of the proposed control algorithm. Also, two indices are introduced in the paper to assess the effectiveness and efficiency of control laws

Computational issues in alternating projection algorithms for fixed-order control design

DEFF Research Database (Denmark)

Beran, Eric Bengt; Grigoriadis, K.

1997-01-01

Alternating projection algorithms have been introduced recently to solve fixed-order controller design problems described by linear matrix inequalities and non-convex coupling rank constraints. In this work, an extensive numerical experimentation using proposed benchmark fixed-order control design...... examples is used to indicate the computational efficiency of the method. These results indicate that the proposed alternating projections are effective in obtaining low-order controllers for small and medium order problems...

Researching on YH100 Numerical Control Servo Press Hydraulic Control System and Control Algorithm

Directory of Open Access Journals (Sweden)

Kai LI

2014-09-01

Full Text Available In order to study the numerical control (NC servo press hydraulic control system and its control algorithm. The numerical control servo press performance and control principle of hydraulic control system are analyzed. According to the flow equation of the hydraulic control valve, hydraulic cylinder flow continuity equation and the force balance equation of the hydraulic cylinder with load press, the mathematical model of hydraulic control system is established. And the servo press hydraulic system transfer function is deduced. Introducing the suitable immune particle swarm control algorithm for servo press hydraulic system, and the control system block diagram is established. Immune algorithm is used to optimize new control parameters of the system and adopt the new optimization results to optimize the system simulation. The simulation result shows that the hydraulic system’s transition time controlled by the immune particle swarm algorithm is shorter than traditional ones, and the control performance is obviously improved. Finally it can be concluded that immune particle swarm PID control have these characteristics such as quickness, stability and accuracy. Applying this principle into application, the obtained YH100 numerical control servo press hydraulic control system meets the requirement.

Advanced Suspension and Control Algorithm for U.S. Army Ground Vehicles

Science.gov (United States)

2013-04-01

magnetorheological fluid damper . This report provides a record of the research findings from this research project on advanced suspension and control...nonlinear control algorithm that can effectively work with semi-active dampers , such as the magnetorheological (MR) fluid damper . This research...rheological fluid effects). This is because the viscous damping force for high shaft speed becomes excessive and will transmit the terrain-induced

Recent Advancements in Lightning Jump Algorithm Work

Science.gov (United States)

Schultz, Christopher J.; Petersen, Walter A.; Carey, Lawrence D.

2010-01-01

In the past year, the primary objectives were to show the usefulness of total lightning as compared to traditional cloud-to-ground (CG) networks, test the lightning jump algorithm configurations in other regions of the country, increase the number of thunderstorms within our thunderstorm database, and to pinpoint environments that could prove difficult for any lightning jump configuration. A total of 561 thunderstorms have been examined in the past year (409 non-severe, 152 severe) from four regions of the country (North Alabama, Washington D.C., High Plains of CO/KS, and Oklahoma). Results continue to indicate that the 2 lightning jump algorithm configuration holds the most promise in terms of prospective operational lightning jump algorithms, with a probability of detection (POD) at 81%, a false alarm rate (FAR) of 45%, a critical success index (CSI) of 49% and a Heidke Skill Score (HSS) of 0.66. The second best performing algorithm configuration was the Threshold 4 algorithm, which had a POD of 72%, FAR of 51%, a CSI of 41% and an HSS of 0.58. Because a more complex algorithm configuration shows the most promise in terms of prospective operational lightning jump algorithms, accurate thunderstorm cell tracking work must be undertaken to track lightning trends on an individual thunderstorm basis over time. While these numbers for the 2 configuration are impressive, the algorithm does have its weaknesses. Specifically, low-topped and tropical cyclone thunderstorm environments are present issues for the 2 lightning jump algorithm, because of the suppressed vertical depth impact on overall flash counts (i.e., a relative dearth in lightning). For example, in a sample of 120 thunderstorms from northern Alabama that contained 72 missed events by the 2 algorithm 36% of the misses were associated with these two environments (17 storms).

Algorithms for orbit control on SPEAR

International Nuclear Information System (INIS)

Corbett, J.; Keeley, D.; Hettel, R.; Linscott, I.; Sebek, J.

1994-06-01

A global orbit feedback system has been installed on SPEAR to help stabilize the position of the photon beams. The orbit control algorithms depend on either harmonic reconstruction of the orbit or eigenvector decomposition. The orbit motion is corrected by dipole corrector kicks determined from the inverse corrector-to-bpm response matrix. This paper outlines features of these control algorithms as applied to SPEAR

AN ALGORITHM OF ADAPTIVE TORQUE CONTROL IN INJECTOR INTERNAL COMBUSTION ENGINE

Directory of Open Access Journals (Sweden)

D. N. Gerasimov

2015-07-01

Full Text Available Subject of Research. Internal combustion engine as a plant is a highly nonlinear complex system that works mostly in dynamic regimes in the presence of noise and disturbances. A number of engine characteristics and parameters is not known or known approximately due to the complex structure and multimode operating of the engine. In this regard the problem of torque control is not trivial and motivates the use of modern techniques of control theory that give the possibility to overcome the mentioned problems. As a consequence, a relatively simple algorithm of adaptive torque control of injector engine is proposed in the paper. Method. Proposed method is based on nonlinear dynamic model with parametric and functional uncertainties (static characteristics which are suppressed by means of adaptive control algorithm with single adjustable parameter. The algorithm is presented by proportional control law with adjustable feedback gain and provides the exponential convergence of the control error to the neighborhood of zero equilibrium. It is shown that the radius of the neighborhood can be arbitrary reduced by the change of controller design parameters. Main Results. A dynamical nonlinear model of the engine has been designed for the purpose of control synthesis and simulation of the closed-loop system. The parameters and static functions of the model are identified with the use of data aquired during Federal Test Procedure (USA of Chevrolet Tahoe vehicle with eight cylinders 5,7L engine. The algorithm of adaptive torque control is designed, and the properties of the closed-loop system are analyzed with the use of Lyapunov functions approach. The closed-loop system operating is verified by means of simulation in the MatLab/Simulink environment. Simulation results show that the controller provides the boundedness of all signals and convergence of the control error to the neighborhood of zero equilibrium despite significant variations of engine speed. The

Subcubic Control Flow Analysis Algorithms

DEFF Research Database (Denmark)

Midtgaard, Jan; Van Horn, David

We give the first direct subcubic algorithm for performing control flow analysis of higher-order functional programs. Despite the long held belief that inclusion-based flow analysis could not surpass the ``cubic bottleneck, '' we apply known set compression techniques to obtain an algorithm...... that runs in time O(n^3/log n) on a unit cost random-access memory model machine. Moreover, we refine the initial flow analysis into two more precise analyses incorporating notions of reachability. We give subcubic algorithms for these more precise analyses and relate them to an existing analysis from...

Implantation of algorithms of diffuse control in DSPS; Implantacion de algoritmos de control difuso en DSPS

Energy Technology Data Exchange (ETDEWEB)

Perez C, B

2003-07-01

In this thesis work there are presented: a) The characteristics and main components used in an electronic system based on a Dsp guided to control applications of processes, b) The description of an algorithm of diffuse control whose objective is the regulation of neutron power in a model of the punctual kinetics of a nuclear research reactor type TRIGA, and c) The installation in language assembler and execution in real time of the control algorithm in the system based on a Dsp. With regard to the installation and execution of the algorithm, the reaches of the project have been delimited to the following: a) Readiness of the entrance values to the controller in specific registrations of the system Dsp, b) Conversion of the entrances to the numerical formats with those that one obtains the best acting in the control algorithm, c) Execution of the algorithm until the obtaining of the value of the controller's exit, and d) Placement of the result in specific registrations of the Dsp for their later reading for an external parallel interface. It is necessary to mention that the simulation of the punctual kinetics of a reactor type TRIGA in the Pc and its integration with the control system based on the one Dsp is had contemplated as continuation of this work and that one of those will constitute main activities in my project of master thesis. A brief description of the topics presented in this thesis is given next. In the chapter one it is presented a general description of the diffuse logic and some of their applications in the industry. The main characteristics of a Dsp are also presented that they make it different from a micro controller or a microprocessor of general purpose. In the chapter 2 details of the internal architecture of the Dsp TMS320CS0 of Texas Instruments that are not explained with detail in the manual of user of the same one. This chapter has as objective to understand the internal hardware of the Dsp that is used for to carry out the program

Implantation of algorithms of diffuse control in DSPS; Implantacion de algoritmos de control difuso en DSPS

Energy Technology Data Exchange (ETDEWEB)

Perez C, B

2003-07-01

In this thesis work there are presented: a) The characteristics and main components used in an electronic system based on a Dsp guided to control applications of processes, b) The description of an algorithm of diffuse control whose objective is the regulation of neutron power in a model of the punctual kinetics of a nuclear research reactor type TRIGA, and c) The installation in language assembler and execution in real time of the control algorithm in the system based on a Dsp. With regard to the installation and execution of the algorithm, the reaches of the project have been delimited to the following: a) Readiness of the entrance values to the controller in specific registrations of the system Dsp, b) Conversion of the entrances to the numerical formats with those that one obtains the best acting in the control algorithm, c) Execution of the algorithm until the obtaining of the value of the controller's exit, and d) Placement of the result in specific registrations of the Dsp for their later reading for an external parallel interface. It is necessary to mention that the simulation of the punctual kinetics of a reactor type TRIGA in the Pc and its integration with the control system based on the one Dsp is had contemplated as continuation of this work and that one of those will constitute main activities in my project of master thesis. A brief description of the topics presented in this thesis is given next. In the chapter one it is presented a general description of the diffuse logic and some of their applications in the industry. The main characteristics of a Dsp are also presented that they make it different from a micro controller or a microprocessor of general purpose. In the chapter 2 details of the internal architecture of the Dsp TMS320CS0 of Texas Instruments that are not explained with detail in the manual of user of the same one. This chapter has as objective to understand the internal hardware of the Dsp that is used for to carry out the program in

A novel algorithm for demand-control of a single-room ventilation unit with a rotary heat exchanger

DEFF Research Database (Denmark)

Smith, Kevin Michael; Jansen, Anders Lund; Svendsen, Svend

in the indoor environment. Based on these values, a demand-control algorithm varies fan speeds to change airflow rates and varies the rotational speed of the heat exchanger to modulate heat and moisture recovery. The algorithm varies airflow rates to provide free cooling and limit CO2 concentrations and varies...... moisture recovery by varying the rotational speed and then safely unbalances airflows in a worst-case scenario. In the algorithm, frost protection and minimum supply temperature take the highest priority and override other controls. This paper documents the proposed demand control algorithm and analyses...... its impacts on compliance of building regulations in Denmark. The paper presents an algorithm that manufacturers can program into their controls. The commercially available single-room ventilation unit with a rotary heat exchanger uses this algorithm coded in the C language. Future work will document...

Design and implementation of adaptive inverse control algorithm for a micro-hand control system

Directory of Open Access Journals (Sweden)

Wan-Cheng Wang

2014-01-01

Full Text Available The Letter proposes an online tuned adaptive inverse position control algorithm for a micro-hand. First, the configuration of the micro-hand is discussed. Next, a kinematic analysis of the micro-hand is investigated and then the relationship between the rotor position of micro-permanent magnet synchronous motor and the tip of the micro-finger is derived. After that, an online tuned adaptive inverse control algorithm, which includes an adaptive inverse model and an adaptive inverse control, is designed. The online tuned adaptive inverse control algorithm has better performance than the proportional–integral control algorithm does. In addition, to avoid damaging the object during the grasping process, an online force control algorithm is proposed here as well. An embedded micro-computer, cRIO-9024, is used to realise the whole position control algorithm and the force control algorithm by using software. As a result, the hardware circuit is very simple. Experimental results show that the proposed system can provide fast transient responses, good load disturbance responses, good tracking responses and satisfactory grasping responses.

Searching for the majority: algorithms of voluntary control.

Directory of Open Access Journals (Sweden)

Jin Fan

Full Text Available Voluntary control of information processing is crucial to allocate resources and prioritize the processes that are most important under a given situation; the algorithms underlying such control, however, are often not clear. We investigated possible algorithms of control for the performance of the majority function, in which participants searched for and identified one of two alternative categories (left or right pointing arrows as composing the majority in each stimulus set. We manipulated the amount (set size of 1, 3, and 5 and content (ratio of left and right pointing arrows within a set of the inputs to test competing hypotheses regarding mental operations for information processing. Using a novel measure based on computational load, we found that reaction time was best predicted by a grouping search algorithm as compared to alternative algorithms (i.e., exhaustive or self-terminating search. The grouping search algorithm involves sampling and resampling of the inputs before a decision is reached. These findings highlight the importance of investigating the implications of voluntary control via algorithms of mental operations.

Implementation of fuzzy logic control algorithm in embedded ...

African Journals Online (AJOL)

Fuzzy logic control algorithm solves problems that are difficult to address with traditional control techniques. This paper describes an implementation of fuzzy logic control algorithm using inexpensive hardware as well as how to use fuzzy logic to tackle a specific control problem without any special software tools. As a case ...

Nonlinear model predictive control theory and algorithms

CERN Document Server

Grüne, Lars

2017-01-01

This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...

Reactor controller design using genetic algorithm with simulated annealing

International Nuclear Information System (INIS)

Willjuice Iruthyarajan, M.

2012-01-01

Many reactor control design work, specifically the problem of synthesis and optimization of reactor networks involving the classical reaction schemes was studied, considering a superstructure formed by a CSTR and a PFR and their possible arrangements. A genetic algorithm was proposed, together with a systematic procedure. Two case studies were solved with the proposed systematic. Both of them present similar results than the published in the literature. The first case studied was the Trambouze reaction scheme. Although selectivity values are smaller then the values published in the referred papers, the reactors system combined volume is always minor them the other ones. The second case studied was the Van de Vusse reaction scheme. In this case, the obtained value for the total volume is always minor then the considered papers. One can conclude that when compared with the other works presented in the literature results are compatible and very interesting. The developed algorithms can be used as a good alternative for reactor networks design and optimization problem

Randomized algorithms in automatic control and data mining

CERN Document Server

Granichin, Oleg; Toledano-Kitai, Dvora

2015-01-01

In the fields of data mining and control, the huge amount of unstructured data and the presence of uncertainty in system descriptions have always been critical issues. The book Randomized Algorithms in Automatic Control and Data Mining introduces the readers to the fundamentals of randomized algorithm applications in data mining (especially clustering) and in automatic control synthesis. The methods proposed in this book guarantee that the computational complexity of classical algorithms and the conservativeness of standard robust control techniques will be reduced. It is shown that when a problem requires "brute force" in selecting among options, algorithms based on random selection of alternatives offer good results with certain probability for a restricted time and significantly reduce the volume of operations.

FORMATION OF THE SYNTHESIS ALGORITHMS OF THE COORDINATING CONTROL SYSTEMS BY MEANS OF THE AUTOMATIC GENERATION OF PETRI NETS

Directory of Open Access Journals (Sweden)

A. A. Gurskiy

2016-09-01

Full Text Available The coordinating control system by drives of the robot-manipulator is presented in this article. The purpose of the scientific work is the development and research of the new algorithms for parametric synthesis of the coordinating control systems. To achieve this aim it is necessary to develop the system generating the required parametric synthesis algorithms and performing the necessary procedures according to the generated algorithm. This scientific work deals with the synthesis of Petri net in the specific case with the automatic generation of Petri nets.

Algorithm improvement for phase control of subharmonic buncher

International Nuclear Information System (INIS)

Zhang Junqiang; Yu Luyang; Yin Chongxian; Zhao Minghua; Zhong Shaopeng

2011-01-01

To realize digital phase control of subharmonic buncher,a low level radio frequency control system using down converter, IQ modulator and demodulator techniques, and commercial PXI system, was developed on the platform of LabVIEW. A single-neuron adaptive PID (proportional-integral-derivative) control algorithm with ability of self learning was adopted, satisfying the requirements of phase stability. By comparison with the traditional PID algorithm in field testing, the new algorithm has good stability, fast response and strong anti-interference ability. (authors)

MPPT algorithm for voltage controlled PV inverters

DEFF Research Database (Denmark)

Kerekes, Tamas; Teodorescu, Remus; Liserre, Marco

2008-01-01

This paper presents a novel concept for an MPPT that can be used in case of a voltage controlled grid connected PV inverters. In case of single-phase systems, the 100 Hz ripple in the AC power is also present on the DC side. Depending on the DC link capacitor, this power fluctuation can be used t...... to track the MPP of the PV array, using the information that at MPP the power oscillations are very small. In this way the algorithm can detect the fact that the current working point is at the MPP, for the current atmospheric conditions....

Fuzzy power control algorithm for a pressurized water reactor

International Nuclear Information System (INIS)

Hah, Y.J.; Lee, B.W.

1994-01-01

A fuzzy power control algorithm is presented for automatic reactor power control in a pressurized water reactor (PWR). Automatic power shape control is complicated by the use of control rods with a conventional proportional-integral-differential controller because it is highly coupled with reactivity compensation. Thus, manual shape controls are usually employed even for the limited capability needed for load-following operations including frequency control. In an attempt to achieve automatic power shape control without any design modifications to the core, a fuzzy power control algorithm is proposed. For the fuzzy control, the rule base is formulated based on a multiple-input multiple-output system. The minimum operation rule and the center of area method are implemented for the development of the fuzzy algorithm. The fuzzy power control algorithm has been applied to Yonggwang Nuclear Unit 3. The simulation results show that the fuzzy control can be adapted as a practical control strategy for automatic reactor power control of PWRs during the load-following operations

Robust Fault-Tolerant Control for Satellite Attitude Stabilization Based on Active Disturbance Rejection Approach with Artificial Bee Colony Algorithm

Directory of Open Access Journals (Sweden)

Fei Song

2014-01-01

Full Text Available This paper proposed a robust fault-tolerant control algorithm for satellite stabilization based on active disturbance rejection approach with artificial bee colony algorithm. The actuating mechanism of attitude control system consists of three working reaction flywheels and one spare reaction flywheel. The speed measurement of reaction flywheel is adopted for fault detection. If any reaction flywheel fault is detected, the corresponding fault flywheel is isolated and the spare reaction flywheel is activated to counteract the fault effect and ensure that the satellite is working safely and reliably. The active disturbance rejection approach is employed to design the controller, which handles input information with tracking differentiator, estimates system uncertainties with extended state observer, and generates control variables by state feedback and compensation. The designed active disturbance rejection controller is robust to both internal dynamics and external disturbances. The bandwidth parameter of extended state observer is optimized by the artificial bee colony algorithm so as to improve the performance of attitude control system. A series of simulation experiment results demonstrate the performance superiorities of the proposed robust fault-tolerant control algorithm.

A controllable sensor management algorithm capable of learning

Science.gov (United States)

Osadciw, Lisa A.; Veeramacheneni, Kalyan K.

2005-03-01

Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network"s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.

Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey

Science.gov (United States)

Ni, Jianjun; Wu, Liuying; Fan, Xinnan; Yang, Simon X.

2016-01-01

Bioinspired intelligent algorithm (BIA) is a kind of intelligent computing method, which is with a more lifelike biological working mechanism than other types. BIAs have made significant progress in both understanding of the neuroscience and biological systems and applying to various fields. Mobile robot control is one of the main application fields of BIAs which has attracted more and more attention, because mobile robots can be used widely and general artificial intelligent algorithms meet a development bottleneck in this field, such as complex computing and the dependence on high-precision sensors. This paper presents a survey of recent research in BIAs, which focuses on the research in the realization of various BIAs based on different working mechanisms and the applications for mobile robot control, to help in understanding BIAs comprehensively and clearly. The survey has four primary parts: a classification of BIAs from the biomimetic mechanism, a summary of several typical BIAs from different levels, an overview of current applications of BIAs in mobile robot control, and a description of some possible future directions for research. PMID:26819582

Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey.

Science.gov (United States)

Ni, Jianjun; Wu, Liuying; Fan, Xinnan; Yang, Simon X

2016-01-01

Bioinspired intelligent algorithm (BIA) is a kind of intelligent computing method, which is with a more lifelike biological working mechanism than other types. BIAs have made significant progress in both understanding of the neuroscience and biological systems and applying to various fields. Mobile robot control is one of the main application fields of BIAs which has attracted more and more attention, because mobile robots can be used widely and general artificial intelligent algorithms meet a development bottleneck in this field, such as complex computing and the dependence on high-precision sensors. This paper presents a survey of recent research in BIAs, which focuses on the research in the realization of various BIAs based on different working mechanisms and the applications for mobile robot control, to help in understanding BIAs comprehensively and clearly. The survey has four primary parts: a classification of BIAs from the biomimetic mechanism, a summary of several typical BIAs from different levels, an overview of current applications of BIAs in mobile robot control, and a description of some possible future directions for research.

Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey

Directory of Open Access Journals (Sweden)

Jianjun Ni

2016-01-01

Full Text Available Bioinspired intelligent algorithm (BIA is a kind of intelligent computing method, which is with a more lifelike biological working mechanism than other types. BIAs have made significant progress in both understanding of the neuroscience and biological systems and applying to various fields. Mobile robot control is one of the main application fields of BIAs which has attracted more and more attention, because mobile robots can be used widely and general artificial intelligent algorithms meet a development bottleneck in this field, such as complex computing and the dependence on high-precision sensors. This paper presents a survey of recent research in BIAs, which focuses on the research in the realization of various BIAs based on different working mechanisms and the applications for mobile robot control, to help in understanding BIAs comprehensively and clearly. The survey has four primary parts: a classification of BIAs from the biomimetic mechanism, a summary of several typical BIAs from different levels, an overview of current applications of BIAs in mobile robot control, and a description of some possible future directions for research.

A robust controller design method for feedback substitution schemes using genetic algorithms

Energy Technology Data Exchange (ETDEWEB)

Trujillo, Mirsha M; Hadjiloucas, Sillas; Becerra, Victor M, E-mail: s.hadjiloucas@reading.ac.uk [Cybernetics, School of Systems Engineering, University of Reading, RG6 6AY (United Kingdom)

2011-08-17

Controllers for feedback substitution schemes demonstrate a trade-off between noise power gain and normalized response time. Using as an example the design of a controller for a radiometric transduction process subjected to arbitrary noise power gain and robustness constraints, a Pareto-front of optimal controller solutions fulfilling a range of time-domain design objectives can be derived. In this work, we consider designs using a loop shaping design procedure (LSDP). The approach uses linear matrix inequalities to specify a range of objectives and a genetic algorithm (GA) to perform a multi-objective optimization for the controller weights (MOGA). A clonal selection algorithm is used to further provide a directed search of the GA towards the Pareto front. We demonstrate that with the proposed methodology, it is possible to design higher order controllers with superior performance in terms of response time, noise power gain and robustness.

Reactor controller design using genetic algorithms with simulated annealing

International Nuclear Information System (INIS)

Erkan, K.; Buetuen, E.

2000-01-01

This chapter presents a digital control system for ITU TRIGA Mark-II reactor using genetic algorithms with simulated annealing. The basic principles of genetic algorithms for problem solving are inspired by the mechanism of natural selection. Natural selection is a biological process in which stronger individuals are likely to be winners in a competing environment. Genetic algorithms use a direct analogy of natural evolution. Genetic algorithms are global search techniques for optimisation but they are poor at hill-climbing. Simulated annealing has the ability of probabilistic hill-climbing. Thus, the two techniques are combined here to get a fine-tuned algorithm that yields a faster convergence and a more accurate search by introducing a new mutation operator like simulated annealing or an adaptive cooling schedule. In control system design, there are currently no systematic approaches to choose the controller parameters to obtain the desired performance. The controller parameters are usually determined by test and error with simulation and experimental analysis. Genetic algorithm is used automatically and efficiently searching for a set of controller parameters for better performance. (orig.)

Rate-control algorithms testing by using video source model

DEFF Research Database (Denmark)

Belyaev, Evgeny; Turlikov, Andrey; Ukhanova, Anna

2008-01-01

In this paper the method of rate control algorithms testing by the use of video source model is suggested. The proposed method allows to significantly improve algorithms testing over the big test set.......In this paper the method of rate control algorithms testing by the use of video source model is suggested. The proposed method allows to significantly improve algorithms testing over the big test set....

Influence of control algorithms parameters on an electromechanical converter with a secondary discrete part

Directory of Open Access Journals (Sweden)

Kuimov Denis

2017-01-01

Full Text Available An alternative configuration of a device with a secondary discrete part using a magnetic system of a similar multi-phase inductor machine and concentrated windings without an internal rotor is proposed. An algorithm of sensorless control of a motion process of a secondary discrete part is proposed. The analysis of the distribution nature of the magnetic field for various switching algorithms is carried out to reduce negative influence of the “dead” zones of the first and second order. The features of the movement process of the secondary discrete part in the working chamber of the device are considered. The results of in the electromagnetic force change affecting a ferromagnetic working element are presented, and recommendations for the application of switching algorithms are given.

Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

International Nuclear Information System (INIS)

Dong Yun Kim; Poong Hyun Seong; .

1997-01-01

In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate gains, which minimize the error of system. The proposed algorithm can reduce the time and effort required for obtaining the fuzzy rules through the intelligent learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller. (author)

On the Impact of Localization and Density Control Algorithms in Target Tracking Applications for Wireless Sensor Networks

Science.gov (United States)

Campos, Andre N.; Souza, Efren L.; Nakamura, Fabiola G.; Nakamura, Eduardo F.; Rodrigues, Joel J. P. C.

2012-01-01

Target tracking is an important application of wireless sensor networks. The networks' ability to locate and track an object is directed linked to the nodes' ability to locate themselves. Consequently, localization systems are essential for target tracking applications. In addition, sensor networks are often deployed in remote or hostile environments. Therefore, density control algorithms are used to increase network lifetime while maintaining its sensing capabilities. In this work, we analyze the impact of localization algorithms (RPE and DPE) and density control algorithms (GAF, A3 and OGDC) on target tracking applications. We adapt the density control algorithms to address the k-coverage problem. In addition, we analyze the impact of network density, residual integration with density control, and k-coverage on both target tracking accuracy and network lifetime. Our results show that DPE is a better choice for target tracking applications than RPE. Moreover, among the evaluated density control algorithms, OGDC is the best option among the three. Although the choice of the density control algorithm has little impact on the tracking precision, OGDC outperforms GAF and A3 in terms of tracking time. PMID:22969329

Research on intelligent algorithm of electro - hydraulic servo control system

Science.gov (United States)

Wang, Yannian; Zhao, Yuhui; Liu, Chengtao

2017-09-01

In order to adapt the nonlinear characteristics of the electro-hydraulic servo control system and the influence of complex interference in the industrial field, using a fuzzy PID switching learning algorithm is proposed and a fuzzy PID switching learning controller is designed and applied in the electro-hydraulic servo controller. The designed controller not only combines the advantages of the fuzzy control and PID control, but also introduces the learning algorithm into the switching function, which makes the learning of the three parameters in the switching function can avoid the instability of the system during the switching between the fuzzy control and PID control algorithms. It also makes the switch between these two control algorithm more smoother than that of the conventional fuzzy PID.

Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

International Nuclear Information System (INIS)

Kim, Dong Yun

1997-02-01

In this research, we propose a fuzzy gain scheduler (FGS) with an intelligent learning algorithm for a reactor control. In the proposed algorithm, the gradient descent method is used in order to generate the rule bases of a fuzzy algorithm by learning. These rule bases are obtained by minimizing an objective function, which is called a performance cost function. The objective of the FGS with an intelligent learning algorithm is to generate adequate gains, which minimize the error of system. The proposed algorithm can reduce the time and efforts required for obtaining the fuzzy rules through the intelligent learning function. The evolutionary programming algorithm is modified and adopted as the method in order to find the optimal gains which are used as the initial gains of FGS with learning function. It is applied to reactor control of nuclear power plant (NPP), and the results are compared with those of a conventional PI controller with fixed gains. As a result, it is shown that the proposed algorithm is superior to the conventional PI controller

A comparison of two adaptive algorithms for the control of active engine mounts

Science.gov (United States)

Hillis, A. J.; Harrison, A. J. L.; Stoten, D. P.

2005-08-01

This paper describes work conducted in order to control automotive active engine mounts, consisting of a conventional passive mount and an internal electromagnetic actuator. Active engine mounts seek to cancel the oscillatory forces generated by the rotation of out-of-balance masses within the engine. The actuator generates a force dependent on a control signal from an algorithm implemented with a real-time DSP. The filtered-x least-mean-square (FXLMS) adaptive filter is used as a benchmark for comparison with a new implementation of the error-driven minimal controller synthesis (Er-MCSI) adaptive controller. Both algorithms are applied to an active mount fitted to a saloon car equipped with a four-cylinder turbo-diesel engine, and have no a priori knowledge of the system dynamics. The steady-state and transient performance of the two algorithms are compared and the relative merits of the two approaches are discussed. The Er-MCSI strategy offers significant computational advantages as it requires no cancellation path modelling. The Er-MCSI controller is found to perform in a fashion similar to the FXLMS filter—typically reducing chassis vibration by 50-90% under normal driving conditions.

DC Voltage Droop Control Implementation in the AC/DC Power Flow Algorithm: Combinational Approach

DEFF Research Database (Denmark)

Akhter, F.; Macpherson, D.E.; Harrison, G.P.

2015-01-01

of operational flexibility, as more than one VSC station controls the DC link voltage of the MTDC system. This model enables the study of the effects of DC droop control on the power flows of the combined AC/DC system for steady state studies after VSC station outages or transient conditions without needing...... to use its complete dynamic model. Further, the proposed approach can be extended to include multiple AC and DC grids for combined AC/DC power flow analysis. The algorithm is implemented by modifying the MATPOWER based MATACDC program and the results shows that the algorithm works efficiently....

The research on algorithms for optoelectronic tracking servo control systems

Science.gov (United States)

Zhu, Qi-Hai; Zhao, Chang-Ming; Zhu, Zheng; Li, Kun

2016-10-01

The photoelectric servo control system based on PC controllers is mainly used to control the speed and position of the load. This paper analyzed the mathematical modeling and the system identification of the servo system. In the aspect of the control algorithm, the IP regulator, the fuzzy PID, the Active Disturbance Rejection Control (ADRC) and the adaptive algorithms were compared and analyzed. The PI-P control algorithm was proposed in this paper, which not only has the advantages of the PI regulator that can be quickly saturated, but also overcomes the shortcomings of the IP regulator. The control system has a good starting performance and the anti-load ability in a wide range. Experimental results show that the system has good performance under the guarantee of the PI-P control algorithm.

Solution to automatic generation control problem using firefly algorithm optimized I(λ)D(µ) controller.

Science.gov (United States)

Debbarma, Sanjoy; Saikia, Lalit Chandra; Sinha, Nidul

2014-03-01

Present work focused on automatic generation control (AGC) of a three unequal area thermal systems considering reheat turbines and appropriate generation rate constraints (GRC). A fractional order (FO) controller named as I(λ)D(µ) controller based on crone approximation is proposed for the first time as an appropriate technique to solve the multi-area AGC problem in power systems. A recently developed metaheuristic algorithm known as firefly algorithm (FA) is used for the simultaneous optimization of the gains and other parameters such as order of integrator (λ) and differentiator (μ) of I(λ)D(µ) controller and governor speed regulation parameters (R). The dynamic responses corresponding to optimized I(λ)D(µ) controller gains, λ, μ, and R are compared with that of classical integer order (IO) controllers such as I, PI and PID controllers. Simulation results show that the proposed I(λ)D(µ) controller provides more improved dynamic responses and outperforms the IO based classical controllers. Further, sensitivity analysis confirms the robustness of the so optimized I(λ)D(µ) controller to wide changes in system loading conditions and size and position of SLP. Proposed controller is also found to have performed well as compared to IO based controllers when SLP takes place simultaneously in any two areas or all the areas. Robustness of the proposed I(λ)D(µ) controller is also tested against system parameter variations. © 2013 ISA. Published by Elsevier Ltd. All rights reserved.

Image-based Proof of Work Algorithm for the Incentivization of Blockchain Archival of Interesting Images

OpenAIRE

Billings, Jake

2017-01-01

A new variation of blockchain proof of work algorithm is proposed to incentivize the timely execution of image processing algorithms. A sample image processing algorithm is proposed to determine interesting images using analysis of the entropy of pixel subsets within images. The efficacy of the image processing algorithm is examined using two small sets of training and test data. The interesting image algorithm is then integrated into a simplified blockchain mining proof of work algorithm bas...

Real-Time Attitude Control Algorithm for Fast Tumbling Objects under Torque Constraint

Science.gov (United States)

Tsuda, Yuichi; Nakasuka, Shinichi

This paper describes a new control algorithm for achieving any arbitrary attitude and angular velocity states of a rigid body, even fast and complicated tumbling rotations, under some practical constraints. This technique is expected to be applied for the attitude motion synchronization to capture a non-cooperative, tumbling object in such missions as removal of debris from orbit, servicing broken-down satellites for repairing or inspection, rescue of manned vehicles, etc. For this objective, we have introduced a novel control algorithm called Free Motion Path Method (FMPM) in the previous paper, which was formulated as an open-loop controller. The next step of this consecutive work is to derive a closed-loop FMPM controller, and as the preliminary step toward the objective, this paper attempts to derive a conservative state variables representation of a rigid body dynamics. 6-Dimensional conservative state variables are introduced in place of general angular velocity-attitude angle representation, and how to convert between both representations are shown in this paper.

Data-driven gradient algorithm for high-precision quantum control

Science.gov (United States)

Wu, Re-Bing; Chu, Bing; Owens, David H.; Rabitz, Herschel

2018-04-01

In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., grape) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that grape can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-grape) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-grape algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit controlled-not gate.

Optimization of Aero Engine Acceleration Control in Combat State Based on Genetic Algorithms

Science.gov (United States)

Li, Jie; Fan, Ding; Sreeram, Victor

2012-03-01

In order to drastically exploit the potential of the aero engine and improve acceleration performance in the combat state, an on-line optimized controller based on genetic algorithms is designed for an aero engine. For testing the validity of the presented control method, detailed joint simulation tests of the designed controller and the aero engine model are performed in the whole flight envelope. Simulation test results show that the presented control algorithm has characteristics of rapid convergence speed, high efficiency and can fully exploit the acceleration performance potential of the aero engine. Compared with the former controller, the designed on-line optimized controller (DOOC) can improve the security of the acceleration process and greatly enhance the aero engine thrust in the whole range of the flight envelope, the thrust increases an average of 8.1% in the randomly selected working states. The plane which adopts DOOC can acquire better fighting advantage in the combat state.

Optimal Design for PID Controller Based on DE Algorithm in Omnidirectional Mobile Robot

Directory of Open Access Journals (Sweden)

Wu Peizhang

2017-01-01

Full Text Available This paper introduces a omnidirectional mobile robot based on Mecanum wheel, which is used for conveying heavy load in a small space of the automatic warehousing logistics center. Then analyzes and establishes the omnidirectional chassis inverse and forward kinematic model. In order to improve the performance of motion, the paper proposes the optimal PID controller based on differential evolution algorithm. Finally, through MATLAB simulation, the results show that the kinematic model of mobile robot chassis is correct, further more the controller optimized by the DE algorithm working better than the traditional Z-N PID tuned. So the optimal scheme is reasonable and feasible, which has a value for engineering applications.

Robust reactor power control system design by genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Lee, Yoon Joon; Cho, Kyung Ho; Kim, Sin [Cheju National University, Cheju (Korea, Republic of)

1998-12-31

The H{sub {infinity}} robust controller for the reactor power control system is designed by use of the mixed weight sensitivity. The system is configured into the typical two-port model with which the weight functions are augmented. Since the solution depends on the weighting functions and the problem is of nonconvex, the genetic algorithm is used to determine the weighting functions. The cost function applied in the genetic algorithm permits the direct control of the power tracking performances. In addition, the actual operating constraints such as rod velocity and acceleration can be treated as design parameters. Compared with the conventional approach, the controller designed by the genetic algorithm results in the better performances with the realistic constraints. Also, it is found that the genetic algorithm could be used as an effective tool in the robust design. 4 refs., 6 figs. (Author)

Robust reactor power control system design by genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Lee, Yoon Joon; Cho, Kyung Ho; Kim, Sin [Cheju National University, Cheju (Korea, Republic of)

1997-12-31

The H{sub {infinity}} robust controller for the reactor power control system is designed by use of the mixed weight sensitivity. The system is configured into the typical two-port model with which the weight functions are augmented. Since the solution depends on the weighting functions and the problem is of nonconvex, the genetic algorithm is used to determine the weighting functions. The cost function applied in the genetic algorithm permits the direct control of the power tracking performances. In addition, the actual operating constraints such as rod velocity and acceleration can be treated as design parameters. Compared with the conventional approach, the controller designed by the genetic algorithm results in the better performances with the realistic constraints. Also, it is found that the genetic algorithm could be used as an effective tool in the robust design. 4 refs., 6 figs. (Author)

Visual Perception Based Rate Control Algorithm for HEVC

Science.gov (United States)

Feng, Zeqi; Liu, PengYu; Jia, Kebin

2018-01-01

For HEVC, rate control is an indispensably important video coding technology to alleviate the contradiction between video quality and the limited encoding resources during video communication. However, the rate control benchmark algorithm of HEVC ignores subjective visual perception. For key focus regions, bit allocation of LCU is not ideal and subjective quality is unsatisfied. In this paper, a visual perception based rate control algorithm for HEVC is proposed. First bit allocation weight of LCU level is optimized based on the visual perception of luminance and motion to ameliorate video subjective quality. Then λ and QP are adjusted in combination with the bit allocation weight to improve rate distortion performance. Experimental results show that the proposed algorithm reduces average 0.5% BD-BR and maximum 1.09% BD-BR at no cost in bitrate accuracy compared with HEVC (HM15.0). The proposed algorithm devotes to improving video subjective quality under various video applications.

Wavelet Adaptive Algorithm and Its Application to MRE Noise Control System

Directory of Open Access Journals (Sweden)

Zhang Yulin

2015-01-01

Full Text Available To address the limitation of conventional adaptive algorithm used for active noise control (ANC system, this paper proposed and studied two adaptive algorithms based on Wavelet. The twos are applied to a noise control system including magnetorheological elastomers (MRE, which is a smart viscoelastic material characterized by a complex modulus dependent on vibration frequency and controllable by external magnetic fields. Simulation results reveal that the Decomposition LMS algorithm (D-LMS and Decomposition and Reconstruction LMS algorithm (DR-LMS based on Wavelet can significantly improve the noise reduction performance of MRE control system compared with traditional LMS algorithm.

The product composition control system at Savannah River: Statistical process control algorithm

International Nuclear Information System (INIS)

Brown, K.G.

1994-01-01

The Defense Waste Processing Facility (DWPF) at the Savannah River Site (SRS) will be used to immobilize the approximately 130 million liters of high-level nuclear waste currently stored at the site in 51 carbon steel tanks. Waste handling operations separate this waste into highly radioactive insoluble sludge and precipitate and less radioactive water soluble salts. In DWPF, precipitate (PHA) is blended with insoluble sludge and ground glass frit to produce melter feed slurry which is continuously fed to the DWPF melter. The melter produces a molten borosilicate glass which is poured into stainless steel canisters for cooling and, ultimately, shipment to and storage in an geologic repository. Described here is the Product Composition Control System (PCCS) process control algorithm. The PCCS is the amalgam of computer hardware and software intended to ensure that the melt will be processable and that the glass wasteform produced will be acceptable. Within PCCS, the Statistical Process Control (SPC) Algorithm is the means which guides control of the DWPF process. The SPC Algorithm is necessary to control the multivariate DWPF process in the face of uncertainties arising from the process, its feeds, sampling, modeling, and measurement systems. This article describes the functions performed by the SPC Algorithm, characterization of DWPF prior to making product, accounting for prediction uncertainty, accounting for measurement uncertainty, monitoring a SME batch, incorporating process information, and advantages of the algorithm. 9 refs., 6 figs

Multi-Working Modes Product-Color Planning Based on Evolutionary Algorithms and Swarm Intelligence

Directory of Open Access Journals (Sweden)

Man Ding

2010-01-01

Full Text Available In order to assist designer in color planning during product development, a novel synthesized evaluation method is presented to evaluate color-combination schemes of multi-working modes products (MMPs. The proposed evaluation method considers color-combination images in different working modes as evaluating attributes, to which the corresponding weights are assigned for synthesized evaluation. Then a mathematical model is developed to search for optimal color-combination schemes of MMP based on the proposed evaluation method and two powerful search techniques known as Evolution Algorithms (EAs and Swarm Intelligence (SI. In the experiments, we present a comparative study for two EAs, namely, Genetic Algorithm (GA and Difference Evolution (DE, and one SI algorithm, namely, Particle Swarm Optimization (PSO, on searching for color-combination schemes of MMP problem. All of the algorithms are evaluated against a test scenario, namely, an Arm-type aerial work platform, which has two working modes. The results show that the DE obtains the superior solution than the other two algorithms for color-combination scheme searching problem in terms of optimization accuracy and computation robustness. Simulation results demonstrate that the proposed method is feasible and efficient.

B ampersand W PWR advanced control system algorithm development

International Nuclear Information System (INIS)

Winks, R.W.; Wilson, T.L.; Amick, M.

1992-01-01

This paper discusses algorithm development of an Advanced Control System for the B ampersand W Pressurized Water Reactor (PWR) nuclear power plant. The paper summarizes the history of the project, describes the operation of the algorithm, and presents transient results from a simulation of the plant and control system. The history discusses the steps in the development process and the roles played by the utility owners, B ampersand W Nuclear Service Company (BWNS), Oak Ridge National Laboratory (ORNL), and the Foxboro Company. The algorithm description is a brief overview of the features of the control system. The transient results show that operation of the algorithm in a normal power maneuvering mode and in a moderately large upset following a feedwater pump trip

Design of SVC Controller Based on Improved Biogeography-Based Optimization Algorithm

Directory of Open Access Journals (Sweden)

Feifei Dong

2014-01-01

Full Text Available Considering that common subsynchronous resonance controllers cannot adapt to the characteristics of the time-varying and nonlinear behavior of a power system, the cosine migration model, the improved migration operator, and the mutative scale of chaos and Cauchy mutation strategy are introduced into an improved biogeography-based optimization (IBBO algorithm in order to design an optimal subsynchronous damping controller based on the mechanism of suppressing SSR by static var compensator (SVC. The effectiveness of the improved controller is verified by eigenvalue analysis and electromagnetic simulations. The simulation results of Jinjie plant indicate that the subsynchronous damping controller optimized by the IBBO algorithm can remarkably improve the damping of torsional modes and thus effectively depress SSR, and ensure the safety and stability of units and power grid operation. Moreover, the IBBO algorithm has the merits of a faster searching speed and higher searching accuracy in seeking the optimal control parameters over traditional algorithms, such as BBO algorithm, PSO algorithm, and GA algorithm.

ROBUST CONTROL ALGORITHM FOR MULTIVARIABLE PLANTS WITH QUANTIZED OUTPUT

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A. A. Margun

2017-01-01

Full Text Available The paper deals with robust output control algorithm for multivariable plants under disturbances. A plant is described by the system of linear differential equations with known relative degrees. Plant parameters are unknown but belong to the known closed bounded set. Plant state vector is unmeasured. Plant output is measured only via static quantizer. Control system algorithm is based on the high gain feedback method. Developed controller provides exponential convergence of tracking error to the bounded area. The area bounds depend on quantizer parameters and the value of external disturbances. Experimental approbation of the proposed control algorithm is performed with the use of Twin Rotor MIMO System laboratory bench. This bench is a helicopter like model with two degrees of freedom (pitch and yaw. DC motors are used as actuators. The output signals are measured via optical encoders. Mathematical model of laboratory bench is obtained. Proposed algorithm was compared with proportional - integral – differential controller in conditions of output quantization. Obtained results have confirmed the efficiency of proposed controller.

Fractional Order Controller Designing with Firefly Algorithm and Parameter Optimization for Hydroturbine Governing System

Directory of Open Access Journals (Sweden)

Li Junyi

2015-01-01

Full Text Available A fractional order PID (FOPID controller, which is suitable for control system designing for being insensitive to the variation in system parameter, is proposed for hydroturbine governing system in the paper. The simultaneous optimization for several parameters of controller, that is, Ki, Kd, Kp, λ, and μ, is done by a recently developed metaheuristic nature-inspired algorithm, namely, the firefly algorithm (FA, for the first time, where the selecting, moving, attractiveness behavior between fireflies and updating of brightness, and decision range are studied in detail to simulate the optimization process. Investigation clearly reveals the advantages of the FOPID controller over the integer controllers in terms of reduced oscillations and settling time. The present work also explores the superiority of FA based optimization technique in finding optimal parameters of the controller. Further, convergence characteristics of the FA are compared with optimum integer order PID (IOPID controller to justify its efficiency. What is more, analysis confirms the robustness of FOPID controller under isolated load operation conditions.

Photovoltaic Cells Mppt Algorithm and Design of Controller Monitoring System

Science.gov (United States)

Meng, X. Z.; Feng, H. B.

2017-10-01

This paper combined the advantages of each maximum power point tracking (MPPT) algorithm, put forward a kind of algorithm with higher speed and higher precision, based on this algorithm designed a maximum power point tracking controller with ARM. The controller, communication technology and PC software formed a control system. Results of the simulation and experiment showed that the process of maximum power tracking was effective, and the system was stable.

Constant-work-space algorithms for geometric problems

Directory of Open Access Journals (Sweden)

Tetsuo Asano

2011-07-01

Full Text Available Constant-work-space algorithms may use only constantly many cells of storage in addition to their input, which is provided as a read-only array. We show how to construct several geometric structures efficiently in the constant-work-space model. Traditional algorithms process the input into a suitable data structure (like a doubly-connected edge list that allows efficient traversal of the structure at hand. In the constant-work-space setting, however, we cannot afford to do this. Instead, we provide operations that compute the desired features on the fly by accessing the input with no extra space. The whole geometric structure can be obtained by using these operations to enumerate all the features. Of course, we must pay for the space savings by slower running times. While the standard data structure allows us to implement traversal operations in constant time, our schemes typically take linear time to read the input data in each step.We begin with two simple problems: triangulating a planar point set and finding the trapezoidal decomposition of a simple polygon. In both cases adjacent features can be enumerated in linear time per step, resulting in total quadratic running time to output the whole structure. Actually, we show that the former result carries over to the Delaunay triangulation, and hence the Voronoi diagram. This also means that we can compute the largest empty circle of a planar point set in quadratic time and constant work-space. As another application, we demonstrate how to enumerate the features of an Euclidean minimum spanning tree (EMST in quadratic time per step, so that the whole EMST can be found in cubic time using constant work-space.Finally, we describe how to compute a shortest geodesic path between two points in a simple polygon. Although the shortest path problem in general graphs is NL-complete (Jakoby and Tantau 2003, this constrained problem can be solved in quadratic time using only constant work-space.

Control algorithms for autonomous robot navigation

International Nuclear Information System (INIS)

Jorgensen, C.C.

1985-01-01

This paper examines control algorithm requirements for autonomous robot navigation outside laboratory environments. Three aspects of navigation are considered: navigation control in explored terrain, environment interactions with robot sensors, and navigation control in unanticipated situations. Major navigation methods are presented and relevance of traditional human learning theory is discussed. A new navigation technique linking graph theory and incidental learning is introduced

Application of epidemic algorithms for smart grids control

International Nuclear Information System (INIS)

Krkoleva, Aleksandra

2012-01-01

Smart Grids are a new concept for electricity networks development, aiming to provide economically efficient and sustainable power system by integrating effectively the actions and needs of the network users. The thesis addresses the Smart Grids concept, with emphasis on the control strategies developed on the basis of epidemic algorithms, more specifically, gossip algorithms. The thesis is developed around three Smart grid aspects: the changed role of consumers in terms of taking part in providing services within Smart Grids; the possibilities to implement decentralized control strategies based on distributed algorithms; and information exchange and benefits emerging from implementation of information and communication technologies. More specifically, the thesis presents a novel approach for providing ancillary services by implementing gossip algorithms. In a decentralized manner, by exchange of information between the consumers and by making decisions on local level, based on the received information and local parameters, the group achieves its global objective, i. e. providing ancillary services. The thesis presents an overview of the Smart Grids control strategies with emphasises on new strategies developed for the most promising Smart Grids concepts, as Micro grids and Virtual power plants. The thesis also presents the characteristics of epidemic algorithms and possibilities for their implementation in Smart Grids. Based on the research on epidemic algorithms, two applications have been developed. These applications are the main outcome of the research. The first application enables consumers, represented by their commercial aggregators, to participate in load reduction and consequently, to participate in balancing market or reduce the balancing costs of the group. In this context, the gossip algorithms are used for aggregator's message dissemination for load reduction and households and small commercial and industrial consumers to participate in maintaining

CAS algorithm-based optimum design of PID controller in AVR system

International Nuclear Information System (INIS)

Zhu Hui; Li Lixiang; Zhao Ying; Guo Yu; Yang Yixian

2009-01-01

This paper presents a novel design method for determining the optimal PID controller parameters of an automatic voltage regulator (AVR) system using the chaotic ant swarm (CAS) algorithm. In the tuning process of parameters, the CAS algorithm is iterated to give the optimal parameters of the PID controller based on the fitness theory, where the position vector of each ant in the CAS algorithm corresponds to the parameter vector of the PID controller. The proposed CAS-PID controllers can ensure better control system performance with respect to the reference input in comparison with GA-PID controllers. Numerical simulations are provided to verify the effectiveness and feasibility of PID controller based on CAS algorithm.

PID controller tuning using metaheuristic optimization algorithms for benchmark problems

Science.gov (United States)

Gholap, Vishal; Naik Dessai, Chaitali; Bagyaveereswaran, V.

2017-11-01

This paper contributes to find the optimal PID controller parameters using particle swarm optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The algorithms were developed through simulation of chemical process and electrical system and the PID controller is tuned. Here, two different fitness functions such as Integral Time Absolute Error and Time domain Specifications were chosen and applied on PSO, GA and SA while tuning the controller. The proposed Algorithms are implemented on two benchmark problems of coupled tank system and DC motor. Finally, comparative study has been done with different algorithms based on best cost, number of iterations and different objective functions. The closed loop process response for each set of tuned parameters is plotted for each system with each fitness function.

Genetic Algorithm Optimizes Q-LAW Control Parameters

Science.gov (United States)

Lee, Seungwon; von Allmen, Paul; Petropoulos, Anastassios; Terrile, Richard

2008-01-01

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto-optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution. Q-law control parameters are represented as real-valued genes in the genetic algorithm. The performances of the Q-law control parameters are evaluated in the multi-objective space (flight time vs. propellant mass) and sorted by the non-dominated sorting method that assigns a better fitness value to the solutions that are dominated by a fewer number of other solutions. With the ranking result, the genetic algorithm encourages the solutions with higher fitness values to participate in the reproduction process, improving the solutions in the evolution process. The population of solutions converges to the Pareto front that is permitted within the Q-law control parameter space.

Development of an On-Line Self-Tuning FPGA-PID-PWM Control Algorithm Design for DC-DC Buck Converter in Mobile Applications

Directory of Open Access Journals (Sweden)

Ahmed Sabah Al-Araji

2017-08-01

Full Text Available This paper presents a new development of an on-line hybrid self-tuning control algorithm of the Field Programmable Gate Array - Proportional Integral Derivative - Pulse Width Modulation (FPGA-PID-PWM controller for DC-DC buck converter which is used in battery operation of mobile applications. The main goal in this work is to propose structure of the hybrid Bees-PSO tuning control algorithm which has a capability of quickly and precisely searching in the global regions in order to obtain optimal gain parameters for the proposed controller to generate the best voltage control action to achieve the desired performance of the Buck converter output. Matlab simulation results and Xilinx development tool Integrated Software Environment (ISE experimental work show the robustness and effectiveness of the proposed on-line hybrid Bees-PSO tuning control algorithm in terms of obtaining smooth and unsaturated state voltage control action and minimizing the tracking voltage error of the Buck converter output. Moreover, the fitness evaluation number is reduced.

Configuration-defined control algorithms with the ASDEX Upgrade DCS

Energy Technology Data Exchange (ETDEWEB)

Treutterer, Wolfgang, E-mail: Wolfgang.Treutterer@ipp.mpg.de [Max-Planck-Institut für Plasmaphysik, Boltzmannstr. 2, 85748 Garching (Germany); Cole, Richard [Unlimited Computer Systems, Seeshaupter Str. 15, 82393 Iffeldorf Germany (Germany); Gräter, Alexander [Max-Planck-Institut für Plasmaphysik, Boltzmannstr. 2, 85748 Garching (Germany); Lüddecke, Klaus [Unlimited Computer Systems, Seeshaupter Str. 15, 82393 Iffeldorf Germany (Germany); Neu, Gregor; Rapson, Christopher; Raupp, Gerhard; Zehetbauer, Thomas [Max-Planck-Institut für Plasmaphysik, Boltzmannstr. 2, 85748 Garching (Germany)

2016-11-15

Highlights: • Control algorithm built from combination of pre-fabricated standard function blocks. • Seamless integration in multi-threaded computation context. • Block composition defined by configuration data, only. - Abstract: The ASDEX Upgrade Discharge Control System (DCS) is a distributed real-time control system executing complex control and monitoring tasks. Up to now, DCS control algorithms have been implemented by coding dedicated application processes with the C++ programming language. Algorithm changes required code modification, compilation and commissioning which only experienced programmers could perform. This was a significant constraint of flexibility for both control system operation and design. The new approach extends DCS with the capability of configuration-defined control algorithms. These are composed of chains of small, configurable standard function blocks providing general purpose functions like algebraic operations, filters, feedback controllers, output limiters and decision logic. In a later phase a graphical editor could help to compose and modify such configuration in a Simulink-like fashion. Building algorithms from standard functions can result in a high number of elements. In order to achieve a similar performance as with C++ coding, it is essential to avoid administrative bottlenecks by design. As a consequence, DCS executes a function block chain in the context of a single real-time thread of an application process. No concurrency issues as in a multi-threaded context need to be considered resulting in strongly simplified signal handling and zero performance overhead for inter-block communication. Instead of signal-driven synchronization, a block scheduler derives the execution sequence automatically from the block dependencies as defined in the configuration. All blocks and connecting signals are instantiated dynamically, based on definitions in a configuration file. Algorithms thus are not defined in the code but only in

Maintenance of Process Control Algorithms based on Dynamic Program Slicing

DEFF Research Database (Denmark)

Hansen, Ole Fink; Andersen, Nils Axel; Ravn, Ole

2010-01-01

Today’s industrial control systems gradually lose performance after installation and must be regularly maintained by means of adjusting parameters and modifying the control algorithm, in order to regain high performance. Industrial control algorithms are complex software systems, and it is partic...

Generalized algorithm for control of numerical dispersion in explicit time-domain electromagnetic simulations

Directory of Open Access Journals (Sweden)

Benjamin M. Cowan

2013-04-01

Full Text Available We describe a modification to the finite-difference time-domain algorithm for electromagnetics on a Cartesian grid which eliminates numerical dispersion error in vacuum for waves propagating along a grid axis. We provide details of the algorithm, which generalizes previous work by allowing 3D operation with a wide choice of aspect ratio, and give conditions to eliminate dispersive errors along one or more of the coordinate axes. We discuss the algorithm in the context of laser-plasma acceleration simulation, showing significant reduction—up to a factor of 280, at a plasma density of 10^{23}  m^{-3}—of the dispersion error of a linear laser pulse in a plasma channel. We then compare the new algorithm with the standard electromagnetic update for laser-plasma accelerator stage simulations, demonstrating that by controlling numerical dispersion, the new algorithm allows more accurate simulation than is otherwise obtained. We also show that the algorithm can be used to overcome the critical but difficult challenge of consistent initialization of a relativistic particle beam and its fields in an accelerator simulation.

Controller Parameter Optimization for Nonlinear Systems Using Enhanced Bacteria Foraging Algorithm

Directory of Open Access Journals (Sweden)

V. Rajinikanth

2012-01-01

Full Text Available An enhanced bacteria foraging optimization (EBFO algorithm-based Proportional + integral + derivative (PID controller tuning is proposed for a class of nonlinear process models. The EBFO algorithm is a modified form of standard BFO algorithm. A multiobjective performance index is considered to guide the EBFO algorithm for discovering the best possible value of controller parameters. The efficiency of the proposed scheme has been validated through a comparative study with classical BFO, adaptive BFO, PSO, and GA based controller tuning methods proposed in the literature. The proposed algorithm is tested in real time on a nonlinear spherical tank system. The real-time results show that, EBFO tuned PID controller gives a smooth response for setpoint tracking performance.

Model-Free Adaptive Control Algorithm with Data Dropout Compensation

Directory of Open Access Journals (Sweden)

Xuhui Bu

2012-01-01

Full Text Available The convergence of model-free adaptive control (MFAC algorithm can be guaranteed when the system is subject to measurement data dropout. The system output convergent speed gets slower as dropout rate increases. This paper proposes a MFAC algorithm with data compensation. The missing data is first estimated using the dynamical linearization method, and then the estimated value is introduced to update control input. The convergence analysis of the proposed MFAC algorithm is given, and the effectiveness is also validated by simulations. It is shown that the proposed algorithm can compensate the effect of the data dropout, and the better output performance can be obtained.

DOOCS environment for FPGA-based cavity control system and control algorithms development

International Nuclear Information System (INIS)

Pucyk, P.; Koprek, W.; Kaleta, P.; Szewinski, J.; Pozniak, K.T.; Czarski, T.; Romaniuk, R.S.

2005-01-01

The paper describes the concept and realization of the DOOCS control software for FPGAbased TESLA cavity controller and simulator (SIMCON). It bases on universal software components, created for laboratory purposes and used in MATLAB based control environment. These modules have been recently adapted to the DOOCS environment to ensure a unified software to hardware communication model. The presented solution can be also used as a general platform for control algorithms development. The proposed interfaces between MATLAB and DOOCS modules allow to check the developed algorithm in the operation environment before implementation in the FPGA. As the examples two systems have been presented. (orig.)

Algorithm for Public Electric Transport Schedule Control for Intelligent Embedded Devices

Science.gov (United States)

Alps, Ivars; Potapov, Andrey; Gorobetz, Mikhail; Levchenkov, Anatoly

2010-01-01

In this paper authors present heuristics algorithm for precise schedule fulfilment in city traffic conditions taking in account traffic lights. The algorithm is proposed for programmable controller. PLC is proposed to be installed in electric vehicle to control its motion speed and signals of traffic lights. Algorithm is tested using real controller connected to virtual devices and real functional models of real tram devices. Results of experiments show high precision of public transport schedule fulfilment using proposed algorithm.

How Algorithms Inscribe the Understanding of Crime in Police Work

NARCIS (Netherlands)

Waardenburg, L.; Sergeeva, A.; Huysman, Marleen

2018-01-01

This research focuses on the consequences of the shift to data-driven work for daily police work. Our ongoing ethnographic field study of a team of police officers shows that predictive policing algorithms inscribe a different crime theory-in-use – i.e., the understanding of why crime occurs and how

Secondary Coordinated Control of Islanded Microgrids Based on Consensus Algorithms

DEFF Research Database (Denmark)

Wu, Dan; Dragicevic, Tomislav; Vasquez, Juan Carlos

2014-01-01

systems. Nevertheless, the conventional decentralized secondary control, although does not need to be implemented in a microgrid central controller (MGCC), it has the limitation that all decentralized controllers must be mutually synchronized. In a clear cut contrast, the proposed secondary control......This paper proposes a decentralized secondary control for islanded microgrids based on consensus algorithms. In a microgrid, the secondary control is implemented in order to eliminate the frequency changes caused by the primary control when coordinating renewable energy sources and energy storage...... requires only a more simplified communication protocol and a sparse communication network. Moreover, the proposed approach based on dynamic consensus algorithms is able to achieve the coordinated secondary performance even when all units are initially out-of-synchronism. The control algorithm implemented...

Fuzzy gain scheduling of velocity PI controller with intelligent learning algorithm for reactor control

International Nuclear Information System (INIS)

Kim, Dong Yun; Seong, Poong Hyun

1996-01-01

In this study, we proposed a fuzzy gain scheduler with intelligent learning algorithm for a reactor control. In the proposed algorithm, we used the gradient descent method to learn the rule bases of a fuzzy algorithm. These rule bases are learned toward minimizing an objective function, which is called a performance cost function. The objective of fuzzy gain scheduler with intelligent learning algorithm is the generation of adequate gains, which minimize the error of system. The condition of every plant is generally changed as time gose. That is, the initial gains obtained through the analysis of system are no longer suitable for the changed plant. And we need to set new gains, which minimize the error stemmed from changing the condition of a plant. In this paper, we applied this strategy for reactor control of nuclear power plant (NPP), and the results were compared with those of a simple PI controller, which has fixed gains. As a result, it was shown that the proposed algorithm was superior to the simple PI controller

A Design of a Hybrid Non-Linear Control Algorithm

Directory of Open Access Journals (Sweden)

Farinaz Behrooz

2017-11-01

Full Text Available One of the high energy consuming devices in the buildings is the air-conditioning system. Designing a proper controller to consider the thermal comfort and simultaneously control the energy usage of the device will impact on the system energy efficiency and its performance. The aim of this study was to design a Multiple-Input and Multiple-Output (MIMO, non-linear, and intelligent controller on direct expansion air-conditioning system The control algorithm uses the Fuzzy Cognitive Map method as a main controller and the Generalized Predictive Control method is used for assigning the initial weights of the main controller. The results of the proposed controller shows that the controller was successfully designed and works in set point tracking and under disturbance rejection tests. The obtained results of the Generalized Predictive Control-Fuzzy Cognitive Map controller are compared with the previous MIMO Linear Quadratic Gaussian control design on the same direct expansion air-conditioning system under the same conditions. The comparative results indicate energy savings would be achieved with the proposed controller with long-term usage. Energy efficiency and thermal comfort conditions are achieved by the proposed controller.

SVC control enhancement applying self-learning fuzzy algorithm for islanded microgrid

Directory of Open Access Journals (Sweden)

Hossam Gabbar

2016-03-01

Full Text Available Maintaining voltage stability, within acceptable levels, for islanded Microgrids (MGs is a challenge due to limited exchange power between generation and loads. This paper proposes an algorithm to enhance the dynamic performance of islanded MGs in presence of load disturbance using Static VAR Compensator (SVC with Fuzzy Model Reference Learning Controller (FMRLC. The proposed algorithm compensates MG nonlinearity via fuzzy membership functions and inference mechanism imbedded in both controller and inverse model. Hence, MG keeps the desired performance as required at any operating condition. Furthermore, the self-learning capability of the proposed control algorithm compensates for grid parameter’s variation even with inadequate information about load dynamics. A reference model was designed to reject bus voltage disturbance with achievable performance by the proposed fuzzy controller. Three simulations scenarios have been presented to investigate effectiveness of proposed control algorithm in improving steady-state and transient performance of islanded MGs. The first scenario conducted without SVC, second conducted with SVC using PID controller and third conducted using FMRLC algorithm. A comparison for results shows ability of proposed control algorithm to enhance disturbance rejection due to learning process.

CHAM: weak signals detection through a new multivariate algorithm for process control

Science.gov (United States)

Bergeret, François; Soual, Carole; Le Gratiet, B.

2016-10-01

Derivatives technologies based on core CMOS processes are significantly aggressive in term of design rules and process control requirements. Process control plan is a derived from Process Assumption (PA) calculations which result in a design rule based on known process variability capabilities, taking into account enough margin to be safe not only for yield but especially for reliability. Even though process assumptions are calculated with a 4 sigma known process capability margin, efficient and competitive designs are challenging the process especially for derivatives technologies in 40 and 28nm nodes. For wafer fab process control, PA are declined in monovariate (layer1 CD, layer2 CD, layer2 to layer1 overlay, layer3 CD etc….) control charts with appropriated specifications and control limits which all together are securing the silicon. This is so far working fine but such system is not really sensitive to weak signals coming from interactions of multiple key parameters (high layer2 CD combined with high layer3 CD as an example). CHAM is a software using an advanced statistical algorithm specifically designed to detect small signals, especially when there are many parameters to control and when the parameters can interact to create yield issues. In this presentation we will first present the CHAM algorithm, then the case-study on critical dimensions, with the results, and we will conclude on future work. This partnership between Ippon and STM is part of E450LMDAP, European project dedicated to metrology and lithography development for future technology nodes, especially 10nm.

Design and simulation of airport congestion control algorithms

OpenAIRE

Simaiakis, Ioannis; Balakrishnan, Hamsa

2014-01-01

This paper proposes a stochastic model of runway departures and a dynamic programming algorithm for their control at congested airports. Using a multi-variable state description that includes the capacity forecast, the runway system is modeled as a semi-Markov process. The paper then introduces a queuing system for modeling the controlled departure process that enables the efficient calculation of optimal pushback policies using decomposition techniques. The developed algorithm is simulated a...

Fuzzy algorithm for an automatic reactor power control in a PWR

International Nuclear Information System (INIS)

Hah, Yung Joon; Song, In Ho; Yu, Sung Sik; Choi, Jung In; Lee, Byong Whi

1994-01-01

A fuzzy algorithm is presented for automatic reactor power control in a pressurized water reactor. Automatic power shape control is complicated by the use of control rods because it is highly coupled with reactivity compensation. Thus, manual shape controls are usually employed even for the limited capability for the load - follow operation including frequency control. In an attempt to achieve automatic power shape control without any design modification of the core, a fuzzy power control algorithm is proposed. For the fuzzy control, the rule base is formulated based on a multi - input multi - output system. The minimum operation rule and the center of area method are implemented for the development of the fuzzy algorithm. The fuzzy power control algorithm has been applied to the Yonggwang Nuclear Unit 3. The simulation results show that the fuzzy control can be adapted as a practical control strategy for automatic reactor power control of the pressurized water reactor during the load - follow operation

A chaos-based image encryption algorithm with variable control parameters

International Nuclear Information System (INIS)

Wang Yong; Wong, K.-W.; Liao Xiaofeng; Xiang Tao; Chen Guanrong

2009-01-01

In recent years, a number of image encryption algorithms based on the permutation-diffusion structure have been proposed. However, the control parameters used in the permutation stage are usually fixed in the whole encryption process, which favors attacks. In this paper, a chaos-based image encryption algorithm with variable control parameters is proposed. The control parameters used in the permutation stage and the keystream employed in the diffusion stage are generated from two chaotic maps related to the plain-image. As a result, the algorithm can effectively resist all known attacks against permutation-diffusion architectures. Theoretical analyses and computer simulations both confirm that the new algorithm possesses high security and fast encryption speed for practical image encryption.

VLSI PARTITIONING ALGORITHM WITH ADAPTIVE CONTROL PARAMETER

Directory of Open Access Journals (Sweden)

P. N. Filippenko

2013-03-01

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.

The research of automatic speed control algorithm based on Green CBTC

Science.gov (United States)

Lin, Ying; Xiong, Hui; Wang, Xiaoliang; Wu, Youyou; Zhang, Chuanqi

2017-06-01

Automatic speed control algorithm is one of the core technologies of train operation control system. It’s a typical multi-objective optimization control algorithm, which achieve the train speed control for timing, comfort, energy-saving and precise parking. At present, the train speed automatic control technology is widely used in metro and inter-city railways. It has been found that the automatic speed control technology can effectively reduce the driver’s intensity, and improve the operation quality. However, the current used algorithm is poor at energy-saving, even not as good as manual driving. In order to solve the problem of energy-saving, this paper proposes an automatic speed control algorithm based on Green CBTC system. Based on the Green CBTC system, the algorithm can adjust the operation status of the train to improve the efficient using rate of regenerative braking feedback energy while ensuring the timing, comfort and precise parking targets. Due to the reason, the energy-using of Green CBTC system is lower than traditional CBTC system. The simulation results show that the algorithm based on Green CBTC system can effectively reduce the energy-using due to the improvement of the using rate of regenerative braking feedback energy.

Immune algorithm based active PID control for structure systems

International Nuclear Information System (INIS)

Lee, Young Jin; Cho, Hyun Cheol; Lee, Kwon Soon

2006-01-01

An immune algorithm is a kind of evolutional computation strategies, which is developed in the basis of a real immune mechanism in the human body. Recently, scientific or engineering applications using this scheme are remarkably increased due to its significant ability in terms of adaptation and robustness for external disturbances. Particularly, this algorithm is efficient to search optimal parameters against complicated dynamic systems with uncertainty and perturbation. In this paper, we investigate an immune algorithm embedded Proportional Integral Derivate (called I P ID) control, in which an optimal parameter vector of the controller is determined offline by using a cell-mediated immune response of the immunized mechanism. For evaluation, we apply the proposed control to mitigation of vibrations for nonlinear structural systems, cased by external environment load such as winds and earthquakes. Comparing to traditional controls under same simulation scenarios, we demonstrate the innovation control is superior especially in robustness aspect

An improved cooperative adaptive cruise control (CACC) algorithm considering invalid communication

Science.gov (United States)

Wang, Pangwei; Wang, Yunpeng; Yu, Guizhen; Tang, Tieqiao

2014-05-01

For the Cooperative Adaptive Cruise Control (CACC) Algorithm, existing research studies mainly focus on how inter-vehicle communication can be used to develop CACC controller, the influence of the communication delays and lags of the actuators to the string stability. However, whether the string stability can be guaranteed when inter-vehicle communication is invalid partially has hardly been considered. This paper presents an improved CACC algorithm based on the sliding mode control theory and analyses the range of CACC controller parameters to maintain string stability. A dynamic model of vehicle spacing deviation in a platoon is then established, and the string stability conditions under improved CACC are analyzed. Unlike the traditional CACC algorithms, the proposed algorithm can ensure the functionality of the CACC system even if inter-vehicle communication is partially invalid. Finally, this paper establishes a platoon of five vehicles to simulate the improved CACC algorithm in MATLAB/Simulink, and the simulation results demonstrate that the improved CACC algorithm can maintain the string stability of a CACC platoon through adjusting the controller parameters and enlarging the spacing to prevent accidents. With guaranteed string stability, the proposed CACC algorithm can prevent oscillation of vehicle spacing and reduce chain collision accidents under real-world circumstances. This research proposes an improved CACC algorithm, which can guarantee the string stability when inter-vehicle communication is invalid.

Algorithms as fetish: Faith and possibility in algorithmic work

Directory of Open Access Journals (Sweden)

Suzanne L Thomas

2018-01-01

Full Text Available Algorithms are powerful because we invest in them the power to do things. With such promise, they can transform the ordinary, say snapshots along a robotic vacuum cleaner’s route, into something much more, such as a clean home. Echoing David Graeber’s revision of fetishism, we argue that this easy slip from technical capabilities to broader claims betrays not the “magic” of algorithms but rather the dynamics of their exchange. Fetishes are not indicators of false thinking, but social contracts in material form. They mediate emerging distributions of power often too nascent, too slippery or too disconcerting to directly acknowledge. Drawing primarily on 2016 ethnographic research with computer vision professionals, we show how faith in what algorithms can do shapes the social encounters and exchanges of their production. By analyzing algorithms through the lens of fetishism, we can see the social and economic investment in some people’s labor over others. We also see everyday opportunities for social creativity and change. We conclude that what is problematic about algorithms is not their fetishization but instead their stabilization into full-fledged gods and demons – the more deserving objects of critique.

Control of baker’s yeast fermentation : PID and fuzzy algorithms

OpenAIRE

Machado, Carlos; Gomes, Pedro; Soares, Rui; Pereira, Silvia; Soares, Filomena

2001-01-01

A MATLAB/SIMULINK-based simulator was employed for studies concerning the control of baker’s yeast fed-batch fermentation. Four control algorithms were implemented and compared: the classical PID control, two discrete versions- modified velocity and position algorithms, and a fuzzy law. The simulation package was seen to be an efficient tool for the simulation and tests of control strategies of the non-linear process.

Design of PID Controller Simulator based on Genetic Algorithm

Directory of Open Access Journals (Sweden)

Fahri VATANSEVER

2013-08-01

Full Text Available PID (Proportional Integral and Derivative controllers take an important place in the field of system controlling. Various methods such as Ziegler-Nichols, Cohen-Coon, Chien Hrones Reswick (CHR and Wang-Juang-Chan are available for the design of such controllers benefiting from the system time and frequency domain data. These controllers are in compliance with system properties under certain criteria suitable to the system. Genetic algorithms have become widely used in control system applications in parallel to the advances in the field of computer and artificial intelligence. In this study, PID controller designs have been carried out by means of classical methods and genetic algorithms and comparative results have been analyzed. For this purpose, a graphical user interface program which can be used for educational purpose has been developed. For the definite (entered transfer functions, the suitable P, PI and PID controller coefficients have calculated by both classical methods and genetic algorithms and many parameters and responses of the systems have been compared and presented numerically and graphically

Packet-Based Control Algorithms for Cooperative Surveillance and Reconnaissance

National Research Council Canada - National Science Library

Murray, Richard M

2007-01-01

..., and repeated transmissions. Results include analysis and design of estimation and control algorithms in the presence of packet loss and across multi-hop data networks, distributed estimation and sensor fusion algorithms...

Stall Recovery Guidance Algorithms Based on Constrained Control Approaches

Science.gov (United States)

Stepanyan, Vahram; Krishnakumar, Kalmanje; Kaneshige, John; Acosta, Diana

2016-01-01

Aircraft loss-of-control, in particular approach to stall or fully developed stall, is a major factor contributing to aircraft safety risks, which emphasizes the need to develop algorithms that are capable of assisting the pilots to identify the problem and providing guidance to recover the aircraft. In this paper we present several stall recovery guidance algorithms, which are implemented in the background without interfering with flight control system and altering the pilot's actions. They are using input and state constrained control methods to generate guidance signals, which are provided to the pilot in the form of visual cues. It is the pilot's decision to follow these signals. The algorithms are validated in the pilot-in-the loop medium fidelity simulation experiment.

A guidance and control algorithm for scent tracking micro-robotic vehicle swarms

International Nuclear Information System (INIS)

Dohner, J.L.

1998-03-01

Cooperative micro-robotic scent tracking vehicles are designed to collectively sniff out locations of high scent concentrations in unknown, geometrically complex environments. These vehicles are programmed with guidance and control algorithms that allow inter cooperation among vehicles. In this paper a cooperative guidance and control algorithm for scent tracking micro-robotic vehicles is presented. This algorithm is comprised of a sensory compensation sub-algorithm using point source cancellation, a guidance sub-algorithm using gradient descent tracking, and a control sub-algorithm using proportional feedback. The concepts of social rank and point source cancellation are new concepts introduced within. Simulation results for cooperative vehicles swarms are given. Limitations are discussed

A guidance and control algorithm for scent tracking micro-robotic vehicle swarms

Energy Technology Data Exchange (ETDEWEB)

Dohner, J.L. [Sandia National Labs., Albuquerque, NM (United States). Structural Dynamics Dept.

1998-03-01

Cooperative micro-robotic scent tracking vehicles are designed to collectively sniff out locations of high scent concentrations in unknown, geometrically complex environments. These vehicles are programmed with guidance and control algorithms that allow inter cooperation among vehicles. In this paper a cooperative guidance and control algorithm for scent tracking micro-robotic vehicles is presented. This algorithm is comprised of a sensory compensation sub-algorithm using point source cancellation, a guidance sub-algorithm using gradient descent tracking, and a control sub-algorithm using proportional feedback. The concepts of social rank and point source cancellation are new concepts introduced within. Simulation results for cooperative vehicles swarms are given. Limitations are discussed.

Gradient algorithm applied to laboratory quantum control

International Nuclear Information System (INIS)

Roslund, Jonathan; Rabitz, Herschel

2009-01-01

The exploration of a quantum control landscape, which is the physical observable as a function of the control variables, is fundamental for understanding the ability to perform observable optimization in the laboratory. For high control variable dimensions, trajectory-based methods provide a means for performing such systematic explorations by exploiting the measured gradient of the observable with respect to the control variables. This paper presents a practical, robust, easily implemented statistical method for obtaining the gradient on a general quantum control landscape in the presence of noise. In order to demonstrate the method's utility, the experimentally measured gradient is utilized as input in steepest-ascent trajectories on the landscapes of three model quantum control problems: spectrally filtered and integrated second harmonic generation as well as excitation of atomic rubidium. The gradient algorithm achieves efficiency gains of up to approximately three times that of the standard genetic algorithm and, as such, is a promising tool for meeting quantum control optimization goals as well as landscape analyses. The landscape trajectories directed by the gradient should aid in the continued investigation and understanding of controlled quantum phenomena.

Development and implementation of an automatic control algorithm for the University of Utah nuclear reactor

International Nuclear Information System (INIS)

Crawford, Kevan C.; Sandquist, Gary M.

1990-01-01

The emphasis of this work is the development and implementation of an automatic control philosophy which uses the classical operational philosophies as a foundation. Three control algorithms were derived based on various simplifying assumptions. Two of the algorithms were tested in computer simulations. After realizing the insensitivity of the system to the simplifications, the most reduced form of the algorithms was implemented on the computer control system at the University of Utah (UNEL). Since the operational philosophies have a higher priority than automatic control, they determine when automatic control may be utilized. Unlike the operational philosophies, automatic control is not concerned with component failures. The object of this philosophy is the movement of absorber rods to produce a requested power. When the current power level is compared to the requested power level, an error may be detected which will require the movement of a control rod to correct the error. The automatic control philosophy adds another dimension to the classical operational philosophies. Using this philosophy, normal operator interactions with the computer would be limited only to run parameters such as power, period, and run time. This eliminates subjective judgements, objective judgements under pressure, and distractions to the operator and insures the reactor will be operated in a safe and controlled manner as well as providing reproducible operations

Tuning of PID Controllers for Quadcopter System using Cultural Exchange Imperialist Competitive Algorithm

Directory of Open Access Journals (Sweden)

Nizar Hadi Abbas

2018-02-01

Full Text Available Quadrotors are coming up as an attractive platform for unmanned aerial vehicle (UAV research, due to the simplicity of their structure and maintenance, their ability to hover, and their vertical take-off and landing (VTOL capability. With the vast advancements in small-size sensors, actuators, and processors, researchers are now focusing on developing mini UAV’s to be used in both research and commercial applications. This work presents a detailed mathematical nonlinear dynamic model of the quadrotor which is formulated using the Newton-Euler method. Although the quadrotor is a 6 DOF under-actuated system, the derived rotational subsystem is fully actuated, while the translational subsystem is under-actuated. The derivation of the mathematical model was followed by the development of the controller to control the altitude, attitude, heading and position of the quadrotor in space, which is, based on the linear Proportional-Derivative- Integral (PID controller; thus, a simplified version of the model is obtained. The gains of the controllers will be tuned using optimization techniques to improve the system's dynamic response. The standard Imperialist Competitive Algorithm (ICA was applied to tune the PID parameters and then it was compared to Cultural Exchange Imperialist Competitive algorithm (CEICA tuning, and the results show improvement in the proposed algorithm. The objective function results were enhanced by (23.91% in the CEICA compared with ICA.

Making the error-controlling algorithm of observable operator models constructive.

Science.gov (United States)

Zhao, Ming-Jie; Jaeger, Herbert; Thon, Michael

2009-12-01

Observable operator models (OOMs) are a class of models for stochastic processes that properly subsumes the class that can be modeled by finite-dimensional hidden Markov models (HMMs). One of the main advantages of OOMs over HMMs is that they admit asymptotically correct learning algorithms. A series of learning algorithms has been developed, with increasing computational and statistical efficiency, whose recent culmination was the error-controlling (EC) algorithm developed by the first author. The EC algorithm is an iterative, asymptotically correct algorithm that yields (and minimizes) an assured upper bound on the modeling error. The run time is faster by at least one order of magnitude than EM-based HMM learning algorithms and yields significantly more accurate models than the latter. Here we present a significant improvement of the EC algorithm: the constructive error-controlling (CEC) algorithm. CEC inherits from EC the main idea of minimizing an upper bound on the modeling error but is constructive where EC needs iterations. As a consequence, we obtain further gains in learning speed without loss in modeling accuracy.

Algorithms bio-inspired for the pattern obtention of control bars in BWR reactors

International Nuclear Information System (INIS)

Ortiz, J.J.; Perusquia, R.; Montes, J.L.

2003-01-01

In this work methods based on Genetic Algorithms and Systems based on ant colonies for the obtention of the patterns of control bars of an equilibrium cycle of 18 months for the Laguna Verde nuclear power station are presented. A comparison of obtained results with the methods and with those of design of such equilibrium cycle is presented. As consequence of the study, it was found that the algorithm based on the ant colonies reached to diminish the coast down period (decrease of power at the end of the cycle) in five and half days with respect to the original design what represents an annual saving of $US 100,000. (Author)

Advanced illumination control algorithm for medical endoscopy applications

Science.gov (United States)

Sousa, Ricardo M.; Wäny, Martin; Santos, Pedro; Morgado-Dias, F.

2015-05-01

CMOS image sensor manufacturer, AWAIBA, is providing the world's smallest digital camera modules to the world market for minimally invasive surgery and one time use endoscopic equipment. Based on the world's smallest digital camera head and the evaluation board provided to it, the aim of this paper is to demonstrate an advanced fast response dynamic control algorithm of the illumination LED source coupled to the camera head, over the LED drivers embedded on the evaluation board. Cost efficient and small size endoscopic camera modules nowadays embed minimal size image sensors capable of not only adjusting gain and exposure time but also LED illumination with adjustable illumination power. The LED illumination power has to be dynamically adjusted while navigating the endoscope over changing illumination conditions of several orders of magnitude within fractions of the second to guarantee a smooth viewing experience. The algorithm is centered on the pixel analysis of selected ROIs enabling it to dynamically adjust the illumination intensity based on the measured pixel saturation level. The control core was developed in VHDL and tested in a laboratory environment over changing light conditions. The obtained results show that it is capable of achieving correction speeds under 1 s while maintaining a static error below 3% relative to the total number of pixels on the image. The result of this work will allow the integration of millimeter sized high brightness LED sources on minimal form factor cameras enabling its use in endoscopic surgical robotic or micro invasive surgery.

PSO-RBF Neural Network PID Control Algorithm of Electric Gas Pressure Regulator

Directory of Open Access Journals (Sweden)

Yuanchang Zhong

2014-01-01

Full Text Available The current electric gas pressure regulator often adopts the conventional PID control algorithm to take drive control of the core part (micromotor of electric gas pressure regulator. In order to further improve tracking performance and to shorten response time, this paper presents an improved PID intelligent control algorithm which applies to the electric gas pressure regulator. The algorithm uses the improved RBF neural network based on PSO algorithm to make online adjustment on PID parameters. Theoretical analysis and simulation result show that the algorithm shortens the step response time and improves tracking performance.

Simulation, hardware implementation and control of a multilevel inverter with simulated annealing algorithm

Directory of Open Access Journals (Sweden)

Fayçal Chabni

2017-09-01

Full Text Available Harmonic pollution is a very common issue in the field of power electronics, Harmonics can cause multiple problems for power converters and electrical loads alike, this paper introduces a modulation method called selective harmonic elimination pulse width modulation (SHEPWM, this method allows the elimination of a specific order of harmonics and also control the amplitude of the fundamental component of the output voltage. In this work SHEPWM strategy is applied to a five level cascade inverter. The objective of this study is to demonstrate the total control provided by the SHEPWM strategy over any rank of harmonics using the simulated annealing optimization algorithm and also control the amplitude of the fundamental component at any desired value. Simulation and experimental results are presented in this work.

Applying Planning Algorithms to Argue in Cooperative Work

Science.gov (United States)

Monteserin, Ariel; Schiaffino, Silvia; Amandi, Analía

Negotiation is typically utilized in cooperative work scenarios for solving conflicts. Anticipating possible arguments in this negotiation step represents a key factor since we can take decisions about our participation in the cooperation process. In this context, we present a novel application of planning algorithms for argument generation, where the actions of a plan represent the arguments that a person might use during the argumentation process. In this way, we can plan how to persuade the other participants in cooperative work for reaching an expected agreement in terms of our interests. This approach allows us to take advantages since we can test anticipated argumentative solutions in advance.

A Traffic Prediction Algorithm for Street Lighting Control Efficiency

Directory of Open Access Journals (Sweden)

POPA Valentin

2013-01-01

Full Text Available This paper presents the development of a traffic prediction algorithm that can be integrated in a street lighting monitoring and control system. The prediction algorithm must enable the reduction of energy costs and improve energy efficiency by decreasing the light intensity depending on the traffic level. The algorithm analyses and processes the information received at the command center based on the traffic level at different moments. The data is collected by means of the Doppler vehicle detection sensors integrated within the system. Thus, two methods are used for the implementation of the algorithm: a neural network and a k-NN (k-Nearest Neighbor prediction algorithm. For 500 training cycles, the mean square error of the neural network is 9.766 and for 500.000 training cycles the error amounts to 0.877. In case of the k-NN algorithm the error increases from 8.24 for k=5 to 12.27 for a number of 50 neighbors. In terms of a root means square error parameter, the use of a neural network ensures the highest performance level and can be integrated in a street lighting control system.

PSO-Based Algorithm Applied to Quadcopter Micro Air Vehicle Controller Design

Directory of Open Access Journals (Sweden)

Huu-Khoa Tran

2016-09-01

Full Text Available Due to the rapid development of science and technology in recent times, many effective controllers are designed and applied successfully to complicated systems. The significant task of controller design is to determine optimized control gains in a short period of time. With this purpose in mind, a combination of the particle swarm optimization (PSO-based algorithm and the evolutionary programming (EP algorithm is introduced in this article. The benefit of this integration algorithm is the creation of new best-parameters for control design schemes. The proposed controller designs are then demonstrated to have the best performance for nonlinear micro air vehicle models.

A parallel adaptive quantum genetic algorithm for the controllability of arbitrary networks.

Science.gov (United States)

Li, Yuhong; Gong, Guanghong; Li, Ni

2018-01-01

In this paper, we propose a novel algorithm-parallel adaptive quantum genetic algorithm-which can rapidly determine the minimum control nodes of arbitrary networks with both control nodes and state nodes. The corresponding network can be fully controlled with the obtained control scheme. We transformed the network controllability issue into a combinational optimization problem based on the Popov-Belevitch-Hautus rank condition. A set of canonical networks and a list of real-world networks were experimented. Comparison results demonstrated that the algorithm was more ideal to optimize the controllability of networks, especially those larger-size networks. We demonstrated subsequently that there were links between the optimal control nodes and some network statistical characteristics. The proposed algorithm provides an effective approach to improve the controllability optimization of large networks or even extra-large networks with hundreds of thousands nodes.

Algorithm for Controlling a Centrifugal Compressor

Science.gov (United States)

Benedict, Scott M.

2004-01-01

An algorithm has been developed for controlling a centrifugal compressor that serves as the prime mover in a heatpump system. Experimental studies have shown that the operating conditions for maximum compressor efficiency are close to the boundary beyond which surge occurs. Compressor surge is a destructive condition in which there are instantaneous reversals of flow associated with a high outlet-to-inlet pressure differential. For a given cooling load, the algorithm sets the compressor speed at the lowest possible value while adjusting the inlet guide vane angle and diffuser vane angle to maximize efficiency, subject to an overriding requirement to prevent surge. The onset of surge is detected via the onset of oscillations of the electric current supplied to the compressor motor, associated with surge-induced oscillations of the torque exerted by and on the compressor rotor. The algorithm can be implemented in any of several computer languages.

Computationally efficient model predictive control algorithms a neural network approach

CERN Document Server

Ławryńczuk, Maciej

2014-01-01

This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: ·         A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. ·         Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. ·         The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). ·         The MPC algorithms with neural approximation with no on-line linearization. ·         The MPC algorithms with guaranteed stability and robustness. ·         Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require d...

Noise filtering algorithm for the MFTF-B computer based control system

International Nuclear Information System (INIS)

Minor, E.G.

1983-01-01

An algorithm to reduce the message traffic in the MFTF-B computer based control system is described. The algorithm filters analog inputs to the control system. Its purpose is to distinguish between changes in the inputs due to noise and changes due to significant variations in the quantity being monitored. Noise is rejected while significant changes are reported to the control system data base, thus keeping the data base updated with a minimum number of messages. The algorithm is memory efficient, requiring only four bytes of storage per analog channel, and computationally simple, requiring only subtraction and comparison. Quantitative analysis of the algorithm is presented for the case of additive Gaussian noise. It is shown that the algorithm is stable and tends toward the mean value of the monitored variable over a wide variety of additive noise distributions

Integrated control algorithms for plant environment in greenhouse

Science.gov (United States)

Zhang, Kanyu; Deng, Lujuan; Gong, Youmin; Wang, Shengxue

2003-09-01

In this paper a survey of plant environment control in artificial greenhouse was put forward for discussing the future development. Firstly, plant environment control started with the closed loop control of air temperature in greenhouse. With the emergence of higher property computer, the adaptive control algorithm and system identification were integrated into the control system. As adaptation control is more depending on observation of variables by sensors and yet many variables are unobservable or difficult to observe, especially for observation of crop growth status, so model-based control algorithm were developed. In order to evade modeling difficulty, one method is predigesting the models and the other method is utilizing fuzzy logic and neural network technology that realize the models by the black box and gray box theory. Studies on control method of plant environment in greenhouse by means of expert system (ES) and artificial intelligence (AI) have been initiated and developed. Nowadays, the research of greenhouse environment control focus on energy saving, optimal economic profit, enviornment protection and continualy develop.

Evaluation of TCP Congestion Control Algorithms on the Windows Vista Platform

Energy Technology Data Exchange (ETDEWEB)

Li, Yee-Ting; /SLAC

2006-07-07

CTCP, an innovative TCP congestion control algorithm developed by Microsoft, is evaluated and compared to HSTCP and StandardTCP. Tests were performed on the production Internet from Stanford Linear Accelerator Center (SLAC) to various geographically located hosts to give a broad overview of the performances. We find that certain issues were apparent during testing (not directly related to the congestion control algorithms) which may skew results. With this in mind, we find that CTCP performed similarly to HSTCP across a multitude of different network environments. However, to improve the fairness and to reduce the impact of CTCP upon existing StandardTCP traffic, two areas of further research were investigated. Algorithmic additions to CTCP for burst control to reduce the aggressiveness of its cwnd increments demonstrated beneficial improvements in both fairness and throughput over the original CTCP algorithm. Similarly, {gamma} auto-tuning algorithms were investigated to dynamically adapt CTCP flows to their network conditions for optimal performance. While the effects of these auto-tuning algorithms when used in addition to burst control showed little to no benefit to fairness nor throughput for the limited number of network paths tested, one of the auto-tuning algorithms performed such that there was negligible impact upon StandardTCP. With these improvements, CTCP was found to perform better than HSTCP in terms of fairness and similarly in terms of throughput under the production environments tested.

Evaluation of TCP Congestion Control Algorithms on the Windows Vista Platform

International Nuclear Information System (INIS)

Li, Y

2006-01-01

CTCP, an innovative TCP congestion control algorithm developed by Microsoft, is evaluated and compared to HSTCP and StandardTCP. Tests were performed on the production Internet from Stanford Linear Accelerator Center (SLAC) to various geographically located hosts to give a broad overview of the performances. We find that certain issues were apparent during testing (not directly related to the congestion control algorithms) which may skew results. With this in mind, we find that CTCP performed similarly to HSTCP across a multitude of different network environments. However, to improve the fairness and to reduce the impact of CTCP upon existing StandardTCP traffic, two areas of further research were investigated. Algorithmic additions to CTCP for burst control to reduce the aggressiveness of its cwnd increments demonstrated beneficial improvements in both fairness and throughput over the original CTCP algorithm. Similarly, auto-tuning algorithms were investigated to dynamically adapt CTCP flows to their network conditions for optimal performance. Whilst the effects of these auto-tuning algorithms when used in addition to burst control showed little to no benefit to fairness nor throughput for the limited number of network paths tested, one of the auto-tuning algorithms performed such that there was negligible impact upon StandardTCP. With these improvements, CTCP was found to perform better than HSTCP in terms of fairness and similarly in terms of throughput under the production environments tested

A homotopy algorithm for digital optimal projection control GASD-HADOC

Science.gov (United States)

Collins, Emmanuel G., Jr.; Richter, Stephen; Davis, Lawrence D.

1993-01-01

The linear-quadratic-gaussian (LQG) compensator was developed to facilitate the design of control laws for multi-input, multi-output (MIMO) systems. The compensator is computed by solving two algebraic equations for which standard closed-loop solutions exist. Unfortunately, the minimal dimension of an LQG compensator is almost always equal to the dimension of the plant and can thus often violate practical implementation constraints on controller order. This deficiency is especially highlighted when considering control-design for high-order systems such as flexible space structures. This deficiency motivated the development of techniques that enable the design of optimal controllers whose dimension is less than that of the design plant. A homotopy approach based on the optimal projection equations that characterize the necessary conditions for optimal reduced-order control. Homotopy algorithms have global convergence properties and hence do not require that the initializing reduced-order controller be close to the optimal reduced-order controller to guarantee convergence. However, the homotopy algorithm previously developed for solving the optimal projection equations has sublinear convergence properties and the convergence slows at higher authority levels and may fail. A new homotopy algorithm for synthesizing optimal reduced-order controllers for discrete-time systems is described. Unlike the previous homotopy approach, the new algorithm is a gradient-based, parameter optimization formulation and was implemented in MATLAB. The results reported may offer the foundation for a reliable approach to optimal, reduced-order controller design.

Platform for real-time simulation of dynamic systems and hardware-in-the-loop for control algorithms.

Science.gov (United States)

de Souza, Isaac D T; Silva, Sergio N; Teles, Rafael M; Fernandes, Marcelo A C

2014-10-15

The development of new embedded algorithms for automation and control of industrial equipment usually requires the use of real-time testing. However, the equipment required is often expensive, which means that such tests are often not viable. The objective of this work was therefore to develop an embedded platform for the distributed real-time simulation of dynamic systems. This platform, called the Real-Time Simulator for Dynamic Systems (RTSDS), could be applied in both industrial and academic environments. In industrial applications, the RTSDS could be used to optimize embedded control algorithms. In the academic sphere, it could be used to support research into new embedded solutions for automation and control and could also be used as a tool to assist in undergraduate and postgraduate teaching related to the development of projects concerning on-board control systems.

Application of Fuzzy Algorithm in Optimizing Hierarchical Sliding Mode Control for Pendubot System

Directory of Open Access Journals (Sweden)

Xuan Dung Huynh

2017-12-01

Full Text Available Pendubot is a classical under-actuated SIMO model for control algorithm testing in laboratory of universities. In this paper, authors design a fuzzy-sliding control for this system. The controller is designed from a new idea of application of fuzzy algorithm for optioning control parameters. The response of system on TOP position under fuzzysliding control algorithm is proved to be better than under sliding controller through Matlab/Simulink simulation.

Modular simulation of the dynamics of a 925 MWe PWR electronuclear type reactor and design of a multivariable control algorithm

International Nuclear Information System (INIS)

Mansouri, S.

1985-06-01

This work has been consecrated to the modular simulation of a PWR 925 MWe power plant's dynamic and to the design of a multivariable algorithm control: a mathematical model of a plant type was developed. The programs were written on a structured manner in order to maximize flexibility. A multivariable control algorithm based on pole placement with output feedback was elaborated together with its correspondent program. The simulation results for different normal transients were shown and the capabilities of the new method of multivariable control are illustrated through many examples

Figuring Control in the Algorithmic Era

DEFF Research Database (Denmark)

Markham, Annette; Bossen, Claus

Drawing on actor network theory, we follow how algorithms, information, selfhood and identity-for-others tangle in interesting and unexpected ways. Starting with simple moments in everyday life that might be described as having implications for ‘control,’ we focus attention on the ways in which t...

Genetic Algorithm Applied to the Eigenvalue Equalization Filtered-x LMS Algorithm (EE-FXLMS

Directory of Open Access Journals (Sweden)

Stephan P. Lovstedt

2008-01-01

Full Text Available The FXLMS algorithm, used extensively in active noise control (ANC, exhibits frequency-dependent convergence behavior. This leads to degraded performance for time-varying tonal noise and noise with multiple stationary tones. Previous work by the authors proposed the eigenvalue equalization filtered-x least mean squares (EE-FXLMS algorithm. For that algorithm, magnitude coefficients of the secondary path transfer function are modified to decrease variation in the eigenvalues of the filtered-x autocorrelation matrix, while preserving the phase, giving faster convergence and increasing overall attenuation. This paper revisits the EE-FXLMS algorithm, using a genetic algorithm to find magnitude coefficients that give the least variation in eigenvalues. This method overcomes some of the problems with implementing the EE-FXLMS algorithm arising from finite resolution of sampled systems. Experimental control results using the original secondary path model, and a modified secondary path model for both the previous implementation of EE-FXLMS and the genetic algorithm implementation are compared.

Continuous Steering Stability Control Based on an Energy-Saving Torque Distribution Algorithm for a Four in-Wheel-Motor Independent-Drive Electric Vehicle

Directory of Open Access Journals (Sweden)

Li Zhai

2018-02-01

Full Text Available In this paper, a continuous steering stability controller based on an energy-saving torque distribution algorithm is proposed for a four in-wheel-motor independent-drive electric vehicle (4MIDEV to improve the energy consumption efficiency while maintaining the stability in steering maneuvers. The controller is designed as a hierarchical structure, including the reference model level, the upper-level controller, and the lower-level controller. The upper-level controller adopts the direct yaw moment control (DYC, which is designed to work continuously during the steering maneuver to better ensure steering stability in extreme situations, rather than working only after the vehicle is judged to be unstable. An adaptive two-hierarchy energy-saving torque distribution algorithm is developed in the lower-level controller with the friction ellipse constraint as a basis for judging whether the algorithm needs to be switched, so as to achieve a more stable and energy-efficient steering operation. The proposed stability controller was validated in a co-simulation of CarSim and Matlab/Simulink. The simulation results under different steering maneuvers indicate that the proposed controller, compared with the conventional servo controller and the ordinary continuous controller, can reduce energy consumption up to 23.68% and improve the vehicle steering stability.

An Envelope Based Feedback Control System for Earthquake Early Warning: Reality Check Algorithm

Science.gov (United States)

Heaton, T. H.; Karakus, G.; Beck, J. L.

2016-12-01

Earthquake early warning systems are, in general, designed to be open loop control systems in such a way that the output, i.e., the warning messages, only depend on the input, i.e., recorded ground motions, up to the moment when the message is issued in real-time. We propose an algorithm, which is called Reality Check Algorithm (RCA), which would assess the accuracy of issued warning messages, and then feed the outcome of the assessment back into the system. Then, the system would modify its messages if necessary. That is, we are proposing to convert earthquake early warning systems into feedback control systems by integrating them with RCA. RCA works by continuously monitoring and comparing the observed ground motions' envelopes to the predicted envelopes of Virtual Seismologist (Cua 2005). Accuracy of magnitude and location (both spatial and temporal) estimations of the system are assessed separately by probabilistic classification models, which are trained by a Sparse Bayesian Learning technique called Automatic Relevance Determination prior.

Chemical optimization algorithm for fuzzy controller design

CERN Document Server

Astudillo, Leslie; Castillo, Oscar

2014-01-01

In this book, a novel optimization method inspired by a paradigm from nature is introduced. The chemical reactions are used as a paradigm to propose an optimization method that simulates these natural processes. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. This chemical reaction optimization paradigm is also applied to solve the tracking problem for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on fuzzy logic theory. Computer simulations are presented confirming that this optimization paradigm is able to outperform other optimization techniques applied to this particular robot application

Adaptive Control Algorithm of the Synchronous Generator

Directory of Open Access Journals (Sweden)

Shevchenko Victor

2017-01-01

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.

Numerical Algorithms for Deterministic Impulse Control Models with Applications

NARCIS (Netherlands)

Grass, D.; Chahim, M.

2012-01-01

Abstract: In this paper we describe three different algorithms, from which two (as far as we know) are new in the literature. We take both the size of the jump as the jump times as decision variables. The first (new) algorithm considers an Impulse Control problem as a (multipoint) Boundary Value

Optimization of type-2 fuzzy controllers using the bee colony algorithm

CERN Document Server

Amador, Leticia

2017-01-01

This book focuses on the fields of fuzzy logic, bio-inspired algorithm; especially bee colony optimization algorithm and also considering the fuzzy control area. The main idea is that this areas together can to solve various control problems and to find better results. In this book we test the proposed method using two benchmark problems; the problem for filling a water tank and the problem for controlling the trajectory in an autonomous mobile robot. When Interval Type-2 Fuzzy Logic System is implemented to model the behavior of systems, the results show a better stabilization, because the analysis of uncertainty is better. For this reason we consider in this book the proposed method using fuzzy systems, fuzzy controllers, and bee colony optimization algorithm improve the behavior of the complex control problems.

Design of an optimal SMES for automatic generation control of two-area thermal power system using Cuckoo search algorithm

Directory of Open Access Journals (Sweden)

Sabita Chaine

2015-05-01

Full Text Available This work presents a methodology adopted in order to tune the controller parameters of superconducting magnetic energy storage (SMES system in the automatic generation control (AGC of a two-area thermal power system. The gains of integral controllers of AGC loop, proportional controller of SMES loop and gains of the current feedback loop of the inductor in SMES are optimized simultaneously in order to achieve a desired performance. Recently proposed intelligent technique based algorithm known as Cuckoo search algorithm (CSA is applied for optimization. Sensitivity and robustness of the tuned gains tested at different operating conditions prove the effectiveness of fast acting energy storage devices like SMES in damping out oscillations in power system when their controllers are properly tuned.

Search algorithms, hidden labour and information control

Directory of Open Access Journals (Sweden)

Paško Bilić

2016-06-01

Full Text Available The paper examines some of the processes of the closely knit relationship between Google’s ideologies of neutrality and objectivity and global market dominance. Neutrality construction comprises an important element sustaining the company’s economic position and is reflected in constant updates, estimates and changes to utility and relevance of search results. Providing a purely technical solution to these issues proves to be increasingly difficult without a human hand in steering algorithmic solutions. Search relevance fluctuates and shifts through continuous tinkering and tweaking of the search algorithm. The company also uses third parties to hire human raters for performing quality assessments of algorithmic updates and adaptations in linguistically and culturally diverse global markets. The adaptation process contradicts the technical foundations of the company and calculations based on the initial Page Rank algorithm. Annual market reports, Google’s Search Quality Rating Guidelines, and reports from media specialising in search engine optimisation business are analysed. The Search Quality Rating Guidelines document provides a rare glimpse into the internal architecture of search algorithms and the notions of utility and relevance which are presented and structured as neutral and objective. Intertwined layers of ideology, hidden labour of human raters, advertising revenues, market dominance and control are discussed throughout the paper.

On flexible CAD of adaptive control and identification algorithms

DEFF Research Database (Denmark)

Christensen, Anders; Ravn, Ole

1988-01-01

a total redesign of the system within each sample. The necessary design parameters are evaluated and a decision vector is defined, from which the identification algorithm can be generated by the program. Using the decision vector, a decision-node tree structure is built up, where the nodes define......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...

Identification and real time control of current profile in Tore-supra: algorithms and simulation; Identification et controle en temps reel du profil de courant dans Tore Supra: algorithmes et simulations

Energy Technology Data Exchange (ETDEWEB)

Houy, P

1999-10-15

The aim of this work is to propose a real-time control of the current profile in order to achieve reproducible operating modes with improved energetic confinement in tokamaks. The determination of the profile is based on measurements given by interferometry and polarimetry diagnostics. Different ways to evaluate and improve the accuracy of these measurements are exposed. The position and the shape of a plasma are controlled by the poloidal system that forces them to cope with standard values. Gas or neutral ions or ice pellet or extra power injection are technical means used to control other plasma parameters. These controls are performed by servo-controlled loops. The poloidal system of Tore-supra is presented. The main obstacle to a reliable determination of the current profile is the fact that slightly different Faraday angles lead to very different profiles. The direct identification method that is exposed in this work, gives the profile that minimizes the square of the margin between measured and computed values. The different algorithms proposed to control current profiles on Tore-supra have been validated by using a plasma simulation. The code Cronos that solves the resistive diffusion equation of current has been used. (A.C.)

Optimal Control of Complex Systems Based on Improved Dual Heuristic Dynamic Programming Algorithm

Directory of Open Access Journals (Sweden)

Hui Li

2017-01-01

Full Text Available When applied to solving the data modeling and optimal control problems of complex systems, the dual heuristic dynamic programming (DHP technique, which is based on the BP neural network algorithm (BP-DHP, has difficulty in prediction accuracy, slow convergence speed, poor stability, and so forth. In this paper, a dual DHP technique based on Extreme Learning Machine (ELM algorithm (ELM-DHP was proposed. Through constructing three kinds of network structures, the paper gives the detailed realization process of the DHP technique in the ELM. The controller designed upon the ELM-DHP algorithm controlled a molecular distillation system with complex features, such as multivariability, strong coupling, and nonlinearity. Finally, the effectiveness of the algorithm is verified by the simulation that compares DHP and HDP algorithms based on ELM and BP neural network. The algorithm can also be applied to solve the data modeling and optimal control problems of similar complex systems.

DESIGN OF ALGORITHMS BASED ON BEHAVIOUR FOR THE CONTROL OF MINIBOTS // DISEÑO DE ALGORITMOS BASADOS EN COMPORTAMIENTOS PARA EL CONTROL DE MINIBOTS

OpenAIRE

Maritza Bracho de Rodríguez

2012-01-01

Using the Distributed Artificial Intelligence and the Distributed Robotics as a frame of reference, in this work are designed, developed and implemented algorithms to control autonomous, mobile, reactive, rational, proactive and sociable small robots. These minibots are capable to exhibit behaviors inspired in biological societies. Through the development of this work it was found that if the robot has to perform simple tasks, a reactive architecture is more convenient, efficient and effectiv...

Fuzzy Tracking and Control Algorithm for an SSVEP-Based BCI System

Directory of Open Access Journals (Sweden)

Yeou-Jiunn Chen

2016-09-01

Full Text Available Subjects with amyotrophic lateral sclerosis (ALS consistently experience decreasing quality of life because of this distinctive disease. Thus, a practical brain-computer interface (BCI application can effectively help subjects with ALS to participate in communication or entertainment. In this study, a fuzzy tracking and control algorithm is proposed for developing a BCI remote control system. To represent the characteristics of the measured electroencephalography (EEG signals after visual stimulation, a fast Fourier transform is applied to extract the EEG features. A self-developed fuzzy tracking algorithm quickly traces the changes of EEG signals. The accuracy and stability of a BCI system can be greatly improved by using a fuzzy control algorithm. Fifteen subjects were asked to attend a performance test of this BCI system. The canonical correlation analysis (CCA was adopted to compare the proposed approach, and the average recognition rates are 96.97% and 94.49% for proposed approach and CCA, respectively. The experimental results showed that the proposed approach is preferable to CCA. Overall, the proposed fuzzy tracking and control algorithm applied in the BCI system can profoundly help subjects with ALS to control air swimmer drone vehicles for entertainment purposes.

Distributed control software of high-performance control-loop algorithm

CERN Document Server

Blanc, D

1999-01-01

The majority of industrial cooling and ventilation plants require the control of complex processes. All these processes are highly important for the operation of the machines. The stability and reliability of these processes are leading factors identifying the quality of the service provided. The control system architecture and software structure, as well, are required to have high dynamical performance and robust behaviour. The intelligent systems based on PID or RST controllers are used for their high level of stability and accuracy. The design and tuning of these complex controllers require the dynamic model of the plant to be known (generally obtained by identification) and the desired performance of the various control loops to be specified for achieving good performances. The concept of having a distributed control algorithm software provides full automation facilities with well-adapted functionality and good performances, giving methodology, means and tools to master the dynamic process optimization an...

Hydraulic Pump Fault Diagnosis Control Research Based on PARD-BP Algorithm

Directory of Open Access Journals (Sweden)

LV Dongmei

2014-12-01

Full Text Available Combining working principle and failure mechanism of RZU2000HM hydraulic press, with its present fault cases being collected, the working principle of the oil pressure and faults phenomenon of the hydraulic power unit –swash-plate axial piston pump were studied with some emphasis, whose faults will directly affect the dynamic performance of the oil pressure and flow. In order to make hydraulic power unit work reliably, PARD-BP (Pruning Algorithm based Random Degree neural network fault algorithm was introduced, with swash-plate axial piston pump’s vibration fault sample data regarded as input, and fault mode matrix regarded as target output, so that PARD-BP algorithm could be trained. In the end, the vibration results were verified by the vibration modal test, and it was shown that the biggest upward peaks of vacuum pump in X-direction, Y-direction and Z- direction have fallen by 30.49 %, 21.13 % and 18.73 % respectively, so that the reliability of the fact that PARD-BP algorithm could be used for the online fault detection and diagnosis of the hydraulic pump was verified.

An efficient control algorithm for nonlinear systems

International Nuclear Information System (INIS)

Sinha, S.

1990-12-01

We suggest a scheme to step up the efficiency of a recently proposed adaptive control algorithm, which is remarkably effective for regulating nonlinear systems. The technique involves monitoring of the ''stiffness of control'' to get maximum gain while maintaining a predetermined accuracy. The success of the procedure is demonstrated for the case of the logistic map, where we show that the improvement in performance is often factors of tens, and for small control stiffness, even factors of hundreds. (author). 4 refs, 1 fig., 1 tab

Application of genetic algorithms to tuning fuzzy control systems

Science.gov (United States)

Espy, Todd; Vombrack, Endre; Aldridge, Jack

1993-01-01

Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.

Multi-agent coordination algorithms for control of distributed energy resources in smart grids

Science.gov (United States)

Cortes, Andres

Sustainable energy is a top-priority for researchers these days, since electricity and transportation are pillars of modern society. Integration of clean energy technologies such as wind, solar, and plug-in electric vehicles (PEVs), is a major engineering challenge in operation and management of power systems. This is due to the uncertain nature of renewable energy technologies and the large amount of extra load that PEVs would add to the power grid. Given the networked structure of a power system, multi-agent control and optimization strategies are natural approaches to address the various problems of interest for the safe and reliable operation of the power grid. The distributed computation in multi-agent algorithms addresses three problems at the same time: i) it allows for the handling of problems with millions of variables that a single processor cannot compute, ii) it allows certain independence and privacy to electricity customers by not requiring any usage information, and iii) it is robust to localized failures in the communication network, being able to solve problems by simply neglecting the failing section of the system. We propose various algorithms to coordinate storage, generation, and demand resources in a power grid using multi-agent computation and decentralized decision making. First, we introduce a hierarchical vehicle-one-grid (V1G) algorithm for coordination of PEVs under usage constraints, where energy only flows from the grid in to the batteries of PEVs. We then present a hierarchical vehicle-to-grid (V2G) algorithm for PEV coordination that takes into consideration line capacity constraints in the distribution grid, and where energy flows both ways, from the grid in to the batteries, and from the batteries to the grid. Next, we develop a greedy-like hierarchical algorithm for management of demand response events with on/off loads. Finally, we introduce distributed algorithms for the optimal control of distributed energy resources, i

Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

International Nuclear Information System (INIS)

Husam Fayiz, Al Masri

2017-01-01

The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms. (paper)

Fuzzy model predictive control algorithm applied in nuclear power plant

International Nuclear Information System (INIS)

Zuheir, Ahmad

2006-01-01

The aim of this paper is to design a predictive controller based on a fuzzy model. The Takagi-Sugeno fuzzy model with an Adaptive B-splines neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model using dynamic process information is carried out to build the predictive controller. The easy description of the fuzzy model and the easy computation of the gradient sector during the optimization procedure are the main advantages of the computation algorithm. The algorithm is applied to the control of a U-tube steam generation unit (UTSG) used for electricity generation. (author)

Warehouse sizing algorithm for edification works of construc-tion sector

Directory of Open Access Journals (Sweden)

Andres Mauricio Hualpa Zuñiga

2015-08-01

Full Text Available This article contains the development of an algorithm applied to the solution of problems of sizing of storage spaces in companies in the construction sector. This problem is justified under the degree of informality that occurs at the time of assigning storage areas, without considering parameters related to stages of construction, the characteristics of the product and the provisions of the work area. In a previous study it is identified that the degree of informality at the moment of assigning storage areas, generates poor rates of capacity utilization and delivery of incomplete orders. The design of the algorithm is supported by a comprehensive model of sizing subjected to a system of equations with variables of quantity, volume and material dimensions, to finally establish the necessary storage area. The algorithm is adapted to programming language in order to present the results in graphic language where the sizing of storage spaces is visible. These results are validated through the evaluation of storage capacity utilization and completely delivered orders for different cargo units, where improvements in these indicators are shown.

Fuzzy Control and Connected Region Marking Algorithm-Based SEM Nanomanipulation

Directory of Open Access Journals (Sweden)

Dongjie Li

2012-01-01

Full Text Available The interactive nanomanipulation platform is established based on fuzzy control and connected region marking (CRM algorithm in SEM. The 3D virtual nanomanipulation model is developed to make up the insufficiency of the 2D SEM image information, which provides the operator with depth and real-time visual feedback information to guide the manipulation. The haptic device Omega3 is used as the master to control the 3D motion of the nanopositioner in master-slave mode and offer the force sensing to the operator controlled with fuzzy control algorithm. Aiming at sensing of force feedback during the nanomanipulation, the collision detection method of the virtual nanomanipulation model and the force rending model are studied to realize the force feedback of nanomanipulation. The CRM algorithm is introduced to process the SEM image which provides effective position data of the objects for updating the virtual environment (VE, and relevant issues such as calibration and update rate of VE are also discussed. Finally, the performance of the platform is validated by the ZnO nanowire manipulation experiments.

Randomized Algorithms for Analysis and Control of Uncertain Systems With Applications

CERN Document Server

Tempo, Roberto; Dabbene, Fabrizio

2013-01-01

The presence of uncertainty in a system description has always been a critical issue in control. The main objective of Randomized Algorithms for Analysis and Control of Uncertain Systems, with Applications (Second Edition) is to introduce the reader to the fundamentals of probabilistic methods in the analysis and design of systems subject to deterministic and stochastic uncertainty. The approach propounded by this text guarantees a reduction in the computational complexity of classical  control algorithms and in the conservativeness of standard robust control techniques. The second edition has been thoroughly updated to reflect recent research and new applications with chapters on statistical learning theory, sequential methods for control and the scenario approach being completely rewritten.   Features: ·         self-contained treatment explaining Monte Carlo and Las Vegas randomized algorithms from their genesis in the principles of probability theory to their use for system analysis; ·    ...

Adaptive modification of the delayed feedback control algorithm with a continuously varying time delay

International Nuclear Information System (INIS)

Pyragas, V.; Pyragas, K.

2011-01-01

We propose a simple adaptive delayed feedback control algorithm for stabilization of unstable periodic orbits with unknown periods. The state dependent time delay is varied continuously towards the period of controlled orbit according to a gradient-descent method realized through three simple ordinary differential equations. We demonstrate the efficiency of the algorithm with the Roessler and Mackey-Glass chaotic systems. The stability of the controlled orbits is proven by computation of the Lyapunov exponents of linearized equations. -- Highlights: → A simple adaptive modification of the delayed feedback control algorithm is proposed. → It enables the control of unstable periodic orbits with unknown periods. → The delay time is varied continuously according to a gradient descend method. → The algorithm is embodied by three simple ordinary differential equations. → The validity of the algorithm is proven by computation of the Lyapunov exponents.

Software and hardware platform for testing of Automatic Generation Control algorithms

Directory of Open Access Journals (Sweden)

Vasiliev Alexey

2017-01-01

Full Text Available Development and implementation of new Automatic Generation Control (AGC algorithms requires testing them on a model that adequately simulates primary energetic, information and control processes. In this article an implementation of a test platform based on HRTSim (Hybrid Real Time Simulator and SCADA CK-2007 (which is widely used by the System Operator of Russia is proposed. Testing of AGC algorithms on the test platform based on the same SCADA system that is used in operation allows to exclude errors associated with the transfer of AGC algorithms and settings from the test platform to a real power system. A power system including relay protection, automatic control systems and emergency control automatics can be accurately simulated on HRTSim. Besides the information commonly used by conventional AGC systems HRTSim is able to provide a resemblance of Phasor Measurement Unit (PMU measurements (information about rotor angles, magnitudes and phase angles of currents and voltages etc.. The additional information significantly expands the number of possible AGC algorithms so the test platform is useful in modern AGC system developing. The obtained test results confirm that the proposed system is applicable for the tasks mentioned above.

Fuzzy PID control algorithm based on PSO and application in BLDC motor

Science.gov (United States)

Lin, Sen; Wang, Guanglong

2017-06-01

A fuzzy PID control algorithm is studied based on improved particle swarm optimization (PSO) to perform Brushless DC (BLDC) motor control which has high accuracy, good anti-jamming capability and steady state accuracy compared with traditional PID control. The mathematical and simulation model is established for BLDC motor by simulink software, and the speed loop of the fuzzy PID controller is designed. The simulation results show that the fuzzy PID control algorithm based on PSO has higher stability, high control precision and faster dynamic response speed.

Randomized Crossover Comparison of Personalized MPC and PID Control Algorithms for the Artificial Pancreas.

Science.gov (United States)

Pinsker, Jordan E; Lee, Joon Bok; Dassau, Eyal; Seborg, Dale E; Bradley, Paige K; Gondhalekar, Ravi; Bevier, Wendy C; Huyett, Lauren; Zisser, Howard C; Doyle, Francis J

2016-07-01

To evaluate two widely used control algorithms for an artificial pancreas (AP) under nonideal but comparable clinical conditions. After a pilot safety and feasibility study (n = 10), closed-loop control (CLC) was evaluated in a randomized, crossover trial of 20 additional adults with type 1 diabetes. Personalized model predictive control (MPC) and proportional integral derivative (PID) algorithms were compared in supervised 27.5-h CLC sessions. Challenges included overnight control after a 65-g dinner, response to a 50-g breakfast, and response to an unannounced 65-g lunch. Boluses of announced dinner and breakfast meals were given at mealtime. The primary outcome was time in glucose range 70-180 mg/dL. Mean time in range 70-180 mg/dL was greater for MPC than for PID (74.4 vs. 63.7%, P = 0.020). Mean glucose was also lower for MPC than PID during the entire trial duration (138 vs. 160 mg/dL, P = 0.012) and 5 h after the unannounced 65-g meal (181 vs. 220 mg/dL, P = 0.019). There was no significant difference in time with glucose PID control for the AP indicates that MPC performed particularly well, achieving nearly 75% time in the target range, including the unannounced meal. Although both forms of CLC provided safe and effective glucose management, MPC performed as well or better than PID in all metrics. © 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

A Feed-forward Geometrical Compensation and Adaptive Feedback Control Algorithm for Hydraulic Robot Manipulators

DEFF Research Database (Denmark)

Conrad, Finn; Zhou, Jianjun; Gabacik, Andrzej

1998-01-01

Invited paper presents a new control algorithm based on feed-forward geometrical compensation strategy combined with adaptive feedback control.......Invited paper presents a new control algorithm based on feed-forward geometrical compensation strategy combined with adaptive feedback control....

Control and monitoring of On-line Trigger Algorithms using gaucho

CERN Document Server

Van Herwijnen, Eric

2005-01-01

In the LHCb experiment, the trigger decisions are computed by Gaudi (the LHCb software framework) algorithms running on an event filter farm of around 2000 PCs. The control and monitoring of these algorithms has to be integrated in the overall experiment control system (ECS). To enable and facilitate this integration Gaucho, the GAUdi Component Helping Online, was developed. Gaucho consists of three parts: a C++ package integrated with Gaudi, the communications package DIM, and a set of PVSS panels and libraries. PVSS is a commercial SCADA system chosen as toolkit and framework for the LHCb controls system. The C++ package implements monitor service interface (IMonitorSvc) following the Gaudi specifications, with methods to declare variables and histograms for monitoring. Algorithms writers use them to indicate which quantities should be monitored. Since the interface resides in the GaudiKernel the code does not need changing if the monitoring services are not present. The Gaudi main job implements a state ma...

Comparing, optimizing, and benchmarking quantum-control algorithms in a unifying programming framework

International Nuclear Information System (INIS)

Machnes, S.; Sander, U.; Glaser, S. J.; Schulte-Herbrueggen, T.; Fouquieres, P. de; Gruslys, A.; Schirmer, S.

2011-01-01

For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. Moreover, we introduce a unifying algorithmic framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.

Implementation of Genetic Algorithm in Control Structure of Induction Motor A.C. Drive

Directory of Open Access Journals (Sweden)

BRANDSTETTER, P.

2014-11-01

Full Text Available Modern concepts of control systems with digital signal processors allow the implementation of time-consuming control algorithms in real-time, for example soft computing methods. The paper deals with the design and technical implementation of a genetic algorithm for setting proportional and integral gain of the speed controller of the A.C. drive with the vector-controlled induction motor. Important simulations and experimental measurements have been realized that confirm the correctness of the proposed speed controller tuned by the genetic algorithm and the quality speed response of the A.C. drive with changing parameters and disturbance variables, such as changes in load torque.

Control algorithms for dynamic attenuators

Energy Technology Data Exchange (ETDEWEB)

Hsieh, Scott S., E-mail: sshsieh@stanford.edu [Department of Radiology, Stanford University, Stanford, California 94305 and Department of Electrical Engineering, Stanford University, Stanford, California 94305 (United States); Pelc, Norbert J. [Department of Radiology, Stanford University, Stanford California 94305 and Department of Bioengineering, Stanford University, Stanford, California 94305 (United States)

2014-06-15

Purpose: The authors describe algorithms to control dynamic attenuators in CT and compare their performance using simulated scans. Dynamic attenuators are prepatient beam shaping filters that modulate the distribution of x-ray fluence incident on the patient on a view-by-view basis. These attenuators can reduce dose while improving key image quality metrics such as peak or mean variance. In each view, the attenuator presents several degrees of freedom which may be individually adjusted. The total number of degrees of freedom across all views is very large, making many optimization techniques impractical. The authors develop a theory for optimally controlling these attenuators. Special attention is paid to a theoretically perfect attenuator which controls the fluence for each ray individually, but the authors also investigate and compare three other, practical attenuator designs which have been previously proposed: the piecewise-linear attenuator, the translating attenuator, and the double wedge attenuator. Methods: The authors pose and solve the optimization problems of minimizing the mean and peak variance subject to a fixed dose limit. For a perfect attenuator and mean variance minimization, this problem can be solved in simple, closed form. For other attenuator designs, the problem can be decomposed into separate problems for each view to greatly reduce the computational complexity. Peak variance minimization can be approximately solved using iterated, weighted mean variance (WMV) minimization. Also, the authors develop heuristics for the perfect and piecewise-linear attenuators which do not requirea priori knowledge of the patient anatomy. The authors compare these control algorithms on different types of dynamic attenuators using simulated raw data from forward projected DICOM files of a thorax and an abdomen. Results: The translating and double wedge attenuators reduce dose by an average of 30% relative to current techniques (bowtie filter with tube current

Control algorithms for dynamic attenuators

International Nuclear Information System (INIS)

Hsieh, Scott S.; Pelc, Norbert J.

2014-01-01

Purpose: The authors describe algorithms to control dynamic attenuators in CT and compare their performance using simulated scans. Dynamic attenuators are prepatient beam shaping filters that modulate the distribution of x-ray fluence incident on the patient on a view-by-view basis. These attenuators can reduce dose while improving key image quality metrics such as peak or mean variance. In each view, the attenuator presents several degrees of freedom which may be individually adjusted. The total number of degrees of freedom across all views is very large, making many optimization techniques impractical. The authors develop a theory for optimally controlling these attenuators. Special attention is paid to a theoretically perfect attenuator which controls the fluence for each ray individually, but the authors also investigate and compare three other, practical attenuator designs which have been previously proposed: the piecewise-linear attenuator, the translating attenuator, and the double wedge attenuator. Methods: The authors pose and solve the optimization problems of minimizing the mean and peak variance subject to a fixed dose limit. For a perfect attenuator and mean variance minimization, this problem can be solved in simple, closed form. For other attenuator designs, the problem can be decomposed into separate problems for each view to greatly reduce the computational complexity. Peak variance minimization can be approximately solved using iterated, weighted mean variance (WMV) minimization. Also, the authors develop heuristics for the perfect and piecewise-linear attenuators which do not requirea priori knowledge of the patient anatomy. The authors compare these control algorithms on different types of dynamic attenuators using simulated raw data from forward projected DICOM files of a thorax and an abdomen. Results: The translating and double wedge attenuators reduce dose by an average of 30% relative to current techniques (bowtie filter with tube current

Control algorithms for dynamic attenuators.

Science.gov (United States)

Hsieh, Scott S; Pelc, Norbert J

2014-06-01

The authors describe algorithms to control dynamic attenuators in CT and compare their performance using simulated scans. Dynamic attenuators are prepatient beam shaping filters that modulate the distribution of x-ray fluence incident on the patient on a view-by-view basis. These attenuators can reduce dose while improving key image quality metrics such as peak or mean variance. In each view, the attenuator presents several degrees of freedom which may be individually adjusted. The total number of degrees of freedom across all views is very large, making many optimization techniques impractical. The authors develop a theory for optimally controlling these attenuators. Special attention is paid to a theoretically perfect attenuator which controls the fluence for each ray individually, but the authors also investigate and compare three other, practical attenuator designs which have been previously proposed: the piecewise-linear attenuator, the translating attenuator, and the double wedge attenuator. The authors pose and solve the optimization problems of minimizing the mean and peak variance subject to a fixed dose limit. For a perfect attenuator and mean variance minimization, this problem can be solved in simple, closed form. For other attenuator designs, the problem can be decomposed into separate problems for each view to greatly reduce the computational complexity. Peak variance minimization can be approximately solved using iterated, weighted mean variance (WMV) minimization. Also, the authors develop heuristics for the perfect and piecewise-linear attenuators which do not require a priori knowledge of the patient anatomy. The authors compare these control algorithms on different types of dynamic attenuators using simulated raw data from forward projected DICOM files of a thorax and an abdomen. The translating and double wedge attenuators reduce dose by an average of 30% relative to current techniques (bowtie filter with tube current modulation) without

Experimental study of a novel capacity control algorithm for a multi-evaporator air conditioning system

International Nuclear Information System (INIS)

Xu Xiangguo; Pan Yan; Deng Shiming; Xia Liang; Chan Mingyin

2013-01-01

The use of a multi-evaporator air conditioning (MEAC) system is advantageous in terms of installation convenience, high design flexibility, being easy to maintain and commission, better indoor thermal comfort control and higher energy efficiency. While MEAC units worth billions of dollars are sold worldwide, the detailed accounts on compressor capacity control and refrigeration flow distribution amongst evaporators remain unavailable in public domain, mainly due to commercial confidentiality. Limited control algorithms for MEAC systems have been developed based on system simulation, and no experimental-based capacity controller developments and their controllability tests may be identified in open literature. In the study reported in this paper, a novel capacity control algorithm, which imitated On–Off control of a single evaporator air conditioning (A/C) system in each indoor unit of a MEAC system by using variable speed compressor and electronic expansion valves (EEVs), was developed. Controllability tests under various settings for experimentally validating the novel capacity control algorithm were carried out and the control algorithm was further improved based on the experimental results. - Highlights: ► A capacity control algorithm for a multi-evaporator air conditioning system was developed. ► Experimental controllability tests under various settings were carried out. ► The control algorithm was further improved based on the experimental results.

Synthesis of Control Algorithm for a Leaderheaded UAVs Group

Directory of Open Access Journals (Sweden)

I. O. Samodov

2015-01-01

Full Text Available Currently, a defense sphere uses unmanned aerial vehicles (UAVs. UAVs have several advantages over manned aircrafts such as small size, reduced combat losses of personnel, etc. In addition, in threat environment, it is necessary to arrange both bringing together distant from each other UAVs in a group and their undetected in radar fields compact flying in terms of the joint flight security.However, the task to control a UAVs group is much more difficult than to control a single UAV, since it is necessary not only to control the aircraft, but also take into account the relative position of objects in the group.To solve this problem two ways are possible: using a network exchange between members of the group on the "everyone with everyone" principle and organizing the leader-headed flight.The aim of the article is to develop and study a possible option of the UAVs group control with arranging a leader-headed flight to provide the undetected in radar fields compact flying in terms of the joint flight security.The article develops a universal algorithm to control leader-headed group, based on a new modification of the statistical theory of optimal control. It studies effectiveness of the algorithm. While solving this task, a flight of seven UAVs was simulated in the horizontal plane in a rectangular coordinate system. Control time, linear errors of desired alignment of UAV, and control errors with respect to angular coordinates are used as measures of merit.The study results of the algorithm to control a leader-headed group of UAVs confirmed that it is possible to fulfill tasks of flying free-of-collision group of UAVs with essentially reduced computational costs.

Optimization Design by Genetic Algorithm Controller for Trajectory Control of a 3-RRR Parallel Robot

Directory of Open Access Journals (Sweden)

Lianchao Sheng

2018-01-01

Full Text Available In order to improve the control precision and robustness of the existing proportion integration differentiation (PID controller of a 3-Revolute–Revolute–Revolute (3-RRR parallel robot, a variable PID parameter controller optimized by a genetic algorithm controller is proposed in this paper. Firstly, the inverse kinematics model of the 3-RRR parallel robot was established according to the vector method, and the motor conversion matrix was deduced. Then, the error square integral was chosen as the fitness function, and the genetic algorithm controller was designed. Finally, the control precision of the new controller was verified through the simulation model of the 3-RRR planar parallel robot—built in SimMechanics—and the robustness of the new controller was verified by adding interference. The results show that compared with the traditional PID controller, the new controller designed in this paper has better control precision and robustness, which provides the basis for practical application.

ALGORITHM OF WORK OF SYSTEM OF MANAGEMENT BY AUTOMATED GEAR-BOXES CARS

Directory of Open Access Journals (Sweden)

O. Smirnov

2009-01-01

Full Text Available The development of algorithms of management system’s work by the automated gear-boxes vehicles is considered and the results of their practical use on the example of the KamAZ truck are considered.

Extracting quantum dynamics from genetic learning algorithms through principal control analysis

International Nuclear Information System (INIS)

White, J L; Pearson, B J; Bucksbaum, P H

2004-01-01

Genetic learning algorithms are widely used to control ultrafast optical pulse shapes for photo-induced quantum control of atoms and molecules. An unresolved issue is how to use the solutions found by these algorithms to learn about the system's quantum dynamics. We propose a simple method based on covariance analysis of the control space, which can reveal the degrees of freedom in the effective control Hamiltonian. We have applied this technique to stimulated Raman scattering in liquid methanol. A simple model of two-mode stimulated Raman scattering is consistent with the results. (letter to the editor)

Chaos control of ferroresonance system based on RBF-maximum entropy clustering algorithm

International Nuclear Information System (INIS)

Liu Fan; Sun Caixin; Sima Wenxia; Liao Ruijin; Guo Fei

2006-01-01

With regards to the ferroresonance overvoltage of neutral grounded power system, a maximum-entropy learning algorithm based on radial basis function neural networks is used to control the chaotic system. The algorithm optimizes the object function to derive learning rule of central vectors, and uses the clustering function of network hidden layers. It improves the regression and learning ability of neural networks. The numerical experiment of ferroresonance system testifies the effectiveness and feasibility of using the algorithm to control chaos in neutral grounded system

An Algorithm for Creating Virtual Controls Using Integrated and Harmonized Longitudinal Data.

Science.gov (United States)

Hansen, William B; Chen, Shyh-Huei; Saldana, Santiago; Ip, Edward H

2018-06-01

We introduce a strategy for creating virtual control groups-cases generated through computer algorithms that, when aggregated, may serve as experimental comparators where live controls are difficult to recruit, such as when programs are widely disseminated and randomization is not feasible. We integrated and harmonized data from eight archived longitudinal adolescent-focused data sets spanning the decades from 1980 to 2010. Collectively, these studies examined numerous psychosocial variables and assessed past 30-day alcohol, cigarette, and marijuana use. Additional treatment and control group data from two archived randomized control trials were used to test the virtual control algorithm. Both randomized controlled trials (RCTs) assessed intentions, normative beliefs, and values as well as past 30-day alcohol, cigarette, and marijuana use. We developed an algorithm that used percentile scores from the integrated data set to create age- and gender-specific latent psychosocial scores. The algorithm matched treatment case observed psychosocial scores at pretest to create a virtual control case that figuratively "matured" based on age-related changes, holding the virtual case's percentile constant. Virtual controls matched treatment case occurrence, eliminating differential attrition as a threat to validity. Virtual case substance use was estimated from the virtual case's latent psychosocial score using logistic regression coefficients derived from analyzing the treatment group. Averaging across virtual cases created group estimates of prevalence. Two criteria were established to evaluate the adequacy of virtual control cases: (1) virtual control group pretest drug prevalence rates should match those of the treatment group and (2) virtual control group patterns of drug prevalence over time should match live controls. The algorithm successfully matched pretest prevalence for both RCTs. Increases in prevalence were observed, although there were discrepancies between live

New mode switching algorithm for the JPL 70-meter antenna servo controller

Science.gov (United States)

Nickerson, J. A.

1988-01-01

The design of control mode switching algorithms and logic for JPL's 70 m antenna servo controller are described. The old control mode switching logic was reviewed and perturbation problems were identified. Design approaches for mode switching are presented and the final design is described. Simulations used to compare old and new mode switching algorithms and logic show that the new mode switching techniques will significantly reduce perturbation problems.

Active Noise Control Using Modified FsLMS and Hybrid PSOFF Algorithm

Directory of Open Access Journals (Sweden)

Ranjan Walia

2018-04-01

Full Text Available Active noise control is an efficient technique for noise cancellation of the system, which has been defined in this paper with the aid of Modified Filtered-s Least Mean Square (MFsLMS algorithm. The Hybrid Particle Swarm Optimization and Firefly (HPSOFF algorithm are used to identify the stability factor of the MFsLMS algorithm. The computational difficulty of the modified algorithm is reduced when compared with the original Filtered-s Least Mean Square (FsLMS algorithm. The noise sources are removed from the signal and it is compared with the existing FsLMS algorithm. The performance of the system is established with the normalized mean square error for two different types of noises. The proposed method has also been compared with the existing algorithms for the same purposes.

New recursive-least-squares algorithms for nonlinear active control of sound and vibration using neural networks.

Science.gov (United States)

Bouchard, M

2001-01-01

In recent years, a few articles describing the use of neural networks for nonlinear active control of sound and vibration were published. Using a control structure with two multilayer feedforward neural networks (one as a nonlinear controller and one as a nonlinear plant model), steepest descent algorithms based on two distinct gradient approaches were introduced for the training of the controller network. The two gradient approaches were sometimes called the filtered-x approach and the adjoint approach. Some recursive-least-squares algorithms were also introduced, using the adjoint approach. In this paper, an heuristic procedure is introduced for the development of recursive-least-squares algorithms based on the filtered-x and the adjoint gradient approaches. This leads to the development of new recursive-least-squares algorithms for the training of the controller neural network in the two networks structure. These new algorithms produce a better convergence performance than previously published algorithms. Differences in the performance of algorithms using the filtered-x and the adjoint gradient approaches are discussed in the paper. The computational load of the algorithms discussed in the paper is evaluated for multichannel systems of nonlinear active control. Simulation results are presented to compare the convergence performance of the algorithms, showing the convergence gain provided by the new algorithms.

Investigation of energy management strategies for photovoltaic systems - A predictive control algorithm

Science.gov (United States)

Cull, R. C.; Eltimsahy, A. H.

1983-01-01

The present investigation is concerned with the formulation of energy management strategies for stand-alone photovoltaic (PV) systems, taking into account a basic control algorithm for a possible predictive, (and adaptive) controller. The control system controls the flow of energy in the system according to the amount of energy available, and predicts the appropriate control set-points based on the energy (insolation) available by using an appropriate system model. Aspects of adaptation to the conditions of the system are also considered. Attention is given to a statistical analysis technique, the analysis inputs, the analysis procedure, and details regarding the basic control algorithm.

Vector Control Algorithm for Electric Vehicle AC Induction Motor Based on Improved Variable Gain PID Controller

Directory of Open Access Journals (Sweden)

Gang Qin

2015-01-01

Full Text Available The acceleration performance of EV, which affects a lot of performances of EV such as start-up, overtaking, driving safety, and ride comfort, has become increasingly popular in recent researches. An improved variable gain PID control algorithm to improve the acceleration performance is proposed in this paper. The results of simulation with Matlab/Simulink demonstrate the effectiveness of the proposed algorithm through the control performance of motor velocity, motor torque, and three-phase current of motor. Moreover, it is investigated that the proposed controller is valid by comparison with the other PID controllers. Furthermore, the AC induction motor experiment set is constructed to verify the effect of proposed controller.

Algorithms and procedures in the model based control of accelerators

International Nuclear Information System (INIS)

Bozoki, E.

1987-10-01

The overall design of a Model Based Control system was presented. The system consists of PLUG-IN MODULES, governed by a SUPERVISORY PROGRAM and communicating via SHARED DATA FILES. Models can be ladded or replaced without affecting the oveall system. There can be more then one module (algorithm) to perform the same task. The user can choose the most appropriate algorithm or can compare the results using different algorithms. Calculations, algorithms, file read and write, etc. which are used in more than one module, will be in a subroutine library. This feature will simplify the maintenance of the system. A partial list of modules is presented, specifying the task they perform. 19 refs., 1 fig

Development of real-time plasma analysis and control algorithms for the TCV tokamak using SIMULINK

International Nuclear Information System (INIS)

Felici, F.; Le, H.B.; Paley, J.I.; Duval, B.P.; Coda, S.; Moret, J.-M.; Bortolon, A.; Federspiel, L.; Goodman, T.P.; Hommen, G.; Karpushov, A.; Piras, F.; Pitzschke, A.; Romero, J.; Sevillano, G.; Sauter, O.; Vijvers, W.

2014-01-01

Highlights: • A new digital control system for the TCV tokamak has been commissioned. • The system is entirely programmable by SIMULINK, allowing rapid algorithm development. • Different control system nodes can run different algorithms at varying sampling times. • The previous control system functions have been emulated and improved. • New capabilities include MHD control, profile control, equilibrium reconstruction. - Abstract: One of the key features of the new digital plasma control system installed on the TCV tokamak is the possibility to rapidly design, test and deploy real-time algorithms. With this flexibility the new control system has been used for a large number of new experiments which exploit TCV's powerful actuators consisting of 16 individually controllable poloidal field coils and 7 real-time steerable electron cyclotron (EC) launchers. The system has been used for various applications, ranging from event-based real-time MHD control to real-time current diffusion simulations. These advances have propelled real-time control to one of the cornerstones of the TCV experimental program. Use of the SIMULINK graphical programming language to directly program the control system has greatly facilitated algorithm development and allowed a multitude of different algorithms to be deployed in a short time. This paper will give an overview of the developed algorithms and their application in physics experiments

Simulation study of multi-step model algorithmic control of the nuclear reactor thermal power tracking system

International Nuclear Information System (INIS)

Shi Xiaoping; Xu Tianshu

2001-01-01

The classical control method is usually hard to ensure the thermal power tracking accuracy, because the nuclear reactor system is a complex nonlinear system with uncertain parameters and disturbances. A sort of non-parameter model is constructed with the open-loop impulse response of the system. Furthermore, a sort of thermal power tracking digital control law is presented using the multi-step model algorithmic control principle. The control method presented had good tracking performance and robustness. It can work despite the existence of unmeasurable disturbances. The simulation experiment testifies the correctness and effectiveness of the method. The high accuracy matching between the thermal power and the referenced load is achieved

Sum-of-squares-based fuzzy controller design using quantum-inspired evolutionary algorithm

Science.gov (United States)

Yu, Gwo-Ruey; Huang, Yu-Chia; Cheng, Chih-Yung

2016-07-01

In the field of fuzzy control, control gains are obtained by solving stabilisation conditions in linear-matrix-inequality-based Takagi-Sugeno fuzzy control method and sum-of-squares-based polynomial fuzzy control method. However, the optimal performance requirements are not considered under those stabilisation conditions. In order to handle specific performance problems, this paper proposes a novel design procedure with regard to polynomial fuzzy controllers using quantum-inspired evolutionary algorithms. The first contribution of this paper is a combination of polynomial fuzzy control and quantum-inspired evolutionary algorithms to undertake an optimal performance controller design. The second contribution is the proposed stability condition derived from the polynomial Lyapunov function. The proposed design approach is dissimilar to the traditional approach, in which control gains are obtained by solving the stabilisation conditions. The first step of the controller design uses the quantum-inspired evolutionary algorithms to determine the control gains with the best performance. Then, the stability of the closed-loop system is analysed under the proposed stability conditions. To illustrate effectiveness and validity, the problem of balancing and the up-swing of an inverted pendulum on a cart is used.

An on-line modified least-mean-square algorithm for training neurofuzzy controllers.

Science.gov (United States)

Tan, Woei Wan

2007-04-01

The problem hindering the use of data-driven modelling methods for training controllers on-line is the lack of control over the amount by which the plant is excited. As the operating schedule determines the information available on-line, the knowledge of the process may degrade if the setpoint remains constant for an extended period. This paper proposes an identification algorithm that alleviates "learning interference" by incorporating fuzzy theory into the normalized least-mean-square update rule. The ability of the proposed methodology to achieve faster learning is examined by employing the algorithm to train a neurofuzzy feedforward controller for controlling a liquid level process. Since the proposed identification strategy has similarities with the normalized least-mean-square update rule and the recursive least-square estimator, the on-line learning rates of these algorithms are also compared.

Modified Firefly Algorithm based controller design for integrating and unstable delay processes

Directory of Open Access Journals (Sweden)

A. Gupta

2016-03-01

Full Text Available In this paper, Modified Firefly Algorithm has been used for optimizing the controller parameters of Smith predictor structure. The proposed algorithm modifies the position formula of the standard Firefly Algorithm in order to achieve faster convergence rate. Performance criteria Integral Square Error (ISE is optimized using this optimization technique. Simulation results show high performance for Modified Firefly Algorithm as compared to conventional Firefly Algorithm in terms of convergence rate. Integrating and unstable delay processes are taken as examples to indicate the performance of the proposed method.

Comparing Whole Building Energy Implications of Sidelighting Systems with Alternate Manual Blind Control Algorithms

Directory of Open Access Journals (Sweden)

Christopher Dyke

2015-05-01

Full Text Available Currently, there is no manual blind control guideline used consistently throughout the energy modeling community. This paper identifies and compares five manual blind control algorithms with unique control patterns and reports blind occlusion, rate of change data, and annual building energy consumption. The blind control schemes detailed here represent five reasonable candidates for use in lighting and energy simulation based on difference driving factors. This study was performed on a medium-sized office building using EnergyPlus with the internal daylight harvesting engine. Results show that applying manual blind control algorithms affects the total annual consumption of the building by as much as 12.5% and 11.5% for interior and exterior blinds respectively, compared to the Always Retracted blinds algorithm. Peak demand was also compared showing blind algorithms affected zone load sizing by as much as 9.8%. The alternate algorithms were tested for their impact on American Society of Heating, Refrigeration and Air-Conditioning Engineers (ASHRAE Guideline 14 calibration metrics and all models were found to differ from the original calibrated baseline by more than the recommended ±15% for coefficient of variance of the mean square error (CVRMSE and ±5% for normalized mean bias error (NMBE. The paper recommends that energy modelers use one or more manual blind control algorithms during design stages when making decisions about energy efficiency and other design alternatives.

Speed and Displacement Control System of Bearingless Brushless DC Motor Based on Improved Bacterial Foraging Algorithm

Directory of Open Access Journals (Sweden)

Diao Xiaoyan

2016-01-01

Full Text Available To solve the deficiencies of long optimization time and poor precision existing in conventional bacterial foraging algorithm (BFA in the process of parameter optimization, an improved bacterial foraging algorithm (IBFA is proposed and applied to speed and displacement control system of bearingless brushless DC (Bearingless BLDC motors. To begin with the fundamental principle of BFA, the proposed method is introduced and the individual intelligence is efficiently used in the process of parameter optimization, and then the working principle of bearingless BLDC motors is expounded. Finally, modeling and simulation of the speed and displacement control system of bearingless BLDC motors based on the IBFA are carried out by taking the software of MATLAB/Simulink as a platform. Simulation results show that, speed overshoot, torque ripple and rotor position oscillation are dramatically reduced, thus the proposed method has good application prospects in the field of bearingless motors.

Output Feedback Control of Electro-Hydraulic Cylinder Drives using the Twisting Algorithm

DEFF Research Database (Denmark)

Schmidt, Lasse; Andersen, Torben Ole; Pedersen, Henrik C.

2014-01-01

contributions in literature. This paper considers the twisting algorithm when applied directly for output feedback control, and with the design based on a reduced order model representation of an arbitrary valve driven hydraulic cylinder drive. The consequence of implementing such a controller with the well......This paper discusses the utilization of the so-called twisting algorithm when applied in output feedback position control schemes for electro-hydraulic cylinder drives. The twisting controller was the first second order sliding controller ever introduced, and can structure-wise be considered...... feedback controller may be successfully applied to hydraulic valve driven cylinder drives, with performance being on the level with a conventional surface based first order sliding mode controller....

Controller tuning based on optimization algorithms of a novel spherical rolling robot

International Nuclear Information System (INIS)

Sadegjian, Rasou; Masouleh, Mehdi Tale

2016-01-01

This study presents the construction process of a novel spherical rolling robot and control strategies that are used to improve robot locomotion. The proposed robot drive mechanism is constructed based on a combination of the pendulum and wheel drive mechanisms. The control model of the proposed robot is developed, and the state space model is calculated based on the obtained control model. Two control strategies are defined to improve the synchronization performance of the proposed robot motors. The proportional-derivative and proportional-integral-derivative controllers are designed based on the pole placement method. The proportional-integral-derivative controller leads to a better step response than the proportional-derivative controller. The controller parameters are tuned with genetic and differential evaluation algorithms. The proportional-integral-derivative controller which is tuned based on the differential evaluation algorithm leads to a better step response than the proportional-integral-derivative controller that is tuned based on genetic algorithm. Fuzzy logics are used to reduce the robot drive mechanism motors synchronizing process time to the end of achieving a high-performance controller. The experimental implementation results of fuzzy-proportional-integral-derivative on the proposed spherical rolling robot resulted in a desirable synchronizing performance in a short time

Controller tuning based on optimization algorithms of a novel spherical rolling robot

Energy Technology Data Exchange (ETDEWEB)

Sadegjian, Rasou [Dept. of Electrical, Biomedical, and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, QazvinI (Iran, Islamic Republic of); Masouleh, Mehdi Tale [Human and Robot Interaction Laboratory, Faculty of New Sciences and Technologies, University of Tehran, Tehran (Iran, Islamic Republic of)

2016-11-15

This study presents the construction process of a novel spherical rolling robot and control strategies that are used to improve robot locomotion. The proposed robot drive mechanism is constructed based on a combination of the pendulum and wheel drive mechanisms. The control model of the proposed robot is developed, and the state space model is calculated based on the obtained control model. Two control strategies are defined to improve the synchronization performance of the proposed robot motors. The proportional-derivative and proportional-integral-derivative controllers are designed based on the pole placement method. The proportional-integral-derivative controller leads to a better step response than the proportional-derivative controller. The controller parameters are tuned with genetic and differential evaluation algorithms. The proportional-integral-derivative controller which is tuned based on the differential evaluation algorithm leads to a better step response than the proportional-integral-derivative controller that is tuned based on genetic algorithm. Fuzzy logics are used to reduce the robot drive mechanism motors synchronizing process time to the end of achieving a high-performance controller. The experimental implementation results of fuzzy-proportional-integral-derivative on the proposed spherical rolling robot resulted in a desirable synchronizing performance in a short time.

Design a software real-time operation platform for wave piercing catamarans motion control using linear quadratic regulator based genetic algorithm.

Science.gov (United States)

Liang, Lihua; Yuan, Jia; Zhang, Songtao; Zhao, Peng

2018-01-01

This work presents optimal linear quadratic regulator (LQR) based on genetic algorithm (GA) to solve the two degrees of freedom (2 DoF) motion control problem in head seas for wave piercing catamarans (WPC). The proposed LQR based GA control strategy is to select optimal weighting matrices (Q and R). The seakeeping performance of WPC based on proposed algorithm is challenged because of multi-input multi-output (MIMO) system of uncertain coefficient problems. Besides the kinematical constraint problems of WPC, the external conditions must be considered, like the sea disturbance and the actuators (a T-foil and two flaps) control. Moreover, this paper describes the MATLAB and LabVIEW software plats to simulate the reduction effects of WPC. Finally, the real-time (RT) NI CompactRIO embedded controller is selected to test the effectiveness of the actuators based on proposed techniques. In conclusion, simulation and experimental results prove the correctness of the proposed algorithm. The percentage of heave and pitch reductions are more than 18% in different high speeds and bad sea conditions. And the results also verify the feasibility of NI CompactRIO embedded controller.

Effects on employees of controlling working hours and working schedules.

Science.gov (United States)

Kubo, T; Takahashi, M; Togo, F; Liu, X; Shimazu, A; Tanaka, K; Takaya, M

2013-03-01

High levels of control over working time and low variability in working hours have been associated with improved health-related outcomes. The potential mechanisms for this association remain unclear. To examine how work-time control and variability of working times are associated with fatigue recovery, sleep quality, work-life balance, and 'near misses' at work. Manufacturing sector employees completed a questionnaire that assessed work-time control, work-time variability, fatigue recovery, sleep quality, work-life balance and the frequency of near misses in the past 6 months. Mixed model analysis of covariance and multiple logistic regression analysis tested the main effects of work-time control and variability and their interaction, while adjusting for age, sex, work schedules, and overtime work in the past month. Subscales of work-time control were also investigated (control over daily working hours and over days off). One thousand three hundred and seventy-two completed questionnaires were returned, a response rate of 69%. A significantly higher quality of sleep and better work-life balance were found in the 'high control with low variability' reference group than in the other groups. Significantly better recovery of fatigue was also observed in the group having control over days off with low variability. While near misses were more frequent in the group with high control over daily working hours coupled with high variability compared with the reference group this was not significant. High work-time control and low variability were associated with favourable outcomes of health and work-life balance. This combined effect was not observed for the safety outcome addressed here.

Fuzzy algorithms to generate level controllers for nuclear power plant steam generators

International Nuclear Information System (INIS)

Moon, Byung Soo; Park, Jae Chang; Kim, Dong Hwa; Kim, Byung Koo

1993-01-01

In this paper, we present two sets of fuzzy algorithms for the steam generater level control; one for the high power operations where the flow error is available and the other for the low power operations where the flow error is not available. These are converted to a PID type controller for the high power case and to a quadratic function form of a controller for the low power case. These controllers are implemented on the Compact Nuclear Simulator at Korea Atomic Energy Research Institute and tested by a set of four simulation experiments for each. For both cases, the results show that the total variation of the level error and of the flow error are about 50% of those by the PI controllers with about one half of the control action. For the high power case, this is mainly due to the fact that a combination of two PD type controllers in the velocity algorithm form rather than a combination of two PI type controllers in the position algorithm form is used. For the low power case, the controller is essentially a PID type with a very small integral component where the average values for the derivative component input and for the controller output are used. (Author)

Comparison of switching control algorithms effective in restricting the switching in the neighborhood of the origin

International Nuclear Information System (INIS)

Joung, JinWook; Chung, Lan; Smyth, Andrew W

2010-01-01

The active interaction control (AIC) system consisting of a primary structure, an auxiliary structure and an interaction element was proposed to protect the primary structure against earthquakes and winds. The objective of the AIC system in reducing the responses of the primary structure is fulfilled by activating or deactivating the switching between the engagement and the disengagement of the primary and auxiliary structures through the interaction element. The status of the interaction element is controlled by switching control algorithms. The previously developed switching control algorithms require an excessive amount of switching, which is inefficient. In this paper, the excessive amount of switching is restricted by imposing an appropriately designed switching boundary region, where switching is prohibited, on pre-designed engagement–disengagement conditions. Two different approaches are used in designing the newly proposed AID-off and AID-off 2 algorithms. The AID-off 2 algorithm is designed to affect deactivated switching regions explicitly, unlike the AID-off algorithm, which follows the same procedure of designing the engagement–disengagement conditions of the previously developed algorithms, by using the current status of the AIC system. Both algorithms are shown to be effective in reducing the amount of switching times triggered from the previously developed AID algorithm under an appropriately selected control sampling period for different earthquakes, but the AID-off 2 algorithm outperforms the AID-off algorithm in reducing the number of switching times

New human-centered linear and nonlinear motion cueing algorithms for control of simulator motion systems

Science.gov (United States)

Telban, Robert J.

While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. To address this, new human-centered motion cueing algorithms were developed. A revised "optimal algorithm" uses time-invariant filters developed by optimal control, incorporating human vestibular system models. The "nonlinear algorithm" is a novel approach that is also formulated by optimal control, but can also be updated in real time. It incorporates a new integrated visual-vestibular perception model that includes both visual and vestibular sensation and the interaction between the stimuli. A time-varying control law requires the matrix Riccati equation to be solved in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. As a result of unsatisfactory sensation, an augmented turbulence cue was added to the vertical mode for both the optimal and nonlinear algorithms. The relative effectiveness of the algorithms, in simulating aircraft maneuvers, was assessed with an eleven-subject piloted performance test conducted on the NASA Langley Visual Motion Simulator (VMS). Two methods, the quasi-objective NASA Task Load Index (TLX), and power spectral density analysis of pilot control, were used to assess pilot workload. TLX analysis reveals, in most cases, less workload and variation among pilots with the nonlinear algorithm. Control input

Optimal gravitational search algorithm for automatic generation control of interconnected power systems

Directory of Open Access Journals (Sweden)

Rabindra Kumar Sahu

2014-09-01

Full Text Available An attempt is made for the effective application of Gravitational Search Algorithm (GSA to optimize PI/PIDF controller parameters in Automatic Generation Control (AGC of interconnected power systems. Initially, comparison of several conventional objective functions reveals that ITAE yields better system performance. Then, the parameters of GSA technique are properly tuned and the GSA control parameters are proposed. The superiority of the proposed approach is demonstrated by comparing the results of some recently published techniques such as Differential Evolution (DE, Bacteria Foraging Optimization Algorithm (BFOA and Genetic Algorithm (GA. Additionally, sensitivity analysis is carried out that demonstrates the robustness of the optimized controller parameters to wide variations in operating loading condition and time constants of speed governor, turbine, tie-line power. Finally, the proposed approach is extended to a more realistic power system model by considering the physical constraints such as reheat turbine, Generation Rate Constraint (GRC and Governor Dead Band nonlinearity.

A Decomposition Algorithm for Mean-Variance Economic Model Predictive Control of Stochastic Linear Systems

DEFF Research Database (Denmark)

Sokoler, Leo Emil; Dammann, Bernd; Madsen, Henrik

2014-01-01

This paper presents a decomposition algorithm for solving the optimal control problem (OCP) that arises in Mean-Variance Economic Model Predictive Control of stochastic linear systems. The algorithm applies the alternating direction method of multipliers to a reformulation of the OCP...

Wind turbine pitch control using ICPSO-PID algorithm

DEFF Research Database (Denmark)

Xu, Chang; Tian, Qiangqiang; Shen, Wen Zhong

2013-01-01

For the traditional simplified first-order pitch-control system model, it is difficult to describe a real dynamic characteristic of a variable pitch action system, thus a complete high order mathematical model has to be developed for the pitch control of wind turbine generation (WTG). In the paper...... controller parameters quickly; and the feed-forward controller for wind speed can improve dynamics of a pitch-control system; additionally the power controller can allow a wind turbine to have a constant power output as a wind speed is over the rated one. Compared with a conventional PID, the controller...... with ICPSO-PID algorithm has a smaller overshoot, a shorter tuning time and better robustness. The design method proposed in the paper can be applied in a practical electro-hydraulic pitch control system for WTG....

Control Algorithms for Large-scale Single-axis Photovoltaic Trackers

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Dorian Schneider

2012-01-01

Full Text Available The electrical yield of large-scale photovoltaic power plants can be greatly improved by employing solar trackers. While fixed-tilt superstructures are stationary and immobile, trackers move the PV-module plane in order to optimize its alignment to the sun. This paper introduces control algorithms for single-axis trackers (SAT, including a discussion for optimal alignment and backtracking. The results are used to simulate and compare the electrical yield of fixed-tilt and SAT systems. The proposed algorithms have been field tested, and are in operation in solar parks worldwide.

A Biologically-Inspired Power Control Algorithm for Energy-Efficient Cellular Networks

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Hyun-Ho Choi

2016-03-01

Full Text Available Most of the energy used to operate a cellular network is consumed by a base station (BS, and reducing the transmission power of a BS can therefore afford a substantial reduction in the amount of energy used in a network. In this paper, we propose a distributed transmit power control (TPC algorithm inspired by bird flocking behavior as a means of improving the energy efficiency of a cellular network. Just as each bird in a flock attempts to match its velocity with the average velocity of adjacent birds, in the proposed algorithm, each mobile station (MS in a cell matches its rate with the average rate of the co-channel MSs in adjacent cells by controlling the transmit power of its serving BS. We verify that this bio-inspired TPC algorithm using a local rate-average process achieves an exponential convergence and maximizes the minimum rate of the MSs concerned. Simulation results show that the proposed TPC algorithm follows the same convergence properties as the flocking algorithm and also effectively reduces the power consumption at the BSs while maintaining a low outage probability as the inter-cell interference increases; in so doing, it significantly improves the energy efficiency of a cellular network.

A parallel row-based algorithm for standard cell placement with integrated error control

Science.gov (United States)

Sargent, Jeff S.; Banerjee, Prith

1989-01-01

A new row-based parallel algorithm for standard-cell placement targeted for execution on a hypercube multiprocessor is presented. Key features of this implementation include a dynamic simulated-annealing schedule, row-partitioning of the VLSI chip image, and two novel approaches to control error in parallel cell-placement algorithms: (1) Heuristic Cell-Coloring; (2) Adaptive Sequence Length Control.

Research on the Random Shock Vibration Test Based on the Filter-X LMS Adaptive Inverse Control Algorithm

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Wang Wei

2016-01-01

Full Text Available The related theory and algorithm of adaptive inverse control were presented through the research which pointed out the adaptive inverse control strategy could effectively eliminate the noise influence on the system control. Proposed using a frequency domain filter-X LMS adaptive inverse control algorithm, and the control algorithm was applied to the two-exciter hydraulic vibration test system of random shock vibration control process and summarized the process of the adaptive inverse control strategies in the realization of the random shock vibration test. The self-closed-loop and field test show that using the frequency-domain filter-X LMS adaptive inverse control algorithm can realize high precision control of random shock vibration test.

Comparison of Controller and Flight Deck Algorithm Performance During Interval Management with Dynamic Arrival Trees (STARS)

Science.gov (United States)

Battiste, Vernol; Lawton, George; Lachter, Joel; Brandt, Summer; Koteskey, Robert; Dao, Arik-Quang; Kraut, Josh; Ligda, Sarah; Johnson, Walter W.

2012-01-01

Managing the interval between arrival aircraft is a major part of the en route and TRACON controller s job. In an effort to reduce controller workload and low altitude vectoring, algorithms have been developed to allow pilots to take responsibility for, achieve and maintain proper spacing. Additionally, algorithms have been developed to create dynamic weather-free arrival routes in the presence of convective weather. In a recent study we examined an algorithm to handle dynamic re-routing in the presence of convective weather and two distinct spacing algorithms. The spacing algorithms originated from different core algorithms; both were enhanced with trajectory intent data for the study. These two algorithms were used simultaneously in a human-in-the-loop (HITL) simulation where pilots performed weather-impacted arrival operations into Louisville International Airport while also performing interval management (IM) on some trials. The controllers retained responsibility for separation and for managing the en route airspace and some trials managing IM. The goal was a stress test of dynamic arrival algorithms with ground and airborne spacing concepts. The flight deck spacing algorithms or controller managed spacing not only had to be robust to the dynamic nature of aircraft re-routing around weather but also had to be compatible with two alternative algorithms for achieving the spacing goal. Flight deck interval management spacing in this simulation provided a clear reduction in controller workload relative to when controllers were responsible for spacing the aircraft. At the same time, spacing was much less variable with the flight deck automated spacing. Even though the approaches taken by the two spacing algorithms to achieve the interval management goals were slightly different they seem to be simpatico in achieving the interval management goal of 130 sec by the TRACON boundary.

Hierarchical Control Strategy for Active Hydropneumatic Suspension Vehicles Based on Genetic Algorithms

Directory of Open Access Journals (Sweden)

Jinzhi Feng

2015-02-01

Full Text Available A new hierarchical control strategy for active hydropneumatic suspension systems is proposed. This strategy considers the dynamic characteristics of the actuator. The top hierarchy controller uses a combined control scheme: a genetic algorithm- (GA- based self-tuning proportional-integral-derivative controller and a fuzzy logic controller. For practical implementations of the proposed control scheme, a GA-based self-learning process is initiated only when the defined performance index of vehicle dynamics exceeds a certain debounce time threshold. The designed control algorithm is implemented on a virtual prototype and cosimulations are performed with different road disturbance inputs. Cosimulation results show that the active hydropneumatic suspension system designed in this study significantly improves riding comfort characteristics of vehicles. The robustness and adaptability of the proposed controller are also examined when the control system is subjected to extremely rough road conditions.

Application of genetic algorithm to control design

International Nuclear Information System (INIS)

Lee, Yoon Joon; Cho, Kyung Ho

1995-01-01

A classical PID controller is designed by applying the GA (Genetic Algorithm) which searches the optimal parameters through three major operators of reproduction, crossover and mutation under the given constraints. The GA could minimize the designer's interference and the whole design process could easily be automated. In contrast with other traditional PID design methods which allows for the system output responses only, the design with the GA can take account of the magnitude or the rate of change of control input together with the output responses, which reflects the more realistic situations. Compared with other PIDs designed by the traditional methods such as Ziegler and analytic, the PID by the GA shows the superior response characteristics to those of others with the least control input energy

The algorithms for control of heating massive material

Directory of Open Access Journals (Sweden)

Karol Kostúr

2008-03-01

Full Text Available In numerous technological processes a change on the output follows change on the input pending specific time. This time is called dead time and if this time is too large, it causes problems in the control. This contribution is aimed at analyzing the algorithms of discreet regulation of the systems with dead time. Verified were classical PID regulator and a regulator using Dead Beat method. The control was also tried with Dead interval method. The regulators were tested by simulation and in the electrical laboratory furnace. The task was to control the temperature inside the material heated by furnace power.

An Improvement of a Fuzzy Logic-Controlled Maximum Power Point Tracking Algorithm for Photovoltic Applications

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Woonki Na

2017-03-01

Full Text Available This paper presents an improved maximum power point tracking (MPPT algorithm using a fuzzy logic controller (FLC in order to extract potential maximum power from photovoltaic cells. The objectives of the proposed algorithm are to improve the tracking speed, and to simultaneously solve the inherent drawbacks such as slow tracking in the conventional perturb and observe (P and O algorithm. The performances of the conventional P and O algorithm and the proposed algorithm are compared by using MATLAB/Simulink in terms of the tracking speed and steady-state oscillations. Additionally, both algorithms were experimentally validated through a digital signal processor (DSP-based controlled-boost DC-DC converter. The experimental results show that the proposed algorithm performs with a shorter tracking time, smaller output power oscillation, and higher efficiency, compared with the conventional P and O algorithm.

Combined Intelligent Control (CIC an Intelligent Decision Making Algorithm

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Moteaal Asadi Shirzi

2007-03-01

Full Text Available The focus of this research is to introduce the concept of combined intelligent control (CIC as an effective architecture for decision-making and control of intelligent agents and multi-robot sets. Basically, the CIC is a combination of various architectures and methods from fields such as artificial intelligence, Distributed Artificial Intelligence (DAI, control and biological computing. Although any intelligent architecture may be very effective for some specific applications, it could be less for others. Therefore, CIC combines and arranges them in a way that the strengths of any approach cover the weaknesses of others. In this paper first, we introduce some intelligent architectures from a new aspect. Afterward, we offer the CIC by combining them. CIC has been executed in a multi-agent set. In this set, robots must cooperate to perform some various tasks in a complex and nondeterministic environment with a low sensory feedback and relationship. In order to investigate, improve, and correct the combined intelligent control method, simulation software has been designed which will be presented and considered. To show the ability of the CIC algorithm as a distributed architecture, a central algorithm is designed and compared with the CIC.

A fuzzy controller design for nuclear research reactors using the particle swarm optimization algorithm

International Nuclear Information System (INIS)

Coban, Ramazan

2011-01-01

Research highlights: → A closed-loop fuzzy logic controller based on the particle swarm optimization algorithm was proposed for controlling the power level of nuclear research reactors. → The proposed control system was tested for various initial and desired power levels, and it could control the reactor successfully for most situations. → The proposed controller is robust against the disturbances. - Abstract: In this paper, a closed-loop fuzzy logic controller based on the particle swarm optimization algorithm is proposed for controlling the power level of nuclear research reactors. The principle of the fuzzy logic controller is based on the rules constructed from numerical experiments made by means of a computer code for the core dynamics calculation and from human operator's experience and knowledge. In addition to these intuitive and experimental design efforts, consequent parts of the fuzzy rules are optimally (or near optimally) determined using the particle swarm optimization algorithm. The contribution of the proposed algorithm to a reactor control system is investigated in details. The performance of the controller is also tested with numerical simulations in numerous operating conditions from various initial power levels to desired power levels, as well as under disturbance. It is shown that the proposed control system performs satisfactorily under almost all operating conditions, even in the case of very small initial power levels.

Development and analysis of an economizer control strategy algorithm to promote an opportunity for energy savings in air conditioning installations

Energy Technology Data Exchange (ETDEWEB)

Neto, Jose H.M.; Azevedo, Walter L. [Centro Federal de Educacao Tecnologica de Minas Gerais (CEFET), Belo Horizonte, MG (Brazil). Dept. de Engenharia Mecanica]. E-mail: henrique@daem.des.cefetmg.br

2000-07-01

This work presents an algorithm control strategy denominated enthalpy economizer. The objective of this algorithm strategy is to determine the adequate fractions of outside and return air flowrates entering a cooling coil based on the analysis of the outside, return and supply air enthalpies, rather than on the analysis of the dry bulb temperatures. The proposed algorithm predicts the actual opening position of the outside and return air dampers in order to provide the lower mixing air enthalpy. First, the psychometrics properties of the outside and return air are calculated from actual measurements of the dry and wet bulb temperatures. Then, three distinct cases are analyzed: the enthalpy of the outside air is lower than the enthalpy of the supply air (free cooling); the enthalpy of the outside air is higher than the enthalpy of the return air; the enthalpy of the outside air is lower than the enthalpy of the return air and higher than the temperature of the supply air. Different outside air conditions were selected in order to represent typical weather data of Brazilians cities, as well as typical return air conditions. It was found that the enthalpy control strategy could promote an opportunity for energy savings mainly during mild nights and wintertime periods as well as during warm afternoons and summertime periods, depending on the outside air relative humidity. The proposed algorithm works well and can be integrated in some commercial automation software to reduce energy consumption and electricity demand. (author)

Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay.

Science.gov (United States)

Pan, Indranil; Das, Saptarshi; Gupta, Amitava

2011-01-01

An optimal PID and an optimal fuzzy PID have been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output for a networked control system (NCS). The tuning is attempted for a higher order and a time delay system using two stochastic algorithms viz. the Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) and the closed loop performances are compared. The paper shows that random variation in network delay can be handled efficiently with fuzzy logic based PID controllers over conventional PID controllers. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.

Analysis of the Command and Control Segment (CCS) attitude estimation algorithm

Science.gov (United States)

Stockwell, Catherine

1993-01-01

This paper categorizes the qualitative behavior of the Command and Control Segment (CCS) differential correction algorithm as applied to attitude estimation using simultaneous spin axis sun angle and Earth cord length measurements. The categories of interest are the domains of convergence, divergence, and their boundaries. Three series of plots are discussed that show the dependence of the estimation algorithm on the vehicle radius, the sun/Earth angle, and the spacecraft attitude. Common qualitative dynamics to all three series are tabulated and discussed. Out-of-limits conditions for the estimation algorithm are identified and discussed.

A parallel adaptive quantum genetic algorithm for the controllability of arbitrary networks

Science.gov (United States)

Li, Yuhong

2018-01-01

In this paper, we propose a novel algorithm—parallel adaptive quantum genetic algorithm—which can rapidly determine the minimum control nodes of arbitrary networks with both control nodes and state nodes. The corresponding network can be fully controlled with the obtained control scheme. We transformed the network controllability issue into a combinational optimization problem based on the Popov-Belevitch-Hautus rank condition. A set of canonical networks and a list of real-world networks were experimented. Comparison results demonstrated that the algorithm was more ideal to optimize the controllability of networks, especially those larger-size networks. We demonstrated subsequently that there were links between the optimal control nodes and some network statistical characteristics. The proposed algorithm provides an effective approach to improve the controllability optimization of large networks or even extra-large networks with hundreds of thousands nodes. PMID:29554140

Application of the tuning algorithm with the least squares approximation to the suboptimal control algorithm for integrating objects

Science.gov (United States)

Kuzishchin, V. F.; Merzlikina, E. I.; Van Va, Hoang

2017-11-01

The problem of PID and PI-algorithms tuning by means of the approximation by the least square method of the frequency response of a linear algorithm to the sub-optimal algorithm is considered. The advantage of the method is that the parameter values are obtained through one cycle of calculation. Recommendations how to choose the parameters of the least square method taking into consideration the plant dynamics are given. The parameters mentioned are the time constant of the filter, the approximation frequency range and the correction coefficient for the time delay parameter. The problem is considered for integrating plants for some practical cases (the level control system in a boiler drum). The transfer function of the suboptimal algorithm is determined relating to the disturbance that acts in the point of the control impact input, it is typical for thermal plants. In the recommendations it is taken into consideration that the overregulation for the transient process when the setpoint is changed is also limited. In order to compare the results the systems under consideration are also calculated by the classical method with the limited frequency oscillation index. The results given in the paper can be used by specialists dealing with tuning systems with the integrating plants.

Quadratic Stabilization of LPV System by an LTI Controller Based on ILMI Algorithm

Directory of Open Access Journals (Sweden)

Wei Xie

2007-01-01

Full Text Available A linear time-invariant (LTI output feedback controller is designed for a linear parameter-varying (LPV control system to achieve quadratic stability. The LPV system includes immeasurable dependent parameters that are assumed to vary in a polytopic space. To solve this control problem, a heuristic algorithm is proposed in the form of an iterative linear matrix inequality (ILMI formulation. Furthermore, an effective method of setting an initial value of the ILMI algorithm is also proposed to increase the probability of getting an admissible solution for the controller design problem.

Implementation of advanced feedback control algorithms for controlled resonant magnetic perturbation physics studies on EXTRAP T2R

International Nuclear Information System (INIS)

Frassinetti, L.; Olofsson, K.E.J.; Brunsell, P.R.; Drake, J.R.

2011-01-01

The EXTRAP T2R feedback system (active coils, sensor coils and controller) is used to study and develop new tools for advanced control of the MHD instabilities in fusion plasmas. New feedback algorithms developed in EXTRAP T2R reversed-field pinch allow flexible and independent control of each magnetic harmonic. Methods developed in control theory and applied to EXTRAP T2R allow a closed-loop identification of the machine plant and of the resistive wall modes growth rates. The plant identification is the starting point for the development of output-tracking algorithms which enable the generation of external magnetic perturbations. These algorithms will then be used to study the effect of a resonant magnetic perturbation (RMP) on the tearing mode (TM) dynamics. It will be shown that the stationary RMP can induce oscillations in the amplitude and jumps in the phase of the rotating TM. It will be shown that the RMP strongly affects the magnetic island position.

Implementation of advanced feedback control algorithms for controlled resonant magnetic perturbation physics studies on EXTRAP T2R

Science.gov (United States)

Frassinetti, L.; Olofsson, K. E. J.; Brunsell, P. R.; Drake, J. R.

2011-06-01

The EXTRAP T2R feedback system (active coils, sensor coils and controller) is used to study and develop new tools for advanced control of the MHD instabilities in fusion plasmas. New feedback algorithms developed in EXTRAP T2R reversed-field pinch allow flexible and independent control of each magnetic harmonic. Methods developed in control theory and applied to EXTRAP T2R allow a closed-loop identification of the machine plant and of the resistive wall modes growth rates. The plant identification is the starting point for the development of output-tracking algorithms which enable the generation of external magnetic perturbations. These algorithms will then be used to study the effect of a resonant magnetic perturbation (RMP) on the tearing mode (TM) dynamics. It will be shown that the stationary RMP can induce oscillations in the amplitude and jumps in the phase of the rotating TM. It will be shown that the RMP strongly affects the magnetic island position.

Study of On-Ramp PI Controller Based on Dural Group QPSO with Different Well Centers Algorithm

Directory of Open Access Journals (Sweden)

Tao Wu

2015-01-01

Full Text Available A novel quantum-behaved particle swarm optimization (QPSO algorithm, dual-group QPSO with different well centers (DWC-QPSO algorithm, is proposed by constructing the master-slave subswarms. The new algorithm was applied in the parameter optimization of on-ramp traffic PI controller combining with nonlinear feedback theory. With the critical information contained in the searching space and results of the basic QPSO algorithm, this algorithm avoids the rapid disappearance of swarm diversity and enhances the global searching ability through collaboration between subswarms. Experiment results on an on-ramp traffic control simulation show that DWC-QPSO can be well applied in the study of on-ramp traffic PI controller and the comparison results illustrate that DWC-QPSO outperforms other evolutionary algorithms with enhancement in both adaptability and stability.

Algorithm of Particle Data Association for SLAM Based on Improved Ant Algorithm

Directory of Open Access Journals (Sweden)

KeKe Gen

2015-01-01

Full Text Available The article considers a problem of data association algorithm for simultaneous localization and mapping guidelines in determining the route of unmanned aerial vehicles (UAVs. Currently, these equipments are already widely used, but mainly controlled from the remote operator. An urgent task is to develop a control system that allows for autonomous flight. Algorithm SLAM (simultaneous localization and mapping, which allows to predict the location, speed, the ratio of flight parameters and the coordinates of landmarks and obstacles in an unknown environment, is one of the key technologies to achieve real autonomous UAV flight. The aim of this work is to study the possibility of solving this problem by using an improved ant algorithm.The data association for SLAM algorithm is meant to establish a matching set of observed landmarks and landmarks in the state vector. Ant algorithm is one of the widely used optimization algorithms with positive feedback and the ability to search in parallel, so the algorithm is suitable for solving the problem of data association for SLAM. But the traditional ant algorithm in the process of finding routes easily falls into local optimum. Adding random perturbations in the process of updating the global pheromone to avoid local optima. Setting limits pheromone on the route can increase the search space with a reasonable amount of calculations for finding the optimal route.The paper proposes an algorithm of the local data association for SLAM algorithm based on an improved ant algorithm. To increase the speed of calculation, local data association is used instead of the global data association. The first stage of the algorithm defines targets in the matching space and the observed landmarks with the possibility of association by the criterion of individual compatibility (IC. The second stage defines the matched landmarks and their coordinates using improved ant algorithm. Simulation results confirm the efficiency and

A Feedback Optimal Control Algorithm with Optimal Measurement Time Points

Directory of Open Access Journals (Sweden)

Felix Jost

2017-02-01

Full Text Available Nonlinear model predictive control has been established as a powerful methodology to provide feedback for dynamic processes over the last decades. In practice it is usually combined with parameter and state estimation techniques, which allows to cope with uncertainty on many levels. To reduce the uncertainty it has also been suggested to include optimal experimental design into the sequential process of estimation and control calculation. Most of the focus so far was on dual control approaches, i.e., on using the controls to simultaneously excite the system dynamics (learning as well as minimizing a given objective (performing. We propose a new algorithm, which sequentially solves robust optimal control, optimal experimental design, state and parameter estimation problems. Thus, we decouple the control and the experimental design problems. This has the advantages that we can analyze the impact of measurement timing (sampling independently, and is practically relevant for applications with either an ethical limitation on system excitation (e.g., chemotherapy treatment or the need for fast feedback. The algorithm shows promising results with a 36% reduction of parameter uncertainties for the Lotka-Volterra fishing benchmark example.

Energy Optimal Control Strategy of PHEV Based on PMP Algorithm

Directory of Open Access Journals (Sweden)

Tiezhou Wu

2017-01-01

Full Text Available Under the global voice of “energy saving” and the current boom in the development of energy storage technology at home and abroad, energy optimal control of the whole hybrid electric vehicle power system, as one of the core technologies of electric vehicles, is bound to become a hot target of “clean energy” vehicle development and research. This paper considers the constraints to the performance of energy storage system in Parallel Hybrid Electric Vehicle (PHEV, from which lithium-ion battery frequently charges/discharges, PHEV largely consumes energy of fuel, and their are difficulty in energy recovery and other issues in a single cycle; the research uses lithium-ion battery combined with super-capacitor (SC, which is hybrid energy storage system (Li-SC HESS, working together with internal combustion engine (ICE to drive PHEV. Combined with PSO-PI controller and Li-SC HESS internal power limited management approach, the research proposes the PHEV energy optimal control strategy. It is based on revised Pontryagin’s minimum principle (PMP algorithm, which establishes the PHEV vehicle simulation model through ADVISOR software and verifies the effectiveness and feasibility. Finally, the results show that the energy optimization control strategy can improve the instantaneity of tracking PHEV minimum fuel consumption track, implement energy saving, and prolong the life of lithium-ion batteries and thereby can improve hybrid energy storage system performance.

Control of the lighting system using a genetic algorithm

Directory of Open Access Journals (Sweden)

Čongradac Velimir D.

2012-01-01

Full Text Available The manufacturing, distribution and use of electricity are of fundamental importance for the social life and they have the biggest influence on the environment associated with any human activity. The energy needed for building lighting makes up 20-40% of the total consumption. This paper displays the development of the mathematical model and genetic algorithm for the control of dimmable lighting on problems of regulating the level of internal lighting and increase of energetic efficiency using daylight. A series of experiments using the optimization algorithm on the realized model confirmed very high savings in electricity consumption.

Research on digital PID control algorithm for HPCT

International Nuclear Information System (INIS)

Zeng Yi; Li Rui; Shen Tianjian; Ke Xinhua

2009-01-01

Digital PID applied in high-precision HPCT (High-precision current transducer) based on Digital Signal Processor (DSP) TMS320F2812 and special D/A converter was researched. By using increment style PID Control algorithm, the stability and precision of high-precision HPCT output voltage is improved. On basis of deeply analysing incremental digital PID, the scheme model of HPCT is proposed, the feasibility simulation using Matlab is given. Practical hardware circuit verified the incremental PID has closed-loop control process in tracking HPCT output voltage. (authors)

Meta-heuristic algorithms for parallel identical machines scheduling problem with weighted late work criterion and common due date.

Science.gov (United States)

Xu, Zhenzhen; Zou, Yongxing; Kong, Xiangjie

2015-01-01

To our knowledge, this paper investigates the first application of meta-heuristic algorithms to tackle the parallel machines scheduling problem with weighted late work criterion and common due date ([Formula: see text]). Late work criterion is one of the performance measures of scheduling problems which considers the length of late parts of particular jobs when evaluating the quality of scheduling. Since this problem is known to be NP-hard, three meta-heuristic algorithms, namely ant colony system, genetic algorithm, and simulated annealing are designed and implemented, respectively. We also propose a novel algorithm named LDF (largest density first) which is improved from LPT (longest processing time first). The computational experiments compared these meta-heuristic algorithms with LDF, LPT and LS (list scheduling), and the experimental results show that SA performs the best in most cases. However, LDF is better than SA in some conditions, moreover, the running time of LDF is much shorter than SA.

Quantum control using genetic algorithms in quantum communication: superdense coding

International Nuclear Information System (INIS)

Domínguez-Serna, Francisco; Rojas, Fernando

2015-01-01

We present a physical example model of how Quantum Control with genetic algorithms is applied to implement the quantum superdense code protocol. We studied a model consisting of two quantum dots with an electron with spin, including spin-orbit interaction. The electron and the spin get hybridized with the site acquiring two degrees of freedom, spin and charge. The system has tunneling and site energies as time dependent control parameters that are optimized by means of genetic algorithms to prepare a hybrid Bell-like state used as a transmission channel. This state is transformed to obtain any state of the four Bell basis as required by superdense protocol to transmit two bits of classical information. The control process protocol is equivalent to implement one of the quantum gates in the charge subsystem. Fidelities larger than 99.5% are achieved for the hybrid entangled state preparation and the superdense operations. (paper)

Synthesis of sequential control algorithms for pneumatic drives controlled by monostable valves

Directory of Open Access Journals (Sweden)

Ł. Dworzak

2009-07-01

Full Text Available Application of the Grafpol method [1] for synthesising sequential control algorithms for pneumatic drives controlled by monostable valves is presented. The developed principles simplify the MTS method of programming production processes in the scope of the memory realisation [2]. Thanks to this, time for synthesising the schematic equation can be significantly reduced in comparison to the network transformation method [3]. The designed schematic equation makes a ground for writing an application program of a PLC using any language defined in IEC 61131-3.

Design Genetic Algorithm Optimization Education Software Based Fuzzy Controller for a Tricopter Fly Path Planning

Science.gov (United States)

Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao

2016-01-01

In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…

Modeling and Sensitivity Study of Consensus Algorithm-Based Distributed Hierarchical Control for DC Microgrids

DEFF Research Database (Denmark)

Meng, Lexuan; Dragicevic, Tomislav; Roldan Perez, Javier

2016-01-01

Distributed control methods based on consensus algorithms have become popular in recent years for microgrid (MG) systems. These kinds of algorithms can be applied to share information in order to coordinate multiple distributed generators within a MG. However, stability analysis becomes a challen......Distributed control methods based on consensus algorithms have become popular in recent years for microgrid (MG) systems. These kinds of algorithms can be applied to share information in order to coordinate multiple distributed generators within a MG. However, stability analysis becomes...... in the communication network, continuous-time methods can be inaccurate for this kind of dynamic study. Therefore, this paper aims at modeling a complete DC MG using a discrete-time approach in order to perform a sensitivity analysis taking into account the effects of the consensus algorithm. To this end......, a generalized modeling method is proposed and the influence of key control parameters, the communication topology and the communication speed are studied in detail. The theoretical results obtained with the proposed model are verified by comparing them with the results obtained with a detailed switching...

The development of controller and navigation algorithm for underwater wall crawler

Energy Technology Data Exchange (ETDEWEB)

Cho, Hyung Suck; Kim, Kyung Hoon; Kim, Min Young [Korea Advanced Institute of Science and Technology, Taejon (Korea)

1999-01-01

In this project, the control system of a underwater robotic vehicle(URV) for underwater wall inspection in the nuclear reactor pool or the related facilities has been developed. The following 4-sub projects have been studied for this project: (1) Development of the controller and motor driver for the URV (2) Development of the control algorithm for the tracking control of the URV (3) Development of the localization system (4) Underwater experiments of the developed system. First, the dynamic characteristic of thruster with the DC servo-motor was analyzed experimentally. Second the controller board using the INTEL 80C196 was designed and constructed, and the software for the communication and motor control is developed. Third the PWM motor-driver was developed. Fourth the localization system using the laser scanner and inclinometer was developed and tested in the pool. Fifth the dynamics of the URV was studied and the proper control algorithms for the URV was proposed. Lastly the validation of the integrated system was experimentally performed. (author). 27 refs., 51 figs., 8 tabs.

Artificial intelligence programming with LabVIEW: genetic algorithms for instrumentation control and optimization.

Science.gov (United States)

Moore, J H

1995-06-01

A genetic algorithm for instrumentation control and optimization was developed using the LabVIEW graphical programming environment. The usefulness of this methodology for the optimization of a closed loop control instrument is demonstrated with minimal complexity and the programming is presented in detail to facilitate its adaptation to other LabVIEW applications. Closed loop control instruments have variety of applications in the biomedical sciences including the regulation of physiological processes such as blood pressure. The program presented here should provide a useful starting point for those wishing to incorporate genetic algorithm approaches to LabVIEW mediated optimization of closed loop control instruments.

A parallel neural network training algorithm for control of discrete dynamical systems.

Energy Technology Data Exchange (ETDEWEB)

Gordillo, J. L.; Hanebutte, U. R.; Vitela, J. E.

1998-01-20

In this work we present a parallel neural network controller training code, that uses MPI, a portable message passing environment. A comprehensive performance analysis is reported which compares results of a performance model with actual measurements. The analysis is made for three different load assignment schemes: block distribution, strip mining and a sliding average bin packing (best-fit) algorithm. Such analysis is crucial since optimal load balance can not be achieved because the work load information is not available a priori. The speedup results obtained with the above schemes are compared with those corresponding to the bin packing load balance scheme with perfect load prediction based on a priori knowledge of the computing effort. Two multiprocessor platforms: a SGI/Cray Origin 2000 and a IBM SP have been utilized for this study. It is shown that for the best load balance scheme a parallel efficiency of over 50% for the entire computation is achieved by 17 processors of either parallel computers.

Semi-flocking algorithm for motion control of mobile sensors in large-scale surveillance systems.

Science.gov (United States)

Semnani, Samaneh Hosseini; Basir, Otman A

2015-01-01

The ability of sensors to self-organize is an important asset in surveillance sensor networks. Self-organize implies self-control at the sensor level and coordination at the network level. Biologically inspired approaches have recently gained significant attention as a tool to address the issue of sensor control and coordination in sensor networks. These approaches are exemplified by the two well-known algorithms, namely, the Flocking algorithm and the Anti-Flocking algorithm. Generally speaking, although these two biologically inspired algorithms have demonstrated promising performance, they expose deficiencies when it comes to their ability to maintain simultaneous robust dynamic area coverage and target coverage. These two coverage performance objectives are inherently conflicting. This paper presents Semi-Flocking, a biologically inspired algorithm that benefits from key characteristics of both the Flocking and Anti-Flocking algorithms. The Semi-Flocking algorithm approaches the problem by assigning a small flock of sensors to each target, while at the same time leaving some sensors free to explore the environment. This allows the algorithm to strike balance between robust area coverage and target coverage. Such balance is facilitated via flock-sensor coordination. The performance of the proposed Semi-Flocking algorithm is examined and compared with other two flocking-based algorithms once using randomly moving targets and once using a standard walking pedestrian dataset. The results of both experiments show that the Semi-Flocking algorithm outperforms both the Flocking algorithm and the Anti-Flocking algorithm with respect to the area of coverage and the target coverage objectives. Furthermore, the results show that the proposed algorithm demonstrates shorter target detection time and fewer undetected targets than the other two flocking-based algorithms.

Nuclear power control system design using genetic algorithm

International Nuclear Information System (INIS)

Lee, Yoon Joon; Cho, Kyung Ho

1996-01-01

The genetic algorithm(GA) is applied to the design of the nuclear power control system. The reactor control system model is described in the LQR configuration. The LQR system order is increased to make the tracking system. The key parameters of the design are weighting matrices, and these are usually determined through numerous simulations in the conventional design. To determine the more objective and optimal weightings, the improved GA is applied. The results show that the weightings determined by the GA yield the better system responses than those obtained by the conventional design method

A neuro-fuzzy controlling algorithm for wind turbine

Energy Technology Data Exchange (ETDEWEB)

Lin, Li [Tampere Univ. of Technology (Finland); Eriksson, J T [Tampere Univ. of Technology (Finland)

1996-12-31

The wind turbine control system is stochastic and nonlinear, offering a demanding field for different control methods. An improved and efficient controller will have great impact on the cost-effectiveness of the technology. In this article, a design method for a self-organizing fuzzy controller is discussed, which combines two popular computational intelligence techniques, neural networks and fuzzy logic. Based on acquisited dynamic parameters of the wind, it can effectively predict wind changes in speed and direction. Maximum power can always be extracted from the kinetic energy of the wind. Based on the stimulating experiments applying nonlinear dynamics to a `Variable Speed Fixed Angle` wind turbine, it is demonstrated that the proposed control model 3rd learning algorithm provide a predictable, stable and accurate performance. The robustness of the controller to system parameter variations and measurement disturbances is also discussed. (author)

A neuro-fuzzy controlling algorithm for wind turbine

Energy Technology Data Exchange (ETDEWEB)

Li Lin [Tampere Univ. of Technology (Finland); Eriksson, J.T. [Tampere Univ. of Technology (Finland)

1995-12-31

The wind turbine control system is stochastic and nonlinear, offering a demanding field for different control methods. An improved and efficient controller will have great impact on the cost-effectiveness of the technology. In this article, a design method for a self-organizing fuzzy controller is discussed, which combines two popular computational intelligence techniques, neural networks and fuzzy logic. Based on acquisited dynamic parameters of the wind, it can effectively predict wind changes in speed and direction. Maximum power can always be extracted from the kinetic energy of the wind. Based on the stimulating experiments applying nonlinear dynamics to a `Variable Speed Fixed Angle` wind turbine, it is demonstrated that the proposed control model 3rd learning algorithm provide a predictable, stable and accurate performance. The robustness of the controller to system parameter variations and measurement disturbances is also discussed. (author)

Study on improved Ip-iq APF control algorithm and its application in micro grid

Science.gov (United States)

Xie, Xifeng; Shi, Hua; Deng, Haiyingv

2018-01-01

In order to enhance the tracking velocity and accuracy of harmonic detection by ip-iq algorithm, a novel ip-iq control algorithm based on the Instantaneous reactive power theory is presented, the improved algorithm adds the lead correction link to adjust the zero point of the detection system, the Fuzzy Self-Tuning Adaptive PI control is introduced to dynamically adjust the DC-link Voltage, which meets the requirement of the harmonic compensation of the micro grid. Simulation and experimental results verify the proposed method is feasible and effective in micro grid.

Guidance and Control Algorithms for the Mars Entry, Descent and Landing Systems Analysis

Science.gov (United States)

Davis, Jody L.; CwyerCianciolo, Alicia M.; Powell, Richard W.; Shidner, Jeremy D.; Garcia-Llama, Eduardo

2010-01-01

The purpose of the Mars Entry, Descent and Landing Systems Analysis (EDL-SA) study was to identify feasible technologies that will enable human exploration of Mars, specifically to deliver large payloads to the Martian surface. This paper focuses on the methods used to guide and control two of the contending technologies, a mid- lift-to-drag (L/D) rigid aeroshell and a hypersonic inflatable aerodynamic decelerator (HIAD), through the entry portion of the trajectory. The Program to Optimize Simulated Trajectories II (POST2) is used to simulate and analyze the trajectories of the contending technologies and guidance and control algorithms. Three guidance algorithms are discussed in this paper: EDL theoretical guidance, Numerical Predictor-Corrector (NPC) guidance and Analytical Predictor-Corrector (APC) guidance. EDL-SA also considered two forms of control: bank angle control, similar to that used by Apollo and the Space Shuttle, and a center-of-gravity (CG) offset control. This paper presents the performance comparison of these guidance algorithms and summarizes the results as they impact the technology recommendations for future study.

A new home energy management algorithm with voltage control in a smart home environment

International Nuclear Information System (INIS)

Elma, Onur; Selamogullari, Ugur Savas

2015-01-01

Energy management in electrical systems is one of the important issues for energy efficiency and future grid systems. Energy management is defined as a HEM (home energy management) on the residential consumer side. The HEM system plays a key role in residential demand response applications. In this study, a new HEM algorithm is proposed for smart home environments to reduce peak demand and increase the energy efficiency. The proposed algorithm includes VC (voltage control) methodology to reduce the power consumption of residential appliances so that the shifting of appliances is minimized. The results of the survey are used to produce representative load profiles for a weekday and for a weekend. Then, case studies are completed to test the proposed HEM algorithm in reducing the peak demand in the house. The main aim of the proposed HEM algorithm is to minimize the number of turned-off appliances to decrease demand so that the customer comfort is maximized. The smart home laboratory at Yildiz Technical University, Istanbul, Turkey is used in case studies. Experimental results show that the proposed HEM algorithm reduces the peak demand by 17.5% with the voltage control and by 38% with both the voltage control and the appliance shifting. - Highlights: • A new HEM (home energy management) algorithm is proposed. • Voltage control in the HEM is introduced as a solution for peak load reduction. • Customer comfort is maximized by minimizing the number of turned-off appliances. • The proposed HEM algorithm is experimentally validated at a smart home laboratory. • A survey is completed to produce typical load profiles of a Turkish family.

Experimental implementation of a robust damped-oscillation control algorithm on a full-sized, two-degree-of-freedom, AC induction motor-driven crane

International Nuclear Information System (INIS)

Kress, R.L.; Jansen, J.F.; Noakes, M.W.

1994-01-01

When suspended payloads are moved with an overhead crane, pendulum like oscillations are naturally introduced. This presents a problem any time a crane is used, especially when expensive and/or delicate objects are moved, when moving in a cluttered an or hazardous environment, and when objects are to be placed in tight locations. Damped-oscillation control algorithms have been demonstrated over the past several years for laboratory-scale robotic systems on dc motor-driven overhead cranes. Most overhead cranes presently in use in industry are driven by ac induction motors; consequently, Oak Ridge National Laboratory has implemented damped-oscillation crane control on one of its existing facility ac induction motor-driven overhead cranes. The purpose of this test was to determine feasibility, to work out control and interfacing specifications, and to establish the capability of newly available ac motor control hardware with respect to use in damped-oscillation-controlled systems. Flux vector inverter drives are used to investigate their acceptability for damped-oscillation crane control. The purpose of this paper is to describe the experimental implementation of a control algorithm on a full-sized, two-degree-of-freedom, industrial crane; describe the experimental evaluation of the controller including robustness to payload length changes; explain the results of experiments designed to determine the hardware required for implementation of the control algorithms; and to provide a theoretical description of the controller

Optimal control of hybrid qubits: Implementing the quantum permutation algorithm

Science.gov (United States)

Rivera-Ruiz, C. M.; de Lima, E. F.; Fanchini, F. F.; Lopez-Richard, V.; Castelano, L. K.

2018-03-01

The optimal quantum control theory is employed to determine electric pulses capable of producing quantum gates with a fidelity higher than 0.9997, when noise is not taken into account. Particularly, these quantum gates were chosen to perform the permutation algorithm in hybrid qubits in double quantum dots (DQDs). The permutation algorithm is an oracle based quantum algorithm that solves the problem of the permutation parity faster than a classical algorithm without the necessity of entanglement between particles. The only requirement for achieving the speedup is the use of a one-particle quantum system with at least three levels. The high fidelity found in our results is closely related to the quantum speed limit, which is a measure of how fast a quantum state can be manipulated. Furthermore, we model charge noise by considering an average over the optimal field centered at different values of the reference detuning, which follows a Gaussian distribution. When the Gaussian spread is of the order of 5 μ eV (10% of the correct value), the fidelity is still higher than 0.95. Our scheme also can be used for the practical realization of different quantum algorithms in DQDs.

Development of fuzzy algorithm with learning function for nuclear steam generator level control

International Nuclear Information System (INIS)

Park, Gee Yong; Seong, Poong Hyun

1993-01-01

A fuzzy algorithm with learning function is applied to the steam generator level control of nuclear power plant. This algorithm can make its rule base and membership functions suited for steam generator level control by use of the data obtained from the control actions of a skilled operator or of other controllers (i.e., PID controller). The rule base of fuzzy controller with learning function is divided into two parts. One part of the rule base is provided to level control of steam generator at low power level (0 % - 30 % of full power) and the other to level control at high power level (30 % - 100 % of full power). Response time of steam generator level control at low power range with this rule base is shown to be shorter than that of fuzzy controller with direct inference. (Author)

Balancing Inverted Pendulum by Angle Sensing Using Fuzzy Logic Supervised PID Controller Optimized by Genetic Algorithm

Directory of Open Access Journals (Sweden)

Ashutosh K. AGARWAL

2011-10-01

Full Text Available Genetic algorithms are robust search techniques based on the principles of evolution. A genetic algorithm maintains a population of encoded solutions and guides the population towards the optimum solution. This important property of genetic algorithm is used in this paper to stabilize the Inverted pendulum system. This paper highlights the application and stability of inverted pendulum using PID controller with fuzzy logic genetic algorithm supervisor . There are a large number of well established search techniques in use within the information technology industry. We propose a method to control inverted pendulum steady state error and overshoot using genetic algorithm technique.

Learning-based traffic signal control algorithms with neighborhood information sharing: An application for sustainable mobility

Energy Technology Data Exchange (ETDEWEB)

Aziz, H. M. Abdul [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Zhu, Feng [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering; Ukkusuri, Satish V. [Purdue University, West Lafayette, IN (United States). Lyles School of Civil Engineering

2017-10-04

Here, this research applies R-Markov Average Reward Technique based reinforcement learning (RL) algorithm, namely RMART, for vehicular signal control problem leveraging information sharing among signal controllers in connected vehicle environment. We implemented the algorithm in a network of 18 signalized intersections and compare the performance of RMART with fixed, adaptive, and variants of the RL schemes. Results show significant improvement in system performance for RMART algorithm with information sharing over both traditional fixed signal timing plans and real time adaptive control schemes. Additionally, the comparison with reinforcement learning algorithms including Q learning and SARSA indicate that RMART performs better at higher congestion levels. Further, a multi-reward structure is proposed that dynamically adjusts the reward function with varying congestion states at the intersection. Finally, the results from test networks show significant reduction in emissions (CO, CO₂, NO_x, VOC, PM₁₀) when RL algorithms are implemented compared to fixed signal timings and adaptive schemes.

A novel adaptive joint power control algorithm with channel estimation in a CDMA cellular system

Institute of Scientific and Technical Information of China (English)

无

2005-01-01

Joint power control has advantages of multi-user detection and power control; and it can combat the multi-access interference and the near-far problem. A novel adaptive joint power control algorithm with channel estimation in a CDMA cellular system was designed. Simulation results show that the algorithm can control the power not only quickly but also precisely with a time change. The method is useful for increasing system capacity.

Filtered-X Affine Projection Algorithms for Active Noise Control Using Volterra Filters

Directory of Open Access Journals (Sweden)

Sicuranza Giovanni L

2004-01-01

Full Text Available We consider the use of adaptive Volterra filters, implemented in the form of multichannel filter banks, as nonlinear active noise controllers. In particular, we discuss the derivation of filtered-X affine projection algorithms for homogeneous quadratic filters. According to the multichannel approach, it is then easy to pass from these algorithms to those of a generic Volterra filter. It is shown in the paper that the AP technique offers better convergence and tracking capabilities than the classical LMS and NLMS algorithms usually applied in nonlinear active noise controllers, with a limited complexity increase. This paper extends in two ways the content of a previous contribution published in Proc. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03, Grado, Italy, June 2003. First of all, a general adaptation algorithm valid for any order of affine projections is presented. Secondly, a more complete set of experiments is reported. In particular, the effects of using multichannel filter banks with a reduced number of channels are investigated and relevant results are shown.

A Probabilistic and Highly Efficient Topology Control Algorithm for Underwater Cooperating AUV Networks.

Science.gov (United States)

Li, Ning; Cürüklü, Baran; Bastos, Joaquim; Sucasas, Victor; Fernandez, Jose Antonio Sanchez; Rodriguez, Jonathan

2017-05-04

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV's parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power

A Probabilistic and Highly Efficient Topology Control Algorithm for Underwater Cooperating AUV Networks

Directory of Open Access Journals (Sweden)

Ning Li

2017-05-01

Full Text Available The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs project is to make autonomous underwater vehicles (AUVs, remote operated vehicles (ROVs and unmanned surface vehicles (USVs more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV’s parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the

Research on Segmentation Monitoring Control of IA-RWA Algorithm with Probe Flow

Science.gov (United States)

Ren, Danping; Guo, Kun; Yao, Qiuyan; Zhao, Jijun

2018-04-01

The impairment-aware routing and wavelength assignment algorithm with probe flow (P-IA-RWA) can make an accurate estimation for the transmission quality of the link when the connection request comes. But it also causes some problems. The probe flow data introduced in the P-IA-RWA algorithm can result in the competition for wavelength resources. In order to reduce the competition and the blocking probability of the network, a new P-IA-RWA algorithm with segmentation monitoring-control mechanism (SMC-P-IA-RWA) is proposed. The algorithm would reduce the holding time of network resources for the probe flow. It segments the candidate path suitably for the data transmitting. And the transmission quality of the probe flow sent by the source node will be monitored in the endpoint of each segment. The transmission quality of data can also be monitored, so as to make the appropriate treatment to avoid the unnecessary probe flow. The simulation results show that the proposed SMC-P-IA-RWA algorithm can effectively reduce the blocking probability. It brings a better solution to the competition for resources between the probe flow and the main data to be transferred. And it is more suitable for scheduling control in the large-scale network.

Super-Encryption Implementation Using Monoalphabetic Algorithm and XOR Algorithm for Data Security

Science.gov (United States)

Rachmawati, Dian; Andri Budiman, Mohammad; Aulia, Indra

2018-03-01

The exchange of data that occurs offline and online is very vulnerable to the threat of data theft. In general, cryptography is a science and art to maintain data secrecy. An encryption is a cryptography algorithm in which data is transformed into cipher text, which is something that is unreadable and meaningless so it cannot be read or understood by other parties. In super-encryption, two or more encryption algorithms are combined to make it more secure. In this work, Monoalphabetic algorithm and XOR algorithm are combined to form a super- encryption. Monoalphabetic algorithm works by changing a particular letter into a new letter based on existing keywords while the XOR algorithm works by using logic operation XOR Since Monoalphabetic algorithm is a classical cryptographic algorithm and XOR algorithm is a modern cryptographic algorithm, this scheme is expected to be both easy-to-implement and more secure. The combination of the two algorithms is capable of securing the data and restoring it back to its original form (plaintext), so the data integrity is still ensured.

Automatic brightness control algorithms and their effect on fluoroscopic imaging

International Nuclear Information System (INIS)

Quinn, P.W.; Gagne, R.M.

1989-01-01

This paper reports a computer model used to investigate the effect on dose and image quality of three automatic brightness control (ABC) algorithms used in the imaging of barium during general-purpose fluoroscopy. A model incorporating all aspects of image formation - i.e., x- ray production, phantom attenuation, and energy absorption in the CSI phosphor - was driven according to each ABC algorithm as a function of patient thickness. The energy absorbed in the phosphor was kept constant, while the changes in exposure, integral dose, organ dose, and contrast were monitored

Development of a control algorithm for teleoperation of DFDF(IMEF/M6 hot cell) maintenance equipment

Energy Technology Data Exchange (ETDEWEB)

Oh, Chae Youn; Kwon, Hyuk Jo; Kim, Hak Duck; Jun, Ji Myung; Oh, Hee Geun [Chonbuk National University, Chonju (Korea)

2002-03-01

Teleoperation has been used for separating operators from the working environment. Thus, it is usually used to perform a work in an inaccessible place such as space, deep sea, etc. Also, it is used to perform a work in an accessible but a very poor working environment such as explosive, poison gas, radioactive area, etc. It is one of the advanced technology-intensive research areas. It has potentially big economical and industrial value. There is a tendency to avoid working in a difficult, dirty or dangerous place, particularly, in a high radioactive area since there always exist a possibility to be in a very dangerous situation. Thus, developing and utilizing of a teleoperation system will minimize the possibility to be exposed in such a extreme situation directly. Recently, there has been many researches on reflecting force information occurring in teleoperation to the operator in addition to visual information. The reflected force information is used to control the teleoperation system bilaterally. It will contribute a lot to improve teleoperation's safety and working efficiency. This study developed a bilateral force reflecting control algorithm. It may be used as a key technology of a teleoperation system for maintaining, repairing and dismantling facilities exposed in a high radioactive. 42 refs., 71 figs., 12 tabs. (Author)

A methodology for obtaining the control rod patterns in a BWR using genetic algorithms

International Nuclear Information System (INIS)

Ortiz S, J.J.; Montes T, J.L.; Requena R, I.

2003-01-01

In this work the GACRP system based on the genetic algorithms technique for the obtaining of the drivers of control bars in a BWR reactor is presented. This methodology was applied to a transition cycle and a one of balance of the Laguna Verde nuclear power station (CNLV). For each one of the studied cycles, it was executed the methodology with a fixed length of the cycle and it was compared the effective multiplication factor of neutrons at the end of the cycle that it is obtained with the proposed drivers of control bars and the multiplication factor of neutrons obtained by means of a Haling calculation. It was found that it is possible to extend several days the length of both cycles with regard to the one Haling calculation. (Author)

An Improved PID Algorithm Based on Insulin-on-Board Estimate for Blood Glucose Control with Type 1 Diabetes.

Science.gov (United States)

Hu, Ruiqiang; Li, Chengwei

2015-01-01

Automated closed-loop insulin infusion therapy has been studied for many years. In closed-loop system, the control algorithm is the key technique of precise insulin infusion. The control algorithm needs to be designed and validated. In this paper, an improved PID algorithm based on insulin-on-board estimate is proposed and computer simulations are done using a combinational mathematical model of the dynamics of blood glucose-insulin regulation in the blood system. The simulation results demonstrate that the improved PID algorithm can perform well in different carbohydrate ingestion and different insulin sensitivity situations. Compared with the traditional PID algorithm, the control performance is improved obviously and hypoglycemia can be avoided. To verify the effectiveness of the proposed control algorithm, in silico testing is done using the UVa/Padova virtual patient software.

Position Control of Switched Reluctance Motor Using Super Twisting Algorithm

Directory of Open Access Journals (Sweden)

Muhammad Rafiq Mufti

2016-01-01

Full Text Available The inherent problem of chattering in traditional sliding mode control is harmful for practical application of control system. This paper pays a considerable attention to a chattering-free control method, that is, higher-order sliding mode (super twisting algorithm. The design of a position controller for switched reluctance motor is presented and its stability is assured using Lyapunov stability theorem. In order to highlight the advantages of higher-order sliding mode controller (HOSMC, a classical first-order sliding mode controller (FOSMC is also applied to the same system and compared. The simulation results reflect the effectiveness of the proposed technique.

A cooperative control algorithm for camera based observational systems.

Energy Technology Data Exchange (ETDEWEB)

Young, Joseph G.

2012-01-01

Over the last several years, there has been considerable growth in camera based observation systems for a variety of safety, scientific, and recreational applications. In order to improve the effectiveness of these systems, we frequently desire the ability to increase the number of observed objects, but solving this problem is not as simple as adding more cameras. Quite often, there are economic or physical restrictions that prevent us from adding additional cameras to the system. As a result, we require methods that coordinate the tracking of objects between multiple cameras in an optimal way. In order to accomplish this goal, we present a new cooperative control algorithm for a camera based observational system. Specifically, we present a receding horizon control where we model the underlying optimal control problem as a mixed integer linear program. The benefit of this design is that we can coordinate the actions between each camera while simultaneously respecting its kinematics. In addition, we further improve the quality of our solution by coupling our algorithm with a Kalman filter. Through this integration, we not only add a predictive component to our control, but we use the uncertainty estimates provided by the filter to encourage the system to periodically observe any outliers in the observed area. This combined approach allows us to intelligently observe the entire region of interest in an effective and thorough manner.

Comparative performance analysis of the artificial-intelligence-based thermal control algorithms for the double-skin building

International Nuclear Information System (INIS)

Moon, Jin Woo

2015-01-01

This study aimed at developing artificial-intelligence-(AI)-theory-based optimal control algorithms for improving the indoor temperature conditions and heating energy efficiency of the double-skin buildings. For this, one conventional rule-based and four AI-based algorithms were developed, including artificial neural network (ANN), fuzzy logic (FL), and adaptive neuro fuzzy inference systems (ANFIS), for operating the surface openings of the double skin and the heating system. A numerical computer simulation method incorporating the matrix laboratory (MATLAB) and the transient systems simulation (TRNSYS) software was used for the comparative performance tests. The analysis results revealed that advanced thermal-environment comfort and stability can be provided by the AI-based algorithms. In particular, the FL and ANFIS algorithms were superior to the ANN algorithm in terms of providing better thermal conditions. The ANN-based algorithm, however, proved its potential to be the most energy-efficient and stable strategy among the four AI-based algorithms. It can be concluded that the optimal algorithm can be differently determined according to the major focus of the strategy. If comfortable thermal condition is the principal interest, then the FL or ANFIS algorithm could be the proper solution, and if energy saving for space heating and system operation stability is the main concerns, then the ANN-based algorithm may be applicable. - Highlights: • Integrated control algorithms were developed for the heating system and surface openings. • AI theories were applied to the control algorithms. • ANN, FL, and ANFIS were the applied AI theories. • Comparative performance tests were conducted using computer simulation. • AI algorithms presented superior temperature environment.

Method to evaluate steering and alignment algorithms for controlling emittance growth

International Nuclear Information System (INIS)

Adolphsen, C.; Raubenheimer, T.

1993-04-01

Future linear colliders will likely use sophisticated beam-based alignment and/or steering algorithms to control the growth of the beam emittance in the linac. In this paper, a mathematical framework is presented which simplifies the evaluation of the effectiveness of these algorithms. As an application, a quad alignment that uses beam data taken with the nominal linac optics, and with a scaled optics, is evaluated in terms of the dispersive emittance growth remaining after alignment

A Hierarchical Algorithm for Integrated Scheduling and Control With Applications to Power Systems

DEFF Research Database (Denmark)

Sokoler, Leo Emil; Dinesen, Peter Juhler; Jørgensen, John Bagterp

2016-01-01

The contribution of this paper is a hierarchical algorithm for integrated scheduling and control via model predictive control of hybrid systems. The controlled system is a linear system composed of continuous control, state, and output variables. Binary variables occur as scheduling decisions in ...

Model-independent nonlinear control algorithm with application to a liquid bridge experiment

International Nuclear Information System (INIS)

Petrov, V.; Haaning, A.; Muehlner, K.A.; Van Hook, S.J.; Swinney, H.L.

1998-01-01

We present a control method for high-dimensional nonlinear dynamical systems that can target remote unstable states without a priori knowledge of the underlying dynamical equations. The algorithm constructs a high-dimensional look-up table based on the system's responses to a sequence of random perturbations. The method is demonstrated by stabilizing unstable flow of a liquid bridge surface-tension-driven convection experiment that models the float zone refining process. Control of the dynamics is achieved by heating or cooling two thermoelectric Peltier devices placed in the vicinity of the liquid bridge surface. The algorithm routines along with several example programs written in the MATLAB language can be found at ftp://ftp.mathworks.com/pub/contrib/v5/control/nlcontrol. copyright 1998 The American Physical Society

A new algorithm for optimum voltage and reactive power control for minimizing transmission lines losses

International Nuclear Information System (INIS)

Ghoudjehbaklou, H.; Danai, B.

2001-01-01

Reactive power dispatch for voltage profile modification has been of interest to power utilities. Usually local bus voltages can be altered by changing generator voltages, reactive shunts, ULTC transformers and SVCs. Determination of optimum values for control parameters, however, is not simple for modern power system networks. Heuristic and rather intelligent algorithms have to be sought. In this paper a new algorithm is proposed that is based on a variant of a genetic algorithm combined with simulated annealing updates. In this algorithm a fuzzy multi-objective a approach is used for the fitness function of the genetic algorithm. This fuzzy multi-objective function can efficiently modify the voltage profile in order to minimize transmission lines losses, thus reducing the operating costs. The reason for such a combination is to utilize the best characteristics of each method and overcome their deficiencies. The proposed algorithm is much faster than the classical genetic algorithm and cna be easily integrated into existing power utilities software. The proposed algorithm is tested on an actual system model of 1284 buses, 799 lines, 1175 fixed and ULTC transformers, 86 generators, 181 controllable shunts and 425 loads

Case Study: A Bio-Inspired Control Algorithm for a Robotic Foot-Ankle Prosthesis Provides Adaptive Control of Level Walking and Stair Ascent

Directory of Open Access Journals (Sweden)

Uzma Tahir

2018-04-01

Full Text Available Powered ankle-foot prostheses assist users through plantarflexion during stance and dorsiflexion during swing. Provision of motor power permits faster preferred walking speeds than passive devices, but use of active motor power raises the issue of control. While several commercially available algorithms provide torque control for many intended activities and variations of terrain, control approaches typically exhibit no inherent adaptation. In contrast, muscles adapt instantaneously to changes in load without sensory feedback due to the intrinsic property that their stiffness changes with length and velocity. We previously developed a “winding filament” hypothesis (WFH for muscle contraction that accounts for intrinsic muscle properties by incorporating the giant titin protein. The goals of this study were to develop a WFH-based control algorithm for a powered prosthesis and to test its robustness during level walking and stair ascent in a case study of two subjects with 4–5 years of experience using a powered prosthesis. In the WFH algorithm, ankle moments produced by virtual muscles are calculated based on muscle length and activation. Net ankle moment determines the current applied to the motor. Using this algorithm implemented in a BiOM T2 prosthesis, we tested subjects during level walking and stair ascent. During level walking at variable speeds, the WFH algorithm produced plantarflexion angles (range = −8 to −19° and ankle moments (range = 1 to 1.5 Nm/kg similar to those produced by the BiOM T2 stock controller and to people with no amputation. During stair ascent, the WFH algorithm produced plantarflexion angles (range −15 to −19° that were similar to persons with no amputation and were ~5 times larger on average at 80 steps/min than those produced by the stock controller. This case study provides proof-of-concept that, by emulating muscle properties, the WFH algorithm provides robust, adaptive control of level walking at

Algorithms bio-inspired for the pattern obtention of control bars in BWR reactors; Algoritmos bio-inspirados para la obtencion de patrones de barras de control en reactores BWR

Energy Technology Data Exchange (ETDEWEB)

Ortiz, J.J.; Perusquia, R.; Montes, J.L. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico)

2003-07-01

In this work methods based on Genetic Algorithms and Systems based on ant colonies for the obtention of the patterns of control bars of an equilibrium cycle of 18 months for the Laguna Verde nuclear power station are presented. A comparison of obtained results with the methods and with those of design of such equilibrium cycle is presented. As consequence of the study, it was found that the algorithm based on the ant colonies reached to diminish the coast down period (decrease of power at the end of the cycle) in five and half days with respect to the original design what represents an annual saving of $US 100,000. (Author)

State-Space Equations and the First-Phase Algorithm for Signal Control of Single Intersections

Institute of Scientific and Technical Information of China (English)

LI Jinyuan; PAN Xin; WANG Xiqin

2007-01-01

State-space equations were applied to formulate the queuing and delay of traffic at a single intersection in this paper. The signal control of a single intersection was then modeled as a discrete-time optimal control problem, with consideration of the constraints of stream conflicts, saturation flow rate, minimum green time, and maximum green time. The problem cannot be solved directly due to the nonlinear constraints.However, the results of qualitative analysis were used to develop a first-phase signal control algorithm. Simulation results show that the algorithm substantially reduces the total delay compared to fixed-time control.

Simulation of obstacles’ effect on industrial robots’ working space using genetic algorithm

Directory of Open Access Journals (Sweden)

M.F. Aly

2014-07-01

Full Text Available The study of robot workspace is an interesting problem since its applications are directly related to industry. However, it involves several mathematical complications; Thus, many of the arising questions are left without a definite answer. With the motivation of industrial demand, the need for finding better answers than the existing ones lasts. The workspace (WS determination of a robot with general structural parameters is a complex problem, which cannot be solved in an explicit way. Closed form solutions are only available in some particular cases. Otherwise, computational algorithms and numerical techniques are used. The task becomes even much more complicated by the presence of obstacles in the robot accessible region. Obstacle presence does not only exclude points from the original WS but it affects the whole robot workspace’s shape and size to the extent that it sometimes divides the working space in two or more separate regions that cannot be linked by the same robot. Much research work in the literature is directed toward path planning in the presence of obstacles without having to determine the robot WS. However, a real situation in industry occurs when the knowledge of the WS is of importance in facility layout. This paper presents an approach for the estimation of a generic open-chain robot in the presence of obstacles with any desired number of prismatic and/or revolute joints of any order. Joints’ axes may have any orientation relative to each other. The robot can be placed in free space or in a work cell consisting of a set of Computer Numerically Controlled (CNC machines and some obstacles.

Algebraic dynamics algorithm: Numerical comparison with Runge-Kutta algorithm and symplectic geometric algorithm

Institute of Scientific and Technical Information of China (English)

WANG ShunJin; ZHANG Hua

2007-01-01

Based on the exact analytical solution of ordinary differential equations,a truncation of the Taylor series of the exact solution to the Nth order leads to the Nth order algebraic dynamics algorithm.A detailed numerical comparison is presented with Runge-Kutta algorithm and symplectic geometric algorithm for 12 test models.The results show that the algebraic dynamics algorithm can better preserve both geometrical and dynamical fidelity of a dynamical system at a controllable precision,and it can solve the problem of algorithm-induced dissipation for the Runge-Kutta algorithm and the problem of algorithm-induced phase shift for the symplectic geometric algorithm.

Algebraic dynamics algorithm:Numerical comparison with Runge-Kutta algorithm and symplectic geometric algorithm

Institute of Scientific and Technical Information of China (English)

2007-01-01

Based on the exact analytical solution of ordinary differential equations, a truncation of the Taylor series of the exact solution to the Nth order leads to the Nth order algebraic dynamics algorithm. A detailed numerical comparison is presented with Runge-Kutta algorithm and symplectic geometric algorithm for 12 test models. The results show that the algebraic dynamics algorithm can better preserve both geometrical and dynamical fidelity of a dynamical system at a controllable precision, and it can solve the problem of algorithm-induced dissipation for the Runge-Kutta algorithm and the problem of algorithm-induced phase shift for the symplectic geometric algorithm.

Active control of flexible structures using a fuzzy logic algorithm

Science.gov (United States)

Cohen, Kelly; Weller, Tanchum; Ben-Asher, Joseph Z.

2002-08-01

This study deals with the development and application of an active control law for the vibration suppression of beam-like flexible structures experiencing transient disturbances. Collocated pairs of sensors/actuators provide active control of the structure. A design methodology for the closed-loop control algorithm based on fuzzy logic is proposed. First, the behavior of the open-loop system is observed. Then, the number and locations of collocated actuator/sensor pairs are selected. The proposed control law, which is based on the principles of passivity, commands the actuator to emulate the behavior of a dynamic vibration absorber. The absorber is tuned to a targeted frequency, whereas the damping coefficient of the dashpot is varied in a closed loop using a fuzzy logic based algorithm. This approach not only ensures inherent stability associated with passive absorbers, but also circumvents the phenomenon of modal spillover. The developed controller is applied to the AFWAL/FIB 10 bar truss. Simulated results using MATLAB© show that the closed-loop system exhibits fairly quick settling times and desirable performance, as well as robustness characteristics. To demonstrate the robustness of the control system to changes in the temporal dynamics of the flexible structure, the transient response to a considerably perturbed plant is simulated. The modal frequencies of the 10 bar truss were raised as well as lowered substantially, thereby significantly perturbing the natural frequencies of vibration. For these cases, too, the developed control law provides adequate settling times and rates of vibrational energy dissipation.

Implementation of the resonant vibratory feeders control algorithm on Simatic S7-1200 from MATLAB Simulink enviroment

Directory of Open Access Journals (Sweden)

Mitrović Radomir B.

2016-01-01

Full Text Available Simulink is an important tool for modeling and simulation of process and control algorithms. It's expansion, PLC Coder, enables direct conversion of model subsystem into SCL, structured text code, which is then used by PLC IDE to create function blocks. This shortens developing time of algorithms for PLC controller. Also, this reduces possibility for a coding error. This paper describes Simulink PLC Coder and workflow for developing PID control algorithm for Siemens Simatic S7-1200 PLC. Control object used here is resonant vibratory feeder having electromagnetic drive.

A Model Predictive Algorithm for Active Control of Nonlinear Noise Processes

Directory of Open Access Journals (Sweden)

Qi-Zhi Zhang

2005-01-01

Full Text Available In this paper, an improved nonlinear Active Noise Control (ANC system is achieved by introducing an appropriate secondary source. For ANC system to be successfully implemented, the nonlinearity of the primary path and time delay of the secondary path must be overcome. A nonlinear Model Predictive Control (MPC strategy is introduced to deal with the time delay in the secondary path and the nonlinearity in the primary path of the ANC system. An overall online modeling technique is utilized for online secondary path and primary path estimation. The secondary path is estimated using an adaptive FIR filter, and the primary path is estimated using a Neural Network (NN. The two models are connected in parallel with the two paths. In this system, the mutual disturbances between the operation of the nonlinear ANC controller and modeling of the secondary can be greatly reduced. The coefficients of the adaptive FIR filter and weight vector of NN are adjusted online. Computer simulations are carried out to compare the proposed nonlinear MPC method with the nonlinear Filter-x Least Mean Square (FXLMS algorithm. The results showed that the convergence speed of the proposed nonlinear MPC algorithm is faster than that of nonlinear FXLMS algorithm. For testing the robust performance of the proposed nonlinear ANC system, the sudden changes in the secondary path and primary path of the ANC system are considered. Results indicated that the proposed nonlinear ANC system can rapidly track the sudden changes in the acoustic paths of the nonlinear ANC system, and ensure the adaptive algorithm stable when the nonlinear ANC system is time variable.

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.

Science.gov (United States)

Mo, Yuanfu; Yu, Dexin; Song, Jun; Zheng, Kun; Guo, Yajuan

2015-01-01

In a vehicular ad hoc network (VANET), the periodic exchange of single-hop status information broadcasts (beacon frames) produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.

A Beacon Transmission Power Control Algorithm Based on Wireless Channel Load Forecasting in VANETs.

Directory of Open Access Journals (Sweden)

Yuanfu Mo

Full Text Available In a vehicular ad hoc network (VANET, the periodic exchange of single-hop status information broadcasts (beacon frames produces channel loading, which causes channel congestion and induces information conflict problems. To guarantee fairness in beacon transmissions from each node and maximum network connectivity, adjustment of the beacon transmission power is an effective method for reducing and preventing channel congestion. In this study, the primary factors that influence wireless channel loading are selected to construct the KF-BCLF, which is a channel load forecasting algorithm based on a recursive Kalman filter and employs multiple regression equation. By pre-adjusting the transmission power based on the forecasted channel load, the channel load was kept within a predefined range; therefore, channel congestion was prevented. Based on this method, the CLF-BTPC, which is a transmission power control algorithm, is proposed. To verify KF-BCLF algorithm, a traffic survey method that involved the collection of floating car data along a major traffic road in Changchun City is employed. By comparing this forecast with the measured channel loads, the proposed KF-BCLF algorithm was proven to be effective. In addition, the CLF-BTPC algorithm is verified by simulating a section of eight-lane highway and a signal-controlled urban intersection. The results of the two verification process indicate that this distributed CLF-BTPC algorithm can effectively control channel load, prevent channel congestion, and enhance the stability and robustness of wireless beacon transmission in a vehicular network.

Design of permanent magnet synchronous motor speed loop controller based on sliding mode control algorithm

Science.gov (United States)

Qiang, Jiang; Meng-wei, Liao; Ming-jie, Luo

2018-03-01

Abstract.The control performance of Permanent Magnet Synchronous Motor will be affected by the fluctuation or changes of mechanical parameters when PMSM is applied as driving motor in actual electric vehicle,and external disturbance would influence control robustness.To improve control dynamic quality and robustness of PMSM speed control system, a new second order integral sliding mode control algorithm is introduced into PMSM vector control.The simulation results show that, compared with the traditional PID control,the modified control scheme optimized has better control precision and dynamic response ability and perform better with a stronger robustness facing external disturbance,it can effectively solve the traditional sliding mode variable structure control chattering problems as well.

Denni Algorithm An Enhanced Of SMS (Scan, Move and Sort) Algorithm

Science.gov (United States)

Aprilsyah Lubis, Denni; Salim Sitompul, Opim; Marwan; Tulus; Andri Budiman, M.

2017-12-01

Sorting has been a profound area for the algorithmic researchers, and many resources are invested to suggest a more working sorting algorithm. For this purpose many existing sorting algorithms were observed in terms of the efficiency of the algorithmic complexity. Efficient sorting is important to optimize the use of other algorithms that require sorted lists to work correctly. Sorting has been considered as a fundamental problem in the study of algorithms that due to many reasons namely, the necessary to sort information is inherent in many applications, algorithms often use sorting as a key subroutine, in algorithm design there are many essential techniques represented in the body of sorting algorithms, and many engineering issues come to the fore when implementing sorting algorithms., Many algorithms are very well known for sorting the unordered lists, and one of the well-known algorithms that make the process of sorting to be more economical and efficient is SMS (Scan, Move and Sort) algorithm, an enhancement of Quicksort invented Rami Mansi in 2010. This paper presents a new sorting algorithm called Denni-algorithm. The Denni algorithm is considered as an enhancement on the SMS algorithm in average, and worst cases. The Denni algorithm is compared with the SMS algorithm and the results were promising.

Design of a fractional order PID controller using GBMO algorithm for load-frequency control with governor saturation consideration.

Science.gov (United States)

Zamani, Abbasali; Barakati, S Masoud; Yousofi-Darmian, Saeed

2016-09-01

Load-frequency control is one of the most important issues in power system operation. In this paper, a Fractional Order PID (FOPID) controller based on Gases Brownian Motion Optimization (GBMO) is used in order to mitigate frequency and exchanged power deviation in two-area power system with considering governor saturation limit. In a FOPID controller derivative and integrator parts have non-integer orders which should be determined by designer. FOPID controller has more flexibility than PID controller. The GBMO algorithm is a recently introduced search method that has suitable accuracy and convergence rate. Thus, this paper uses the advantages of FOPID controller as well as GBMO algorithm to solve load-frequency control. However, computational load will higher than conventional controllers due to more complexity of design procedure. Also, a GBMO based fuzzy controller is designed and analyzed in detail. The performance of the proposed controller in time domain and its robustness are verified according to comparison with other controllers like GBMO based fuzzy controller and PI controller that used for load-frequency control system in confronting with model parameters variations. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Work Time Control and Sleep Disturbances

DEFF Research Database (Denmark)

Salo, Paula; Ala-Mursula, Leena; Rod, Naja Hulvej

2014-01-01

OBJECTIVES: Employee control over work times has been associated with favorable psychosocial and health-related outcomes, but the evidence regarding sleep quality remains inconclusive. We examined cross-sectional and prospective associations between work time control and sleep disturbances...... in a large working population, taking into account total hours worked. METHODS: The data were from a full-panel longitudinal cohort study of Finnish public sector employees who responded to questions on work time control and sleep disturbances in years 2000-2001, 2004-2005, 2008-2009, and 2012. The analysis....... RESULTS: Consistently in both cross-sectional and longitudinal models, less control over work time was associated with greater sleep disturbances in the total population and among those working normal 40-hour weeks. Among participants working more than 40 hours a week, work time that was both very high...

Research on magnetorheological damper suspension with permanent magnet and magnetic valve based on developed FOA-optimal control algorithm

Energy Technology Data Exchange (ETDEWEB)

Xiao, Ping; Gao, Hong [Anhui Polytechnic University, Wuhu (China); Niu, Limin [Anhui University of Technology, Maanshan (China)

2017-07-15

Due to the fail safe problem, it was difficult for the existing Magnetorheological damper (MD) to be widely applied in automotive suspensions. Therefore, permanent magnets and magnetic valves were introduced to existing MDs so that fail safe problem could be solved by the magnets and damping force could be adjusted easily by the magnetic valve. Thus, a new Magnetorheological damper with permanent magnet and magnetic valve (MDPMMV) was developed and MDPMMV suspension was studied. First of all, mechanical structure of existing magnetorheological damper applied in automobile suspensions was redesigned, comprising a permanent magnet and a magnetic valve. In addition, prediction model of damping force was built based on electromagnetics theory and Bingham model. Experimental research was onducted on the newly designed damper and goodness of fit between experiment results and simulated ones by models was high. On this basis, a quarter suspension model was built. Then, fruit Fly optimization algorithm (FOA)-optimal control algorithm suitable for automobile suspension was designed based on developing normal FOA. Finally, simulation experiments and bench tests with input surface of pulse road and B road were carried out and the results indicated that working erformance of MDPMMV suspension based on FOA-optimal control algorithm was good.

Optimum Actuator Selection with a Genetic Algorithm for Aircraft Control

Science.gov (United States)

Rogers, James L.

2004-01-01

The placement of actuators on a wing determines the control effectiveness of the airplane. One approach to placement maximizes the moments about the pitch, roll, and yaw axes, while minimizing the coupling. For example, the desired actuators produce a pure roll moment without at the same time causing much pitch or yaw. For a typical wing, there is a large set of candidate locations for placing actuators, resulting in a substantially larger number of combinations to examine in order to find an optimum placement satisfying the mission requirements and mission constraints. A genetic algorithm has been developed for finding the best placement for four actuators to produce an uncoupled pitch moment. The genetic algorithm has been extended to find the minimum number of actuators required to provide uncoupled pitch, roll, and yaw control. A simplified, untapered, unswept wing is the model for each application.

Using the Pebb Universal Controller to Modify Control Algorithms for DC-To-DC Converters and Implement Closed-Loop Control of ARCP Inverters

National Research Council Canada - National Science Library

Floodeen, David

1998-01-01

The objective of this thesis is two-fold. The first goal is to expand the operational capabilities of the Ship's Service Converter Module control algorithm for a DC-to-DC converter using the Universal Controller...

Software-Defined Congestion Control Algorithm for IP Networks

Directory of Open Access Journals (Sweden)

Yao Hu

2017-01-01

Full Text Available The rapid evolution of computer networks, increase in the number of Internet users, and popularity of multimedia applications have exacerbated the congestion control problem. Congestion control is a key factor in ensuring network stability and robustness. When the underlying network and flow information are unknown, the transmission control protocol (TCP must increase or reduce the size of the congestion window to adjust to the changes of traffic in the Internet Protocol (IP network. However, it is possible that a software-defined approach can relieve the network congestion problem more efficiently. This approach has the characteristic of centralized control and can obtain a global topology for unified network management. In this paper, we propose a software-defined congestion control (SDCC algorithm for an IP network. We consider the difference between TCP and the user datagram protocol (UDP and propose a new method to judge node congestion. We initially apply the congestion control mechanism in the congested nodes and then optimize the link utilization to control network congestion.

An Overview of the Total Lightning Jump Algorithm: Past, Present and Future Work

Science.gov (United States)

Schultz, Christopher J.; Petersen, Walter A.; Carey, Lawrence D.; Deierling, Wiebke; Kessinger, Cathy

2011-01-01

Rapid increases in total lightning prior to the onset of severe and hazardous weather have been observed for several decades. These rapid increases are known as lightning jumps and can precede the occurrence of severe weather by tens of minutes. Over the past decade, a significant effort has been made to quantify lightning jump behavior in relation to its utility as a predictor of severe and hazardous weather. Based on a study of 34 thunderstorms that occurred in the Tennessee Valley, early work conducted in our group at Huntsville determined that it was indeed possible to create a reasonable operational lightning jump algorithm (LJA) based on a statistical framework relying on the variance behavior of the lightning trending signal. We the expanded this framework and tested several variance-related LJA configurations on a much larger sample of 87 severe and non severe thunderstorms. This study determined that a configuration named the "2(sigma)" algorithm had the most promise in development of the operational LJA with a probability of detection (POD) of 87%, a false alarm rate (FAR) of 33%, a Heidke Skill Score (HSS) of 0.75. The 2(sigma) algorithm was then tested on an even larger sample of 711 thunderstorms of all types from four regions of the country where total lightning measurement capability existed. The result was very encouraging.Despite the larger number of storms and the inclusion of different regions of the country, the POD remained high (79%), the FAR was low (36%) and HSS was solid (0.71). Average lead time from jump to severe weather occurrence was 20.65 minutes, with a standard deviation of +/- 15 minutes. Also, trends in total lightning were compared to cloud to ground (CG) lightning trends, and it was determined that total lightning trends had a higher POD (79% vs 66%), lower FAR (36% vs 54 %) and a better HSS (0.71 vs 0.55). From the 711-storm case study it was determined that a majority of missed events were due to severe weather producing

Multi-objective PID Optimization for Speed Control of an Isolated Steam Turbine using Gentic Algorithm

OpenAIRE

Sanjay Kr. Singh; D. Boolchandani; S.G. Modani; Nitish Katal

2014-01-01

This study focuses on multi-objective optimization of the PID controllers for optimal speed control for an isolated steam turbine. In complex operations, optimal tuning plays an imperative role in maintaining the product quality and process safety. This study focuses on the comparison of the optimal PID tuning using Multi-objective Genetic Algorithm (NSGA-II) against normal genetic algorithm and Ziegler Nichols methods for the speed control of an isolated steam turbine. Isolated steam turbine...

DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Francisco José Estévez

2016-06-01

Full Text Available The evolution of Smart City projects is pushing researchers and companies to develop more efficient embedded hardware and also more efficient communication technologies. These communication technologies are the focus of this work, presenting a new routing algorithm based on dynamically-allocated sub-networks and node roles. Among these features, our algorithm presents a fast set-up time, a reduced overhead and a hierarchical organization, which allows for the application of complex management techniques. This work presents a routing algorithm based on a dynamically-allocated hierarchical clustering, which uses the link quality indicator as a reference parameter, maximizing the network coverage and minimizing the control message overhead and the convergence time. The present work based its test scenario and analysis in the density measure, considered as a node degree. The routing algorithm is compared with some of the most well known routing algorithms for different scenario densities.

Control Algorithms Along Relative Equilibria of Underactuated Lagrangian Systems on Lie Groups

DEFF Research Database (Denmark)

Nordkvist, Nikolaj; Bullo, F.

2008-01-01

We present novel algorithms to control underactuated mechanical systems. For a class of invariant systems on Lie groups, we design iterative small-amplitude control forces to accelerate along, decelerate along, and stabilize relative equilibria. The technical approach is based upon a perturbation...

Control algorithms along relative equilibria of underactuated Lagrangian systems on Lie groups

DEFF Research Database (Denmark)

Nordkvist, Nikolaj; Bullo, Francesco

2007-01-01

We present novel algorithms to control underactuated mechanical systems. For a class of invariant systems on Lie groups, we design iterative small-amplitude control forces to accelerate along, decelerate along, and stabilize relative equilibria. The technical approach is based upon a perturbation...

Development and validation of a prediction algorithm for the onset of common mental disorders in a working population.

Science.gov (United States)

Fernandez, Ana; Salvador-Carulla, Luis; Choi, Isabella; Calvo, Rafael; Harvey, Samuel B; Glozier, Nicholas

2018-01-01

Common mental disorders are the most common reason for long-term sickness absence in most developed countries. Prediction algorithms for the onset of common mental disorders may help target indicated work-based prevention interventions. We aimed to develop and validate a risk algorithm to predict the onset of common mental disorders at 12 months in a working population. We conducted a secondary analysis of the Household, Income and Labour Dynamics in Australia Survey, a longitudinal, nationally representative household panel in Australia. Data from the 6189 working participants who did not meet the criteria for a common mental disorders at baseline were non-randomly split into training and validation databases, based on state of residence. Common mental disorders were assessed with the mental component score of 36-Item Short Form Health Survey questionnaire (score ⩽45). Risk algorithms were constructed following recommendations made by the Transparent Reporting of a multivariable prediction model for Prevention Or Diagnosis statement. Different risk factors were identified among women and men for the final risk algorithms. In the training data, the model for women had a C-index of 0.73 and effect size (Hedges' g) of 0.91. In men, the C-index was 0.76 and the effect size was 1.06. In the validation data, the C-index was 0.66 for women and 0.73 for men, with positive predictive values of 0.28 and 0.26, respectively Conclusion: It is possible to develop an algorithm with good discrimination for the onset identifying overall and modifiable risks of common mental disorders among working men. Such models have the potential to change the way that prevention of common mental disorders at the workplace is conducted, but different models may be required for women.

Flocking algorithm for autonomous flying robots.

Science.gov (United States)

Virágh, Csaba; Vásárhelyi, Gábor; Tarcai, Norbert; Szörényi, Tamás; Somorjai, Gergő; Nepusz, Tamás; Vicsek, Tamás

2014-06-01

Animal swarms displaying a variety of typical flocking patterns would not exist without the underlying safe, optimal and stable dynamics of the individuals. The emergence of these universal patterns can be efficiently reconstructed with agent-based models. If we want to reproduce these patterns with artificial systems, such as autonomous aerial robots, agent-based models can also be used in their control algorithms. However, finding the proper algorithms and thus understanding the essential characteristics of the emergent collective behaviour requires thorough and realistic modeling of the robot and also the environment. In this paper, we first present an abstract mathematical model of an autonomous flying robot. The model takes into account several realistic features, such as time delay and locality of communication, inaccuracy of the on-board sensors and inertial effects. We present two decentralized control algorithms. One is based on a simple self-propelled flocking model of animal collective motion, the other is a collective target tracking algorithm. Both algorithms contain a viscous friction-like term, which aligns the velocities of neighbouring agents parallel to each other. We show that this term can be essential for reducing the inherent instabilities of such a noisy and delayed realistic system. We discuss simulation results on the stability of the control algorithms, and perform real experiments to show the applicability of the algorithms on a group of autonomous quadcopters. In our case, bio-inspiration works in two ways. On the one hand, the whole idea of trying to build and control a swarm of robots comes from the observation that birds tend to flock to optimize their behaviour as a group. On the other hand, by using a realistic simulation framework and studying the group behaviour of autonomous robots we can learn about the major factors influencing the flight of bird flocks.

Trajectory Tracking of a Tri-Rotor Aerial Vehicle Using an MRAC-Based Robust Hybrid Control Algorithm

Directory of Open Access Journals (Sweden)

Zain Anwar Ali

2017-01-01

Full Text Available In this paper, a novel Model Reference Adaptive Control (MRAC-based hybrid control algorithm is presented for the trajectory tracking of a tri-rotor Unmanned Aerial Vehicle (UAV. The mathematical model of the tri-rotor is based on the Newton–Euler formula, whereas the MRAC-based hybrid controller consists of Fuzzy Proportional Integral Derivative (F-PID and Fuzzy Proportional Derivative (F-PD controllers. MRAC is used as the main controller for the dynamics, while the parameters of the adaptive controller are fine-tuned by the F-PD controller for the altitude control subsystem and the F-PID controller for the attitude control subsystem of the UAV. The stability of the system is ensured and proven by Lyapunov stability analysis. The proposed control algorithm is tested and verified using computer simulations for the trajectory tracking of the desired path as an input. The effectiveness of our proposed algorithm is compared with F-PID and the Fuzzy Logic Controller (FLC. Our proposed controller exhibits much less steady state error, quick error convergence in the presence of disturbance or noise, and model uncertainties.

Coordinated Control of PV Generation and EVs Charging Based on Improved DECell Algorithm

Directory of Open Access Journals (Sweden)

Guo Zhao

2015-01-01

Full Text Available Recently, the coordination of EVs’ charging and renewable energy has become a hot research all around the globe. Considering the requirements of EV owner and the influence of the PV output fluctuation on the power grid, a three-objective optimization model was established by controlling the EVs charging power during charging process. By integrating the meshing method into differential evolution cellular (DECell genetic algorithm, an improved differential evolution cellular (IDECell genetic algorithm was presented to solve the multiobjective optimization model. Compared to the NSGA-II and DECell, the IDECell algorithm showed better performance in the convergence and uniform distribution. Furthermore, the IDECell algorithm was applied to obtain the Pareto front of nondominated solutions. Followed by the normalized sorting of the nondominated solutions, the optimal solution was chosen to arrive at the optimized coordinated control strategy of PV generation and EVs charging. Compared to typical charging pattern, the optimized charging pattern could reduce the fluctuations of PV generation output power, satisfy the demand of EVs charging quantity, and save the total charging cost.

Self-tuning control algorithm design for vehicle adaptive cruise control system through real-time estimation of vehicle parameters and road grade

Science.gov (United States)

Marzbanrad, Javad; Tahbaz-zadeh Moghaddam, Iman

2016-09-01

The main purpose of this paper is to design a self-tuning control algorithm for an adaptive cruise control (ACC) system that can adapt its behaviour to variations of vehicle dynamics and uncertain road grade. To this aim, short-time linear quadratic form (STLQF) estimation technique is developed so as to track simultaneously the trend of the time-varying parameters of vehicle longitudinal dynamics with a small delay. These parameters are vehicle mass, road grade and aerodynamic drag-area coefficient. Next, the values of estimated parameters are used to tune the throttle and brake control inputs and to regulate the throttle/brake switching logic that governs the throttle and brake switching. The performance of the designed STLQF-based self-tuning control (STLQF-STC) algorithm for ACC system is compared with the conventional method based on fixed control structure regarding the speed/distance tracking control modes. Simulation results show that the proposed control algorithm improves the performance of throttle and brake controllers, providing more comfort while travelling, enhancing driving safety and giving a satisfactory performance in the presence of different payloads and road grade variations.

Multimodal optimization by using hybrid of artificial bee colony algorithm and BFGS algorithm

Science.gov (United States)

Anam, S.

2017-10-01

Optimization has become one of the important fields in Mathematics. Many problems in engineering and science can be formulated into optimization problems. They maybe have many local optima. The optimization problem with many local optima, known as multimodal optimization problem, is how to find the global solution. Several metaheuristic methods have been proposed to solve multimodal optimization problems such as Particle Swarm Optimization (PSO), Genetics Algorithm (GA), Artificial Bee Colony (ABC) algorithm, etc. The performance of the ABC algorithm is better than or similar to those of other population-based algorithms with the advantage of employing a fewer control parameters. The ABC algorithm also has the advantages of strong robustness, fast convergence and high flexibility. However, it has the disadvantages premature convergence in the later search period. The accuracy of the optimal value cannot meet the requirements sometimes. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a good iterative method for finding a local optimum. Compared with other local optimization methods, the BFGS algorithm is better. Based on the advantages of the ABC algorithm and the BFGS algorithm, this paper proposes a hybrid of the artificial bee colony algorithm and the BFGS algorithm to solve the multimodal optimization problem. The first step is that the ABC algorithm is run to find a point. In the second step is that the point obtained by the first step is used as an initial point of BFGS algorithm. The results show that the hybrid method can overcome from the basic ABC algorithm problems for almost all test function. However, if the shape of function is flat, the proposed method cannot work well.

Procedural Generation of Levels with Controllable Difficulty for a Platform Game Using a Genetic Algorithm

OpenAIRE

Classon, Johan; Andersson, Viktor

2016-01-01

This thesis describes the implementation and evaluation of a genetic algorithm (GA) for procedurally generating levels with controllable difficulty for a motion-based 2D platform game. Manually creating content can be time-consuming, and it may be desirable to automate this process with an algorithm, using Procedural Content Generation (PCG). An algorithm was implemented and then refined with an iterative method by conducting user tests. The resulting algorithm is considered a success and sho...

Design of optimised backstepping controller for the synchronisation of chaotic Colpitts oscillator using shark smell algorithm

Science.gov (United States)

Fouladi, Ehsan; Mojallali, Hamed

2018-01-01

In this paper, an adaptive backstepping controller has been tuned to synchronise two chaotic Colpitts oscillators in a master-slave configuration. The parameters of the controller are determined using shark smell optimisation (SSO) algorithm. Numerical results are presented and compared with those of particle swarm optimisation (PSO) algorithm. Simulation results show better performance in terms of accuracy and convergence for the proposed optimised method compared to PSO optimised controller or any non-optimised backstepping controller.

Optimization of a predictive controller of a pressurized water reactor Xenon oscillation using the particle swarm optimization algorithm

International Nuclear Information System (INIS)

Medeiros, Jose Antonio Carlos Canedo; Machado, Marcelo Dornellas; Lima, Alan Miranda M. de; Schirru, Roberto

2007-01-01

Predictive control systems are control systems that use a model of the controlled system (plant), used to predict the future behavior of the plant allowing the establishment of an anticipative control based on a future condition of the plant, and an optimizer that, considering a future time horizon of the plant output and a recent horizon of the control action, determines the controller's outputs to optimize a performance index of the controlled plant. The predictive control system does not require analytical models of the plant; the model of predictor of the plant can be learned from historical data of operation of the plant. The optimizer of the predictive controller establishes the strategy of the control: the minimization of a performance index (objective function) is done so that the present and future control actions are computed in such a way to minimize the objective function. The control strategy, implemented by the optimizer, induces the formation of an optimal control mechanism whose effect is to reduce the stabilization time, the 'overshoot' and 'undershoot', minimize the control actuation so that a compromise among those objectives is attained. The optimizer of the predictive controller is usually implemented using gradient-based algorithms. In this work we use the Particle Swarm Optimization algorithm (PSO) in the optimizer component of a predictive controller applied in the control of the xenon oscillation of a pressurized water reactor (PWR). The PSO is a stochastic optimization technique applied in several disciplines, simple and capable of providing a global optimal for high complexity problems and difficult to be optimized, providing in many cases better results than those obtained by other conventional and/or other artificial optimization techniques. (author)

A Modified LQG Algorithm (MLQG for Robust Control of Nonlinear Multivariable Systems

Directory of Open Access Journals (Sweden)

Jens G. Balchen

1993-07-01

Full Text Available The original LQG algorithm is often characterized for its lack of robustness. This is because in the design of the estimator (Kalman filter the process disturbance is assumed to be white noise. If the estimator is to give good estimates, the Kalman gain is increased which means that the estimator fails to become robust. A solution to this problem is to replace the proportional Kalman gain matrix by a dynamic PI algorithm and the proportional LQ feedback gain matrix by a PI algorithm. A tuning method is developed which facilitates the tuning of a modified LQG control system (MLQG by only two tuning parameters.

Enhancement of tracking performance in electro-optical system based on servo control algorithm

Science.gov (United States)

Choi, WooJin; Kim, SungSu; Jung, DaeYoon; Seo, HyoungKyu

2017-10-01

Modern electro-optical surveillance and reconnaissance systems require tracking capability to get exact images of target or to accurately direct the line of sight to target which is moving or still. This leads to the tracking system composed of image based tracking algorithm and servo control algorithm. In this study, we focus on the servo control function to minimize the overshoot in the tracking motion and do not miss the target. The scheme is to limit acceleration and velocity parameters in the tracking controller, depending on the target state information in the image. We implement the proposed techniques by creating a system model of DIRCM and simulate the same environment, validate the performance on the actual equipment.

Tuning and performance evaluation of PID controller for superheater steam temperature control of 200 MW boiler using gain phase assignment algorithm

Science.gov (United States)

Begum, A. Yasmine; Gireesh, N.

2018-04-01

In superheater, steam temperature is controlled in a cascade control loop. The cascade control loop consists of PI and PID controllers. To improve the superheater steam temperature control the controller's gains in a cascade control loop has to be tuned efficiently. The mathematical model of the superheater is derived by sets of nonlinear partial differential equations. The tuning methods taken for study here are designed for delay plus first order transfer function model. Hence from the dynamical model of the superheater, a FOPTD model is derived using frequency response method. Then by using Chien-Hrones-Reswick Tuning Algorithm and Gain-Phase Assignment Algorithm optimum controller gains has been found out based on the least value of integral time weighted absolute error.

Simulation Modeling of Intelligent Control Algorithms for Constructing Autonomous Power Supply Systems with Improved Energy Efficiency

Directory of Open Access Journals (Sweden)

Gimazov Ruslan

2018-01-01

Full Text Available The paper considers the issue of supplying autonomous robots by solar batteries. Low efficiency of modern solar batteries is a critical issue for the whole industry of renewable energy. The urgency of solving the problem of improved energy efficiency of solar batteries for supplying the robotic system is linked with the task of maximizing autonomous operation time. Several methods to improve the energy efficiency of solar batteries exist. The use of MPPT charge controller is one these methods. MPPT technology allows increasing the power generated by the solar battery by 15 – 30%. The most common MPPT algorithm is the perturbation and observation algorithm. This algorithm has several disadvantages, such as power fluctuation and the fixed time of the maximum power point tracking. These problems can be solved by using a sufficiently accurate predictive and adaptive algorithm. In order to improve the efficiency of solar batteries, autonomous power supply system was developed, which included an intelligent MPPT charge controller with the fuzzy logic-based perturbation and observation algorithm. To study the implementation of the fuzzy logic apparatus in the MPPT algorithm, in Matlab/Simulink environment, we developed a simulation model of the system, including solar battery, MPPT controller, accumulator and load. Results of the simulation modeling established that the use of MPPT technology had increased energy production by 23%; introduction of the fuzzy logic algorithm to MPPT controller had greatly increased the speed of the maximum power point tracking and neutralized the voltage fluctuations, which in turn reduced the power underproduction by 2%.

Fuzzy-Based Hybrid Control Algorithm for the Stabilization of a Tri-Rotor UAV.

Science.gov (United States)

Ali, Zain Anwar; Wang, Daobo; Aamir, Muhammad

2016-05-09

In this paper, a new and novel mathematical fuzzy hybrid scheme is proposed for the stabilization of a tri-rotor unmanned aerial vehicle (UAV). The fuzzy hybrid scheme consists of a fuzzy logic controller, regulation pole-placement tracking (RST) controller with model reference adaptive control (MRAC), in which adaptive gains of the RST controller are being fine-tuned by a fuzzy logic controller. Brushless direct current (BLDC) motors are installed in the triangular frame of the tri-rotor UAV, which helps maintain control on its motion and different altitude and attitude changes, similar to rotorcrafts. MRAC-based MIT rule is proposed for system stability. Moreover, the proposed hybrid controller with nonlinear flight dynamics is shown in the presence of translational and rotational velocity components. The performance of the proposed algorithm is demonstrated via MATLAB simulations, in which the proposed fuzzy hybrid controller is compared with the existing adaptive RST controller. It shows that our proposed algorithm has better transient performance with zero steady-state error, and fast convergence towards stability.

Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies

Directory of Open Access Journals (Sweden)

Kathryn L. Jackson

2016-11-01

Full Text Available Abstract Background Community associated methicillin-resistant Staphylococcus aureus (CA-MRSA is one of the most common causes of skin and soft tissue infections in the United States, and a variety of genetic host factors are suspected to be risk factors for recurrent infection. Based on the CDC definition, we have developed and validated an electronic health record (EHR based CA-MRSA phenotype algorithm utilizing both structured and unstructured data. Methods The algorithm was validated at three eMERGE consortium sites, and positive predictive value, negative predictive value and sensitivity, were calculated. The algorithm was then run and data collected across seven total sites. The resulting data was used in GWAS analysis. Results Across seven sites, the CA-MRSA phenotype algorithm identified a total of 349 cases and 7761 controls among the genotyped European and African American biobank populations. PPV ranged from 68 to 100% for cases and 96 to 100% for controls; sensitivity ranged from 94 to 100% for cases and 75 to 100% for controls. Frequency of cases in the populations varied widely by site. There were no plausible GWAS-significant (p < 5 E −8 findings. Conclusions Differences in EHR data representation and screening patterns across sites may have affected identification of cases and controls and accounted for varying frequencies across sites. Future work identifying these patterns is necessary.

3 x 3 free-space optical router based on crossbar network and its control algorithm

Science.gov (United States)

Hou, Peipei; Sun, Jianfeng; Yu, Zhou; Lu, Wei; Wang, Lijuan; Liu, Liren

2015-08-01

A 3 × 3 free-space optical router, which comprises optical switches and polarizing beam splitter (PBS) and based on crossbar network, is proposed in this paper. A control algorithm for the 3 × 3 free-space optical router is also developed to achieve rapid control without rearrangement. In order to test the performance of the network based on 3 × 3 free-space optical router and that of the algorithm developed for the optical router, experiments are designed. The experiment results show that the interconnection network based on the 3 × 3 free-space optical router has low cross talk, fast connection speed. Under the control of the algorithm developed, a non-block and real free interconnection network is obtained based on the 3 × 3 free-space optical router we proposed.

Multiobjective Genetic Algorithm applied to dengue control.

Science.gov (United States)

Florentino, Helenice O; Cantane, Daniela R; Santos, Fernando L P; Bannwart, Bettina F

2014-12-01

Dengue fever is an infectious disease caused by a virus of the Flaviridae family and transmitted to the person by a mosquito of the genus Aedes aegypti. This disease has been a global public health problem because a single mosquito can infect up to 300 people and between 50 and 100 million people are infected annually on all continents. Thus, dengue fever is currently a subject of research, whether in the search for vaccines and treatments for the disease or efficient and economical forms of mosquito control. The current study aims to study techniques of multiobjective optimization to assist in solving problems involving the control of the mosquito that transmits dengue fever. The population dynamics of the mosquito is studied in order to understand the epidemic phenomenon and suggest strategies of multiobjective programming for mosquito control. A Multiobjective Genetic Algorithm (MGA_DENGUE) is proposed to solve the optimization model treated here and we discuss the computational results obtained from the application of this technique. Copyright © 2014 Elsevier Inc. All rights reserved.

Advanced Emergency Braking Control Based on a Nonlinear Model Predictive Algorithm for Intelligent Vehicles

Directory of Open Access Journals (Sweden)

Ronghui Zhang

2017-05-01

Full Text Available Focusing on safety, comfort and with an overall aim of the comprehensive improvement of a vision-based intelligent vehicle, a novel Advanced Emergency Braking System (AEBS is proposed based on Nonlinear Model Predictive Algorithm. Considering the nonlinearities of vehicle dynamics, a vision-based longitudinal vehicle dynamics model is established. On account of the nonlinear coupling characteristics of the driver, surroundings, and vehicle itself, a hierarchical control structure is proposed to decouple and coordinate the system. To avoid or reduce the collision risk between the intelligent vehicle and collision objects, a coordinated cost function of tracking safety, comfort, and fuel economy is formulated. Based on the terminal constraints of stable tracking, a multi-objective optimization controller is proposed using the theory of non-linear model predictive control. To quickly and precisely track control target in a finite time, an electronic brake controller for AEBS is designed based on the Nonsingular Fast Terminal Sliding Mode (NFTSM control theory. To validate the performance and advantages of the proposed algorithm, simulations are implemented. According to the simulation results, the proposed algorithm has better integrated performance in reducing the collision risk and improving the driving comfort and fuel economy of the smart car compared with the existing single AEBS.

New management algorithms in multiple sclerosis

DEFF Research Database (Denmark)

Sorensen, Per Soelberg

2014-01-01

complex. The purpose of the review has been to work out new management algorithms for treatment of relapsing-remitting multiple sclerosis including new oral therapies and therapeutic monoclonal antibodies. RECENT FINDINGS: Recent large placebo-controlled trials in relapsing-remitting multiple sclerosis......PURPOSE OF REVIEW: Our current treatment algorithms include only IFN-β and glatiramer as available first-line disease-modifying drugs and natalizumab and fingolimod as second-line therapies. Today, 10 drugs have been approved in Europe and nine in the United States making the choice of therapy more...

Work control in separations facilities

International Nuclear Information System (INIS)

Olson, L.D.

1990-01-01

The topic addressed in this technical review is the development and implementation of a work control program in one of the chemical separations facilities at the Savannah River Site (SRS) in Aiken, SC. This program will be used as a pilot for the Nuclear Materials Processing Division at the site. The SRS Work Control Pilot program is based on the Institute of Nuclear Power Operations (INPO) good practices and guidelines for the conduct of maintenance and complies with SRS quality assurance and DOE orders on maintenance management. The program follows a ten-step process for control of maintenance and maintenance-related activities in a chemical separations facility. The program took the existing maintenance planning and scheduling system and upgraded it to comply with all INPO work control and related guidelines for histories, post-maintenance testing and scheduling. The development process of adapting a nuclear-related- based plan to a batch/continuous chemical separations plant was a challenge. There were many opportunities to develop improvements in performance while being creative and realistic in applying reactor maintenance technology to chemical plant maintenance. This pilot program for work control in a nonreactor nuclear facility will provide valuable information for applying a controlled maintenance process to a multiphase chemical operating plant environment

Predictive factors for renal failure and a control and treatment algorithm

Directory of Open Access Journals (Sweden)

Denise de Paula Cerqueira

2014-04-01

Full Text Available OBJECTIVES: to evaluate the renal function of patients in an intensive care unit, to identify the predisposing factors for the development of renal failure, and to develop an algorithm to help in the control of the disease.METHOD: exploratory, descriptive, prospective study with a quantitative approach.RESULTS: a total of 30 patients (75.0% were diagnosed with kidney failure and the main factors associated with this disease were: advanced age, systemic arterial hypertension, diabetes mellitus, lung diseases, and antibiotic use. Of these, 23 patients (76.6% showed a reduction in creatinine clearance in the first 24 hours of hospitalization.CONCLUSION: a decline in renal function was observed in a significant number of subjects, therefore, an algorithm was developed with the aim of helping in the control of renal failure in a practical and functional way.

Analysis and Improvement of Control Algorithm for Operation Mode Transition due to Input Channel Trouble in Control Systems

International Nuclear Information System (INIS)

Ahn, Myunghoon; Kim, Woogoon; Yim, Hyeongsoon

2016-01-01

The PI (Proportional plus Integral) controller, which is the essential functional block in control systems, can automatically perform the stable control of an important plant process while reducing the steady state error and improving the transient response. However, if the received input PV (Process Variable) is not normal due to input channel trouble, it will be difficult to control the system automatically. For this reason, many control systems are implemented to change the operation mode from automatic to manual mode in the PI controller when the failed input PV is detected. If the PI controller is in automatic mode for all the time, the control signal varies as the change of the input PV is continuously reflected in the control algorithm. In the other cases, since the controller changes into the manual mode at t=0, the control signal is fixed at the last PI controller output and thus the feedback control is not performed anymore until the operator takes an action such as the operation mode change. As a result of analysis and simulations for the controller’s operation modes in all the cases of input channel trouble, we discovered that it is more appropriate to maintain the automatic mode despite the bad quality in the PV. Therefore, we improved the control system algorithm reflecting the analysis results for the operator’s convenience and the stability of a control system

Analysis and Improvement of Control Algorithm for Operation Mode Transition due to Input Channel Trouble in Control Systems

Energy Technology Data Exchange (ETDEWEB)

Ahn, Myunghoon; Kim, Woogoon; Yim, Hyeongsoon [KEPCO Engineering and Construction Co., Deajeon (Korea, Republic of)

2016-10-15

The PI (Proportional plus Integral) controller, which is the essential functional block in control systems, can automatically perform the stable control of an important plant process while reducing the steady state error and improving the transient response. However, if the received input PV (Process Variable) is not normal due to input channel trouble, it will be difficult to control the system automatically. For this reason, many control systems are implemented to change the operation mode from automatic to manual mode in the PI controller when the failed input PV is detected. If the PI controller is in automatic mode for all the time, the control signal varies as the change of the input PV is continuously reflected in the control algorithm. In the other cases, since the controller changes into the manual mode at t=0, the control signal is fixed at the last PI controller output and thus the feedback control is not performed anymore until the operator takes an action such as the operation mode change. As a result of analysis and simulations for the controller’s operation modes in all the cases of input channel trouble, we discovered that it is more appropriate to maintain the automatic mode despite the bad quality in the PV. Therefore, we improved the control system algorithm reflecting the analysis results for the operator’s convenience and the stability of a control system.

Tuning of active vibration controllers for ACTEX by genetic algorithm

Science.gov (United States)

Kwak, Moon K.; Denoyer, Keith K.

1999-06-01

This paper is concerned with the optimal tuning of digitally programmable analog controllers on the ACTEX-1 smart structures flight experiment. The programmable controllers for each channel include a third order Strain Rate Feedback (SRF) controller, a fifth order SRF controller, a second order Positive Position Feedback (PPF) controller, and a fourth order PPF controller. Optimal manual tuning of several control parameters can be a difficult task even though the closed-loop control characteristics of each controller are well known. Hence, the automatic tuning of individual control parameters using Genetic Algorithms is proposed in this paper. The optimal control parameters of each control law are obtained by imposing a constraint on the closed-loop frequency response functions using the ACTEX mathematical model. The tuned control parameters are then uploaded to the ACTEX electronic control electronics and experiments on the active vibration control are carried out in space. The experimental results on ACTEX will be presented.

Nature-inspired optimization algorithms

CERN Document Server

Yang, Xin-She

2014-01-01

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning

Chaotic queue-based genetic algorithm for design of a self-tuning fuzzy logic controller

Science.gov (United States)

Saini, Sanju; Saini, J. S.

2012-11-01

This paper employs a chaotic queue-based method using logistic equation in a non-canonical genetic algorithm for optimizing the performance of a self-tuning Fuzzy Logic Controller, used for controlling a nonlinear double-coupled system. A comparison has been made with a standard canonical genetic algorithm implemented on the same plant. It has been shown that chaotic queue-method brings an improvement in the performance of the FLC for wide range of set point changes by a more profound initial population spread in the search space.

Optimization of Pressurizer Based on Genetic-Simplex Algorithm

International Nuclear Information System (INIS)

Wang, Cheng; Yan, Chang Qi; Wang, Jian Jun

2014-01-01

Pressurizer is one of key components in nuclear power system. It's important to control the dimension in the design of pressurizer through optimization techniques. In this work, a mathematic model of a vertical electric heating pressurizer was established. A new Genetic-Simplex Algorithm (GSA) that combines genetic algorithm and simplex algorithm was developed to enhance the searching ability, and the comparison among modified and original algorithms is conducted by calculating the benchmark function. Furthermore, the optimization design of pressurizer, taking minimization of volume and net weight as objectives, was carried out considering thermal-hydraulic and geometric constraints through GSA. The results indicate that the mathematical model is agreeable for the pressurizer and the new algorithm is more effective than the traditional genetic algorithm. The optimization design shows obvious validity and can provide guidance for real engineering design

Navigation Algorithm Using Fuzzy Control Method in Mobile Robotics

Directory of Open Access Journals (Sweden)

Cviklovič Vladimír

2016-03-01

Full Text Available The issue of navigation methods is being continuously developed globally. The aim of this article is to test the fuzzy control algorithm for track finding in mobile robotics. The concept of an autonomous mobile robot EN20 has been designed to test its behaviour. The odometry navigation method was used. The benefits of fuzzy control are in the evidence of mobile robot’s behaviour. These benefits are obtained when more physical variables on the base of more input variables are controlled at the same time. In our case, there are two input variables - heading angle and distance, and two output variables - the angular velocity of the left and right wheel. The autonomous mobile robot is moving with human logic.

Implementation and analysis of an adaptive multilevel Monte Carlo algorithm

KAUST Repository

Hoel, Hakon; Von Schwerin, Erik; Szepessy, Anders; Tempone, Raul

2014-01-01

We present an adaptive multilevel Monte Carlo (MLMC) method for weak approximations of solutions to Itô stochastic dierential equations (SDE). The work [11] proposed and analyzed an MLMC method based on a hierarchy of uniform time discretizations and control variates to reduce the computational effort required by a single level Euler-Maruyama Monte Carlo method from O(TOL-3) to O(TOL-2 log(TOL-1)2) for a mean square error of O(TOL2). Later, the work [17] presented an MLMC method using a hierarchy of adaptively re ned, non-uniform time discretizations, and, as such, it may be considered a generalization of the uniform time discretizationMLMC method. This work improves the adaptiveMLMC algorithms presented in [17] and it also provides mathematical analysis of the improved algorithms. In particular, we show that under some assumptions our adaptive MLMC algorithms are asymptotically accurate and essentially have the correct complexity but with improved control of the complexity constant factor in the asymptotic analysis. Numerical tests include one case with singular drift and one with stopped diusion, where the complexity of a uniform single level method is O(TOL-4). For both these cases the results con rm the theory, exhibiting savings in the computational cost for achieving the accuracy O(TOL) from O(TOL-3) for the adaptive single level algorithm to essentially O(TOL-2 log(TOL-1)2) for the adaptive MLMC algorithm. © 2014 by Walter de Gruyter Berlin/Boston 2014.

Optimized Aircraft Electric Control System Based on Adaptive Tabu Search Algorithm and Fuzzy Logic Control

Directory of Open Access Journals (Sweden)

Saifullah Khalid

2016-09-01

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.

A Coral Reef Algorithm Based on Learning Automata for the Coverage Control Problem of Heterogeneous Directional Sensor Networks.

Science.gov (United States)

Li, Ming; Miao, Chunyan; Leung, Cyril

2015-12-04

Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches.

A Hybrid Fuzzy Genetic Algorithm for an Adaptive Traffic Signal System

Directory of Open Access Journals (Sweden)

S. M. Odeh

2015-01-01

Full Text Available This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC and Genetic Algorithms (GAs and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC, and up to 31% in the comparison with a traditional logic controller, FLC.

Algorithm and assessment work of active fire detection based on FengYun-3C/VIRR

Science.gov (United States)

Lin, Z.; Chen, F.

2017-12-01

The wildfire is one of the most destructive and uncontrollable disasters and causes huge environmental, ecological, social effects. To better serve scientific research and practical fire management, an algorithm and corresponding validation work of active fire detection based on FengYun-3C/VIRR data, which is an optical sensor onboard the Chinese polar-orbiting meteorological sun-synchronous satellite, is hereby introduced. While the main structure heritages the `contextual algorithm', some new concepts including `infrared channel slope' are introduced for better adaptions to different situations. The validation work contains three parts: 1) comparing with the current FengYun-3C fire product GFR; 2) comparing with MODIS fire products; 3) comparing with Landsat series data. Study areas are selected from different places all over the world from 2014 to 2016. The results showed great improvement on GFR files on accuracy of both positioning and detection rate. In most study areas, the results match well with MODIS products and Landsat series data (with over 85% match degree) despite the differences in imaging time. However, detection rates and match degrees in Africa and South-east Asia are not satisfied (around 70%), where the occurrences of numerous small fire events and corresponding smokes may strongly affect the results of the algorithm. This is our future research direction and one of the main improvements requires achieving.

Maximum power point tracking-based control algorithm for PMSG wind generation system without mechanical sensors

International Nuclear Information System (INIS)

Hong, Chih-Ming; Chen, Chiung-Hsing; Tu, Chia-Sheng

2013-01-01

Highlights: ► This paper presents MPPT based control for optimal wind energy capture using RBFN. ► MPSO is adopted to adjust the learning rates to improve the learning capability. ► This technique can maintain the system stability and reach the desired performance. ► The EMF in the rotating reference frame is utilized in order to estimate speed. - Abstract: This paper presents maximum-power-point-tracking (MPPT) based control algorithms for optimal wind energy capture using radial basis function network (RBFN) and a proposed torque observer MPPT algorithm. The design of a high-performance on-line training RBFN using back-propagation learning algorithm with modified particle swarm optimization (MPSO) regulating controller for the sensorless control of a permanent magnet synchronous generator (PMSG). The MPSO is adopted in this study to adapt the learning rates in the back-propagation process of the RBFN to improve the learning capability. The PMSG is controlled by the loss-minimization control with MPPT below the base speed, which corresponds to low and high wind speed, and the maximum energy can be captured from the wind. Then the observed disturbance torque is feed-forward to increase the robustness of the PMSG system

Novel probabilistic and distributed algorithms for guidance, control, and nonlinear estimation of large-scale multi-agent systems

Science.gov (United States)

Bandyopadhyay, Saptarshi

Multi-agent systems are widely used for constructing a desired formation shape, exploring an area, surveillance, coverage, and other cooperative tasks. This dissertation introduces novel algorithms in the three main areas of shape formation, distributed estimation, and attitude control of large-scale multi-agent systems. In the first part of this dissertation, we address the problem of shape formation for thousands to millions of agents. Here, we present two novel algorithms for guiding a large-scale swarm of robotic systems into a desired formation shape in a distributed and scalable manner. These probabilistic swarm guidance algorithms adopt an Eulerian framework, where the physical space is partitioned into bins and the swarm's density distribution over each bin is controlled using tunable Markov chains. In the first algorithm - Probabilistic Swarm Guidance using Inhomogeneous Markov Chains (PSG-IMC) - each agent determines its bin transition probabilities using a time-inhomogeneous Markov chain that is constructed in real-time using feedback from the current swarm distribution. This PSG-IMC algorithm minimizes the expected cost of the transitions required to achieve and maintain the desired formation shape, even when agents are added to or removed from the swarm. The algorithm scales well with a large number of agents and complex formation shapes, and can also be adapted for area exploration applications. In the second algorithm - Probabilistic Swarm Guidance using Optimal Transport (PSG-OT) - each agent determines its bin transition probabilities by solving an optimal transport problem, which is recast as a linear program. In the presence of perfect feedback of the current swarm distribution, this algorithm minimizes the given cost function, guarantees faster convergence, reduces the number of transitions for achieving the desired formation, and is robust to disturbances or damages to the formation. We demonstrate the effectiveness of these two proposed swarm

A real-time and closed-loop control algorithm for cascaded multilevel inverter based on artificial neural network.

Science.gov (United States)

Wang, Libing; Mao, Chengxiong; Wang, Dan; Lu, Jiming; Zhang, Junfeng; Chen, Xun

2014-01-01

In order to control the cascaded H-bridges (CHB) converter with staircase modulation strategy in a real-time manner, a real-time and closed-loop control algorithm based on artificial neural network (ANN) for three-phase CHB converter is proposed in this paper. It costs little computation time and memory. It has two steps. In the first step, hierarchical particle swarm optimizer with time-varying acceleration coefficient (HPSO-TVAC) algorithm is employed to minimize the total harmonic distortion (THD) and generate the optimal switching angles offline. In the second step, part of optimal switching angles are used to train an ANN and the well-designed ANN can generate optimal switching angles in a real-time manner. Compared with previous real-time algorithm, the proposed algorithm is suitable for a wider range of modulation index and results in a smaller THD and a lower calculation time. Furthermore, the well-designed ANN is embedded into a closed-loop control algorithm for CHB converter with variable direct voltage (DC) sources. Simulation results demonstrate that the proposed closed-loop control algorithm is able to quickly stabilize load voltage and minimize the line current's THD (<5%) when subjecting the DC sources disturbance or load disturbance. In real design stage, a switching angle pulse generation scheme is proposed and experiment results verify its correctness.

A Real-Time and Closed-Loop Control Algorithm for Cascaded Multilevel Inverter Based on Artificial Neural Network

Directory of Open Access Journals (Sweden)

Libing Wang

2014-01-01

Full Text Available In order to control the cascaded H-bridges (CHB converter with staircase modulation strategy in a real-time manner, a real-time and closed-loop control algorithm based on artificial neural network (ANN for three-phase CHB converter is proposed in this paper. It costs little computation time and memory. It has two steps. In the first step, hierarchical particle swarm optimizer with time-varying acceleration coefficient (HPSO-TVAC algorithm is employed to minimize the total harmonic distortion (THD and generate the optimal switching angles offline. In the second step, part of optimal switching angles are used to train an ANN and the well-designed ANN can generate optimal switching angles in a real-time manner. Compared with previous real-time algorithm, the proposed algorithm is suitable for a wider range of modulation index and results in a smaller THD and a lower calculation time. Furthermore, the well-designed ANN is embedded into a closed-loop control algorithm for CHB converter with variable direct voltage (DC sources. Simulation results demonstrate that the proposed closed-loop control algorithm is able to quickly stabilize load voltage and minimize the line current’s THD (<5% when subjecting the DC sources disturbance or load disturbance. In real design stage, a switching angle pulse generation scheme is proposed and experiment results verify its correctness.

Small Body GN&C Research Report: A Robust Model Predictive Control Algorithm with Guaranteed Resolvability

Science.gov (United States)

Acikmese, Behcet A.; Carson, John M., III

2005-01-01

A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees the resolvability of the associated finite-horizon optimal control problem in a receding-horizon implementation. The control consists of two components; (i) feedforward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives, and derivatives in polytopes. An illustrative numerical example is also provided.

Learning Mobility: Adaptive Control Algorithms for the Novel Unmanned Ground Vehicle (NUGV)

National Research Council Canada - National Science Library

Blackburn, Mike

2003-01-01

Mobility is a serious limiting factor in the usefulness of unmanned ground vehicles, This paper contains a description of our approach to develop control algorithms for the Novel Unmanned Ground Vehicle (NUGV...

An Interactive Control Algorithm Used for Equilateral Triangle Formation with Robotic Sensors

Science.gov (United States)

Li, Xiang; Chen, Hongcai

2014-01-01

This paper describes an interactive control algorithm, called Triangle Formation Algorithm (TFA), used for three neighboring robotic sensors which are distributed randomly to self-organize into and equilateral triangle (E) formation. The algorithm is proposed based on the triangular geometry and considering the actual sensors used in robotics. In particular, the stability of the TFA, which can be executed by robotic sensors independently and asynchronously for E formation, is analyzed in details based on Lyapunov stability theory. Computer simulations are carried out for verifying the effectiveness of the TFA. The analytical results and simulation studies indicate that three neighboring robots employing conventional sensors can self-organize into E formations successfully regardless of their initial distribution using the same TFAs. PMID:24759118

Genetic Algorithm Based PID Controller Tuning Approach for Continuous Stirred Tank Reactor

OpenAIRE

A. Jayachitra; R. Vinodha

2014-01-01

Genetic algorithm (GA) based PID (proportional integral derivative) controller has been proposed for tuning optimized PID parameters in a continuous stirred tank reactor (CSTR) process using a weighted combination of objective functions, namely, integral square error (ISE), integral absolute error (IAE), and integrated time absolute error (ITAE). Optimization of PID controller parameters is the key goal in chemical and biochemical industries. PID controllers have narrowed down the operating r...

System control fuzzy neural sewage pumping stations using genetic algorithms

Directory of Open Access Journals (Sweden)

Владлен Николаевич Кузнецов

2015-06-01

Full Text Available It is considered the system of management of sewage pumping station with regulators based on a neuron network with fuzzy logic. Linguistic rules for the controller based on fuzzy logic, maintaining the level of effluent in the receiving tank within the prescribed limits are developed. The use of genetic algorithms for neuron network training is shown.

Sensitivity Analysis of Linear Programming and Quadratic Programming Algorithms for Control Allocation

Science.gov (United States)

Frost, Susan A.; Bodson, Marc; Acosta, Diana M.

2009-01-01

The Next Generation (NextGen) transport aircraft configurations being investigated as part of the NASA Aeronautics Subsonic Fixed Wing Project have more control surfaces, or control effectors, than existing transport aircraft configurations. Conventional flight control is achieved through two symmetric elevators, two antisymmetric ailerons, and a rudder. The five effectors, reduced to three command variables, produce moments along the three main axes of the aircraft and enable the pilot to control the attitude and flight path of the aircraft. The NextGen aircraft will have additional redundant control effectors to control the three moments, creating a situation where the aircraft is over-actuated and where a simple relationship does not exist anymore between the required effector deflections and the desired moments. NextGen flight controllers will incorporate control allocation algorithms to determine the optimal effector commands and attain the desired moments, taking into account the effector limits. Approaches to solving the problem using linear programming and quadratic programming algorithms have been proposed and tested. It is of great interest to understand their relative advantages and disadvantages and how design parameters may affect their properties. In this paper, we investigate the sensitivity of the effector commands with respect to the desired moments and show on some examples that the solutions provided using the l2 norm of quadratic programming are less sensitive than those using the l1 norm of linear programming.

Discrete-State-Based Vision Navigation Control Algorithm for One Bipedal Robot

Directory of Open Access Journals (Sweden)

Dunwen Wei

2015-01-01

Full Text Available Navigation with the specific objective can be defined by specifying desired timed trajectory. The concept of desired direction field is proposed to deal with such navigation problem. To lay down a principled discussion of the accuracy and efficiency of navigation algorithms, strictly quantitative definitions of tracking error, actuator effect, and time efficiency are established. In this paper, one vision navigation control method based on desired direction field is proposed. This proposed method uses discrete image sequences to form discrete state space, which is especially suitable for bipedal walking robots with single camera walking on a free-barrier plane surface to track the specific objective without overshoot. The shortest path method (SPM is proposed to design such direction field with the highest time efficiency. However, one improved control method called canonical piecewise-linear function (PLF is proposed. In order to restrain the noise disturbance from the camera sensor, the band width control method is presented to significantly decrease the error influence. The robustness and efficiency of the proposed algorithm are illustrated through a number of computer simulations considering the error from camera sensor. Simulation results show that the robustness and efficiency can be balanced by choosing the proper controlling value of band width.

Synchronization Algorithm for SDN-controlled All-Optical TDM Switching in a Random Length Ring Network

DEFF Research Database (Denmark)

Kamchevska, Valerija; Cristofori, Valentina; Da Ros, Francesco

2016-01-01

We propose and demonstrate an algorithm that allows for automatic synchronization of SDN-controlled all-optical TDM switching nodes connected in a ring network. We experimentally show successful WDM-SDM transmission of data bursts between all ring nodes.......We propose and demonstrate an algorithm that allows for automatic synchronization of SDN-controlled all-optical TDM switching nodes connected in a ring network. We experimentally show successful WDM-SDM transmission of data bursts between all ring nodes....

Optimal Power Flow Using Gbest-Guided Cuckoo Search Algorithm with Feedback Control Strategy and Constraint Domination Rule

Directory of Open Access Journals (Sweden)

Gonggui Chen

2017-01-01

Full Text Available The optimal power flow (OPF is well-known as a significant optimization tool for the security and economic operation of power system, and OPF problem is a complex nonlinear, nondifferentiable programming problem. Thus this paper proposes a Gbest-guided cuckoo search algorithm with the feedback control strategy and constraint domination rule which is named as FCGCS algorithm for solving OPF problem and getting optimal solution. This FCGCS algorithm is guided by the global best solution for strengthening exploitation ability. Feedback control strategy is devised to dynamically regulate the control parameters according to actual and specific feedback value in the simulation process. And the constraint domination rule can efficiently handle inequality constraints on state variables, which is superior to traditional penalty function method. The performance of FCGCS algorithm is tested and validated on the IEEE 30-bus and IEEE 57-bus example systems, and simulation results are compared with different methods obtained from other literatures recently. The comparison results indicate that FCGCS algorithm can provide high-quality feasible solutions for different OPF problems.

Comparative Study of Evolutionary Multi-objective Optimization Algorithms for a Non-linear Greenhouse Climate Control Problem

DEFF Research Database (Denmark)

Ghoreishi, Newsha; Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard

2015-01-01

Non-trivial real world decision-making processes usually involve multiple parties having potentially conflicting interests over a set of issues. State-of-the-art multi-objective evolutionary algorithms (MOEA) are well known to solve this class of complex real-world problems. In this paper, we...... compare the performance of state-of-the-art multi-objective evolutionary algorithms to solve a non-linear multi-objective multi-issue optimisation problem found in Greenhouse climate control. The chosen algorithms in the study includes NSGAII, eNSGAII, eMOEA, PAES, PESAII and SPEAII. The performance...... of all aforementioned algorithms is assessed and compared using performance indicators to evaluate proximity, diversity and consistency. Our insights to this comparative study enhanced our understanding of MOEAs performance in order to solve a non-linear complex climate control problem. The empirical...

High-speed algorithm for calculating the neutron field in a reactor when working in dialog mode with a computer

International Nuclear Information System (INIS)

Afanas'ev, A.M.

1987-01-01

The large-scale construction of atomic power stations results in a need for trainers to instruct power-station personnel. The present work considers one problem of developing training computer software, associated with the development of a high-speed algorithm for calculating the neutron field after control-rod (CR) shift by the operator. The case considered here is that in which training units are developed on the basis of small computers of SM-2 type, which fall significantly short of the BESM-6 and EC-type computers used for the design calculations, in terms of speed and memory capacity. Depending on the apparatus for solving the criticality problem, in a two-dimensional single-group approximation, the physical-calculation programs require ∼ 1 min of machine time on a BESM-6 computer, which translates to ∼ 10 min on an SM-2 machine. In practice, this time is even longer, since ultimately it is necessary to determine not the effective multiplication factor K/sub ef/, but rather the local perturbations of the emergency-control (EC) system (to reach criticality) and change in the neutron field on shifting the CR and the EC rods. This long time means that it is very problematic to use physical-calculation programs to work in dialog mode with a computer. The algorithm presented below allows the neutron field following shift of the CR and EC rods to be calculated in a few seconds on a BESM-6 computer (tens of second on an SM-2 machine. This high speed may be achieved as a result of the preliminary calculation of the influence function (IF) for each CR. The IF may be calculated at high speed on a computer. Then it is stored in the external memory (EM) and, where necessary, used as the initial information

Novel control algorithm of braking energy regeneration system for an electric vehicle during safety–critical driving maneuvers

International Nuclear Information System (INIS)

Lv, Chen; Zhang, Junzhi; Li, Yutong; Yuan, Ye

2015-01-01

Highlights: • Models of an electric vehicle with regenerative braking system (RBS) are built. • Control algorithm of RBS under safety–critical driving maneuvers is proposed. • Simulations and HIL tests of the proposed strategy are conducted. • Performance improvement of vehicle’s mean deceleration is up to 13.89%. • Test results verify the feasibility and effectiveness of the proposed method. - Abstract: This paper mainly focuses on control algorithm of the braking energy regeneration system of an electric bus under safety–critical driving situations. With the aims of guaranteeing vehicle stability in various types of tyre–road adhesion conditions, based on the characteristics of electrified powertrain, a novel control algorithm of regenerative braking system is proposed for electric vehicles during anti-lock braking procedures. First, the models of vehicle dynamics and main components including braking energy regenerative system of the case-study electric bus are built in MATLAB/Simulink. Then, based on the phase-plane method, the optimal brake torque is calculated for ABS control of vehicle. Next, a novel allocation strategy, wherein the target optimal brake torque is divided into two parts that are handled separately by the regenerative and friction brakes, is developed. Simulations of the proposed control strategy are conducted based on system models built using MATLAB/Simulink. The simulation results demonstrate that the developed strategy enables improved control in terms of vehicle stability and braking performance under different emergency driving conditions. To further verify the synthesized control algorithm, hardware-in-the-loop tests are also performed. The experimental results validate the simulation data and verify the feasibility and effectiveness of the developed control algorithm.

Synthesizing mixed H2/H-infinity dynamic controller using evolutionary algorithms

DEFF Research Database (Denmark)

Pedersen, Gerulf; Langballe, A.S.; Wisniewski, Rafal

2001-01-01

This paper covers the design of an Evolutionary Algorithm (EA), which should be able to synthesize a mixed H2/H-infinity. It will be shown how a system can be expressed as Matrix Inequalities (MI) and these will then be used in the design of the EA. The main objective is to examine whether a mixed...... H2/H-infinity controller is feasible, and if so, how the optimal mixed controller might befound....

Algorithm Design and Validation for Adaptive Nonlinear Control Enhancement (ADVANCE) Technology Development for Resilient Flight Control, Phase I

Data.gov (United States)

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...

Monitoring endemic livestock diseases using laboratory diagnostic data: A simulation study to evaluate the performance of univariate process monitoring control algorithms.

Science.gov (United States)

Lopes Antunes, Ana Carolina; Dórea, Fernanda; Halasa, Tariq; Toft, Nils

2016-05-01

Surveillance systems are critical for accurate, timely monitoring and effective disease control. In this study, we investigated the performance of univariate process monitoring control algorithms in detecting changes in seroprevalence for endemic diseases. We also assessed the effect of sample size (number of sentinel herds tested in the surveillance system) on the performance of the algorithms. Three univariate process monitoring control algorithms were compared: Shewart p Chart(1) (PSHEW), Cumulative Sum(2) (CUSUM) and Exponentially Weighted Moving Average(3) (EWMA). Increases in seroprevalence were simulated from 0.10 to 0.15 and 0.20 over 4, 8, 24, 52 and 104 weeks. Each epidemic scenario was run with 2000 iterations. The cumulative sensitivity(4) (CumSe) and timeliness were used to evaluate the algorithms' performance with a 1% false alarm rate. Using these performance evaluation criteria, it was possible to assess the accuracy and timeliness of the surveillance system working in real-time. The results showed that EWMA and PSHEW had higher CumSe (when compared with the CUSUM) from week 1 until the end of the period for all simulated scenarios. Changes in seroprevalence from 0.10 to 0.20 were more easily detected (higher CumSe) than changes from 0.10 to 0.15 for all three algorithms. Similar results were found with EWMA and PSHEW, based on the median time to detection. Changes in the seroprevalence were detected later with CUSUM, compared to EWMA and PSHEW for the different scenarios. Increasing the sample size 10 fold halved the time to detection (CumSe=1), whereas increasing the sample size 100 fold reduced the time to detection by a factor of 6. This study investigated the performance of three univariate process monitoring control algorithms in monitoring endemic diseases. It was shown that automated systems based on these detection methods identified changes in seroprevalence at different times. Increasing the number of tested herds would lead to faster

Modification of the algorithm for steam turbine control under loading drop

International Nuclear Information System (INIS)

Nikitin, Yu.V.; Mirnyj, V.A.; Gritsenko, V.N.; Nesterov, L.V.

1989-01-01

Problem related to powerful steam turbine control in case of emergency loading drop is considered. Two laws of control creating conditions for qualitative operation of control system under conditions considered are compared. The system of turbine control comprises the turbine major actuating mechanisms (electrohydraulic transducer, high-pressure servomotor, cut-off slide valve) actuating mechanisms of pulse discharge channel (low-pressure servomotor cut-off slide valve, low-pressure servomotor) and regulator. The frequency of the turbine rotor rotation is the parameter to be controlled in the mode of loading drop. The algorithms considered are based on linear variant of the optimal control theory. One of them is realized in electrohydraulic system of the K-750-65/3000 turbine control at the Ignalinsk NPP

A Novel Algorithm (G-JPSO and Its Development for the Optimal Control of Pumps in Water Distribution Networks

Directory of Open Access Journals (Sweden)

Rasoul Rajabpour

2017-01-01

Full Text Available Recent decades have witnessed growing applications of metaheuristic techniques as efficient tools for solving complex engineering problems. One such method is the JPSO algorithm. In this study, innovative modifications were made in the nature of the jump algorithm JPSO to make it capable of coping with graph-based solutions, which led to the development of a new algorithm called ‘G-JPSO’. The new algorithm was then used to solve the Fletcher-Powell optimal control problem and its application to optimal control of pumps in water distribution networks was evaluated. Optimal control of pumps consists in an optimum operation timetable (on and off for each of the pumps at the desired time interval. Maximum number of on and off positions for each pump was introduced into the objective function as a constraint such that not only would power consumption at each node be reduced but such problem requirements as the minimum pressure required at each node and minimum/maximum storage tank heights would be met. To determine the optimal operation of pumps, a model-based optimization-simulation algorithm was developed based on G-JPSO and JPSO algorithms. The model proposed by van Zyl was used to determine the optimal operation of the distribution network. Finally, the results obtained from the proposed algorithm were compared with those obtained from ant colony, genetic, and JPSO algorithms to show the robustness of the proposed algorithm in finding near-optimum solutions at reasonable computation costs.

Proposal for a fully decentralized blockchain and proof-of-work algorithm for solving NP-complete problems

OpenAIRE

Oliver, Carlos G.; Ricottone, Alessandro; Philippopoulos, Pericles

2017-01-01

We propose a proof-of-work algorithm that rewards blockchain miners for using computational resources to solve NP-complete puzzles. The resulting blockchain will publicly store and improve solutions to problems with real world applications while maintaining a secure and fully functional transaction ledger.

Stochastic Recursive Algorithms for Optimization Simultaneous Perturbation Methods

CERN Document Server

Bhatnagar, S; Prashanth, L A

2013-01-01

Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: • are easily implemented; • do not require an explicit system model; and • work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from sim...

Performance improvement of VAV air conditioning system through feedforward compensation decoupling and genetic algorithm

International Nuclear Information System (INIS)

Wang Jun; Wang Yan

2008-01-01

VAV (variable air volume) control system has the feature of multi-control loops. While all the control loops are working together, they interfere and influence each other. This paper designs the decoupling compensation unit in VAV system in the method of feedforward compensation. This paper also designs the controller parameters of VAV system by means of inverse deducing and the genetic algorithm. Experimental results demonstrate that the combination of the feedforward compensation decoupling and the controller optimization by genetic algorithm can improve the performance of the VAV control system

Diversity Controlling Genetic Algorithm for Order Acceptance and Scheduling Problem

Directory of Open Access Journals (Sweden)

Cheng Chen

2014-01-01

Full Text Available Selection and scheduling are an important topic in production systems. To tackle the order acceptance and scheduling problem on a single machine with release dates, tardiness penalty, and sequence-dependent setup times, in this paper a diversity controlling genetic algorithm (DCGA is proposed, in which a diversified population is maintained during the whole search process through survival selection considering both the fitness and the diversity of individuals. To measure the similarity between individuals, a modified Hamming distance without considering the unaccepted orders in the chromosome is adopted. The proposed DCGA was validated on 1500 benchmark instances with up to 100 orders. Compared with the state-of-the-art algorithms, the experimental results show that DCGA improves the solution quality obtained significantly, in terms of the deviation from upper bound.

Flight Testing of the Space Launch System (SLS) Adaptive Augmenting Control (AAC) Algorithm on an F/A-18

Science.gov (United States)

Dennehy, Cornelius J.; VanZwieten, Tannen S.; Hanson, Curtis E.; Wall, John H.; Miller, Chris J.; Gilligan, Eric T.; Orr, Jeb S.

2014-01-01

The Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division developed an adaptive augmenting control (AAC) algorithm for launch vehicles that improves robustness and performance on an as-needed basis by adapting a classical control algorithm to unexpected environments or variations in vehicle dynamics. This was baselined as part of the Space Launch System (SLS) flight control system. The NASA Engineering and Safety Center (NESC) was asked to partner with the SLS Program and the Space Technology Mission Directorate (STMD) Game Changing Development Program (GCDP) to flight test the AAC algorithm on a manned aircraft that can achieve a high level of dynamic similarity to a launch vehicle and raise the technology readiness of the algorithm early in the program. This document reports the outcome of the NESC assessment.

Modelling and control algorithms of the cross conveyors line with multiengine variable speed drives

Science.gov (United States)

Cheremushkina, M. S.; Baburin, S. V.

2017-02-01

The paper deals with the actual problem of developing the control algorithm that meets the technical requirements of the mine belt conveyors, and enables energy and resource savings taking into account a random sort of traffic. The most effective method of solution of these tasks is the construction of control systems with the use of variable speed drives for asynchronous motors. The authors designed the mathematical model of the system ‘variable speed multiengine drive - conveyor - control system of conveyors’ that takes into account the dynamic processes occurring in the elements of the transport system, provides an assessment of the energy efficiency of application the developed algorithms, which allows one to reduce the dynamic overload in the belt to 15-20%.

Prediction models and control algorithms for predictive applications of setback temperature in cooling systems

International Nuclear Information System (INIS)

Moon, Jin Woo; Yoon, Younju; Jeon, Young-Hoon; Kim, Sooyoung

2017-01-01

Highlights: • Initial ANN model was developed for predicting the time to the setback temperature. • Initial model was optimized for producing accurate output. • Optimized model proved its prediction accuracy. • ANN-based algorithms were developed and tested their performance. • ANN-based algorithms presented superior thermal comfort or energy efficiency. - Abstract: In this study, a temperature control algorithm was developed to apply a setback temperature predictively for the cooling system of a residential building during occupied periods by residents. An artificial neural network (ANN) model was developed to determine the required time for increasing the current indoor temperature to the setback temperature. This study involved three phases: development of the initial ANN-based prediction model, optimization and testing of the initial model, and development and testing of three control algorithms. The development and performance testing of the model and algorithm were conducted using TRNSYS and MATLAB. Through the development and optimization process, the final ANN model employed indoor temperature and the temperature difference between the current and target setback temperature as two input neurons. The optimal number of hidden layers, number of neurons, learning rate, and moment were determined to be 4, 9, 0.6, and 0.9, respectively. The tangent–sigmoid and pure-linear transfer function was used in the hidden and output neurons, respectively. The ANN model used 100 training data sets with sliding-window method for data management. Levenberg-Marquart training method was employed for model training. The optimized model had a prediction accuracy of 0.9097 root mean square errors when compared with the simulated results. Employing the ANN model, ANN-based algorithms maintained indoor temperatures better within target ranges. Compared to the conventional algorithm, the ANN-based algorithms reduced the duration of time, in which the indoor temperature

Multi-objective decoupling algorithm for active distance control of intelligent hybrid electric vehicle

Science.gov (United States)

Luo, Yugong; Chen, Tao; Li, Keqiang

2015-12-01

The paper presents a novel active distance control strategy for intelligent hybrid electric vehicles (IHEV) with the purpose of guaranteeing an optimal performance in view of the driving functions, optimum safety, fuel economy and ride comfort. Considering the complexity of driving situations, the objects of safety and ride comfort are decoupled from that of fuel economy, and a hierarchical control architecture is adopted to improve the real-time performance and the adaptability. The hierarchical control structure consists of four layers: active distance control object determination, comprehensive driving and braking torque calculation, comprehensive torque distribution and torque coordination. The safety distance control and the emergency stop algorithms are designed to achieve the safety and ride comfort goals. The optimal rule-based energy management algorithm of the hybrid electric system is developed to improve the fuel economy. The torque coordination control strategy is proposed to regulate engine torque, motor torque and hydraulic braking torque to improve the ride comfort. This strategy is verified by simulation and experiment using a forward simulation platform and a prototype vehicle. The results show that the novel control strategy can achieve the integrated and coordinated control of its multiple subsystems, which guarantees top performance of the driving functions and optimum safety, fuel economy and ride comfort.

Compliance control based on PSO algorithm to improve the feeling during physical human-robot interaction.

Science.gov (United States)

Jiang, Zhongliang; Sun, Yu; Gao, Peng; Hu, Ying; Zhang, Jianwei

2016-01-01

Robots play more important roles in daily life and bring us a lot of convenience. But when people work with robots, there remain some significant differences in human-human interactions and human-robot interaction. It is our goal to make robots look even more human-like. We design a controller which can sense the force acting on any point of a robot and ensure the robot can move according to the force. First, a spring-mass-dashpot system was used to describe the physical model, and the second-order system is the kernel of the controller. Then, we can establish the state space equations of the system. In addition, the particle swarm optimization algorithm had been used to obtain the system parameters. In order to test the stability of system, the root-locus diagram had been shown in the paper. Ultimately, some experiments had been carried out on the robotic spinal surgery system, which is developed by our team, and the result shows that the new controller performs better during human-robot interaction.

PREDICTIVE CONTROL OF A BATCH POLYMERIZATION SYSTEM USING A FEEDFORWARD NEURAL NETWORK WITH ONLINE ADAPTATION BY GENETIC ALGORITHM

Directory of Open Access Journals (Sweden)

A. Cancelier

Full Text Available Abstract This study used a predictive controller based on an empirical nonlinear model comprising a three-layer feedforward neural network for temperature control of the suspension polymerization process. In addition to the offline training technique, an algorithm was also analyzed for online adaptation of its parameters. For the offline training, the network was statically trained and the genetic algorithm technique was used in combination with the least squares method. For online training, the network was trained on a recurring basis and only the technique of genetic algorithms was used. In this case, only the weights and bias of the output layer neuron were modified, starting from the parameters obtained from the offline training. From the experimental results obtained in a pilot plant, a good performance was observed for the proposed control system, with superior performance for the control algorithm with online adaptation of the model, particularly with respect to the presence of off-set for the case of the fixed parameters model.

Multicontroller: an object programming approach to introduce advanced control algorithms for the GCS large scale project

CERN Document Server

Cabaret, S; Coppier, H; Rachid, A; Barillère, R; CERN. Geneva. IT Department

2007-01-01

The GCS (Gas Control System) project team at CERN uses a Model Driven Approach with a Framework - UNICOS (UNified Industrial COntrol System) - based on PLC (Programming Language Controller) and SCADA (Supervisory Control And Data Acquisition) technologies. The first' UNICOS versions were able to provide a PID (Proportional Integrative Derivative) controller whereas the Gas Systems required more advanced control strategies. The MultiController is a new UNICOS object which provides the following advanced control algorithms: Smith Predictor, PFC (Predictive Function Control), RST* and GPC (Global Predictive Control). Its design is based on a monolithic entity with a global structure definition which is able to capture the desired set of parameters of any specific control algorithm supported by the object. The SCADA system -- PVSS - supervises the MultiController operation. The PVSS interface provides users with supervision faceplate, in particular it links any MultiController with recipes: the GCS experts are ab...

An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network

Directory of Open Access Journals (Sweden)

Kai Hu

2013-01-01

Full Text Available A new image filtering algorithm is proposed. GA-BPN algorithm uses genetic algorithm (GA to decide weights in a back propagation neural network (BPN. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-BPN to do image noise filter researching work. Firstly, this paper uses training samples to train GA-BPN as the noise detector. Then, we utilize the well-trained GA-BPN to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-BPN. Experiment data shows that this algorithm has better performance than other filters.

Searching for full power control rod patterns in a boiling water reactor using genetic algorithms

Energy Technology Data Exchange (ETDEWEB)

Montes, Jose Luis [Departamento Sistemas Nucleares, ININ, Carr. Mexico-Toluca Km. 36.5, Ocoyoacac, Edo. de Mexico (Mexico)]. E-mail: jlmt@nuclear.inin.mx; Ortiz, Juan Jose [Departamento Sistemas Nucleares, ININ, Carr. Mexico-Toluca Km. 36.5, Ocoyoacac, Edo. de Mexico (Mexico)]. E-mail: jjortiz@nuclear.inin.mx; Requena, Ignacio [Departamento Ciencias Computacion e I.A. ETSII, Informatica, Universidad de Granada, C. Daniel Saucedo Aranda s/n. 18071 Granada (Spain)]. E-mail: requena@decsai.ugr.es; Perusquia, Raul [Departamento Sistemas Nucleares, ININ, Carr. Mexico-Toluca Km. 36.5, Ocoyoacac, Edo. de Mexico (Mexico)]. E-mail: rpc@nuclear.inin.mx

2004-11-01

One of the most important questions related to both safety and economic aspects in a nuclear power reactor operation, is without any doubt its reactivity control. During normal operation of a boiling water reactor, the reactivity control of its core is strongly determined by control rods patterns efficiency. In this paper, GACRP system is proposed based on the concepts of genetic algorithms for full power control rod patterns search. This system was carried out using LVNPP transition cycle characteristics, being applied too to an equilibrium cycle. Several operation scenarios, including core water flow variation throughout the cycle and different target axial power distributions, are considered. Genetic algorithm fitness function includes reactor security parameters, such as MLHGR, MCPR, reactor k{sub eff} and axial power density.

A new HBMO algorithm for multiobjective daily Volt/Var control in distribution systems considering Distributed Generators

Energy Technology Data Exchange (ETDEWEB)

Niknam, Taher [Electrical and Electronics Engineering Department, Shiraz University of Technology, Modars Blvd. P.O. 71555-313, Shiraz (Iran, Islamic Republic of)

2011-03-15

In recent years, Distributed Generators (DGs) connected to the distribution network have received increasing attention. The connection of enormous DGs into existing distribution network changes the operation of distribution systems. Because of the small X/R ratio and radial structure of distribution systems, DGs affect the daily Volt/Var control. This paper presents a new algorithm for multiobjective daily Volt/Var control in distribution systems including Distributed Generators (DGs). The objectives are costs of energy generation by DGs and distribution companies, electrical energy losses and the voltage deviations for the next day. A new optimization algorithm based on a Chaotic Improved Honey Bee Mating Optimization (CIHBMO) is proposed to determine the active power values of DGs, reactive power values of capacitors and tap positions of transformers for the next day. Since objectives are not the same, a fuzzy system is used to calculate the best solution. The plausibility of the proposed algorithm is demonstrated and its performance is compared with other methods on a 69-bus distribution feeder. Simulation results illustrate that the proposed algorithm has better outperforms the other algorithms. (author)

A new HBMO algorithm for multiobjective daily Volt/Var control in distribution systems considering Distributed Generators

International Nuclear Information System (INIS)

Niknam, Taher

2011-01-01

In recent years, Distributed Generators (DGs) connected to the distribution network have received increasing attention. The connection of enormous DGs into existing distribution network changes the operation of distribution systems. Because of the small X/R ratio and radial structure of distribution systems, DGs affect the daily Volt/Var control. This paper presents a new algorithm for multiobjective daily Volt/Var control in distribution systems including Distributed Generators (DGs). The objectives are costs of energy generation by DGs and distribution companies, electrical energy losses and the voltage deviations for the next day. A new optimization algorithm based on a Chaotic Improved Honey Bee Mating Optimization (CIHBMO) is proposed to determine the active power values of DGs, reactive power values of capacitors and tap positions of transformers for the next day. Since objectives are not the same, a fuzzy system is used to calculate the best solution. The plausibility of the proposed algorithm is demonstrated and its performance is compared with other methods on a 69-bus distribution feeder. Simulation results illustrate that the proposed algorithm has better outperforms the other algorithms.

Improvement of Networked Control Systems Performance Using a New Encryption Algorithm

Directory of Open Access Journals (Sweden)

Seyed Ali Mesbahifard

2014-07-01

Full Text Available Networked control systems are control systems which controllers and plants are connected via telecommunication network. One of the most important challenges in networked control systems is the problem of network time delay. Increasing of time delay may affect on control system performance extremely. Other important issue in networked control systems is the security problems. Since it is possible that unknown people access to network especially Internet, the probability of terrible attacks such as deception attacks is greater, therefore presentation of methods which could decrease time delay and increase system immunity are desired. In this paper a symmetric encryption with low data volume against deception attacks is proposed. This method has high security and low time delay rather than the other encryption algorithms and could improve the control system performance against deception attacks.

The algorithm for the assessment of functional work capacity of railway workers with chronic obstructive pulmonary disease (COPD

Directory of Open Access Journals (Sweden)

Okiljević Z.

2014-01-01

Full Text Available The assessment of functional work capacity based on the biological function of the body and a specific job demands and job characteristics, determine whether a person is capable to do the job or group of jobs. Evaluation of work capacity (EWC railway workers is conducted according to the program of Regulations for the former and periodic examinations of employees in workplaces with high risk published in the Official Gazette of RS no. 120/ 07 and 655. Regulations on health conditions to be met by railway workers, who are directly involved in railway transport. One of the most common chronic diseases during EWC is chronic obstructive pulmonary disease (COPD. The definition of contraindications for use of railway employees with COPD given by Ordinance 655 is in very general terms, trying to make it easier and improve the quality of assessment of work capacity, we have developed an algorithm for the assessment of work ability among these workers. When doubt the existence of COPD should first prove that the disease exists, according to GOLD (Global Initiative for Chronic Obstructive Lung Disease guidelines, and for occupational medicine we considered important to clarify and standardize the assessment criteria for EWC, which resulting in a diagnostic algorithm for EWC. It is also important to know which type of job will worker to do. Application of a diagnostic algorithm in EWC will allow optimal assessment of disease severity in railway and other workers suffering of COPD working at the workplace with an increased risk efficacy treatment evaluation; assess compensation of functional defects and determine schedule of periodical examination.

Parameter optimization via cuckoo optimization algorithm of fuzzy controller for energy management of a hybrid power system

International Nuclear Information System (INIS)

Berrazouane, S.; Mohammedi, K.

2014-01-01

Highlights: • Optimized fuzzy logic controller (FLC) for operating a standalone hybrid power system based on cuckoo search algorithm. • Comparison between optimized fuzzy logic controller based on cuckoo search and swarm intelligent. • Loss of power supply probability and levelized energy cost are introduced. - Abstract: This paper presents the development of an optimized fuzzy logic controller (FLC) for operating a standalone hybrid power system based on cuckoo search algorithm. The FLC inputs are batteries state of charge (SOC) and net power flow, FLC outputs are the power rate of batteries, photovoltaic and diesel generator. Data for weekly solar irradiation, ambient temperature and load profile are used to tune the proposed controller by using cuckoo search algorithm. The optimized FLC is able to minimize loss of power supply probability (LPSP), excess energy (EE) and levelized energy cost (LEC). Moreover, the results of CS optimization are better than of particle swarm optimization PSO for fuzzy system controller

An implicit adaptation algorithm for a linear model reference control system

Science.gov (United States)

Mabius, L.; Kaufman, H.

1975-01-01

This paper presents a stable implicit adaptation algorithm for model reference control. The constraints for stability are found using Lyapunov's second method and do not depend on perfect model following between the system and the reference model. Methods are proposed for satisfying these constraints without estimating the parameters on which the constraints depend.

An outlook on robust model predictive control algorithms : Reflections on performance and computational aspects

NARCIS (Netherlands)

Saltik, M.B.; Özkan, L.; Ludlage, J.H.A.; Weiland, S.; Van den Hof, P.M.J.

2018-01-01

In this paper, we discuss the model predictive control algorithms that are tailored for uncertain systems. Robustness notions with respect to both deterministic (or set based) and stochastic uncertainties are discussed and contributions are reviewed in the model predictive control literature. We

Automatic Tuning of PID Controller for a 1-D Levitation System Using a Genetic Algorithm

DEFF Research Database (Denmark)

Yang, Zhenyu; Pedersen, Gerulf K.m.

2006-01-01

The automatic PID control design for a onedimensional magnetic levitation system is investigated. The PID controller is automatically tuned using the non-dominated sorting genetic algorithm (NSGA-II) based on a nonlinear system model. The developed controller is digitally implemented and tested...

FFTF Work Control Center

International Nuclear Information System (INIS)

Talbot, M.D.

1986-01-01

A centralized Work Control Center (WCC) is responsible for assuring that maintenance and modification of the Fast Flux Test Facility (FFTF) is performed in accordance with written procedures that ensure design integrity, personnel and public safety, and equipment and system availability for the computerized Master Information Data Acquisition System (MIDAS). Each maintenance task is logged into MIDAS from a Work Request from that has been reviewed and prioritized by the WCC. Thereafter, MIDAS is used to track schedule, manpower and material requirements; authorize field work; and close out the maintenance activity

A modified P&O MPPT algorithm for single-phase PV systems based on deadbeat control

DEFF Research Database (Denmark)

Yang, Yongheng; Blaabjerg, Frede

2012-01-01

A modified perturb and observe (P&O) algorithm is presented to improve maximum power point tracking (MPPT) performance of photovoltaic (PV) systems. This modified algorithm is applied to a single-phase PV system based on deadbeat control in order to test the tracking accuracy and its impact...... on the reliability of the whole system. Both simulations and experimental results show that the proposed algorithm offers a fast response as well as smaller steady-state oscillations even under low irradiance condition compared with classical methods....

A numerically stable, finite memory, fast array recursive least squares algorithm for broadband active noise control

NARCIS (Netherlands)

van Ophem, S.; Berkhoff, Arthur P.

2016-01-01

For broadband active noise control applications with a rapidly changing primary path, it is desirable to find algorithms with a rapid convergence, a fast tracking performance, and a low computational cost. Recently, a promising algorithm has been presented, called the fast-array Kalman filter, which

Solution Approach to Automatic Generation Control Problem Using Hybridized Gravitational Search Algorithm Optimized PID and FOPID Controllers