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Fuzzy adaptive control for a class of nonlinear systems with unknown control gain

Fuzzy adaptive control for a class of nonlinear systems with...

引用

ieee symposium on computational intelligence in control and automation, cica

作者： Salim Labiod Thierry Marie Guerra Faculty of Science and Engineering University of Jijel Jijel Algeria LAMIH UMR CNRS 8530 Université de Valenciennes Valenciennes France

This paper deals with direct adaptive control using fuzzy systems of a class of uncertain SISO nonlinear systems with unknown control gain sign. Within this scheme, a fuzzy system is used to generate directly the control input signal without dynamic system estimation, and the Nussbaum-type function is used to deal with the unknown control gain sign. The stability of the closed-loop system is performed using a Lyapunov approach. Simulation results are provided to verify the effectiveness of the proposed design.

关键词： Fuzzy systems Adaptive control Nonlinear systems Trajectory Simulation

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Infinite horizon LQ optimal output tracking from development to real flight tests

Infinite horizon LQ optimal output tracking from development...

引用

12th ieee International symposium on computational intelligence and Informatics, CINTI 2011

作者： Bauer, Péter Bokor, József Computer and Automation Research Institute Hungarian Academy of Sciences Budapest Hungary

ISBN: (纸本)9781457700453

This paper introduces a new LQ optimal, infinite horizon output tracking solution. It publishes results from problem formulation to real flight tests. After a short literature review, it rigorously derives the problem solution and states all of the properties of this new control method. Then it introduces the lateral-directional aircraft model on which the algorithm was tuned and tested. The LQ optimal algorithm was compared by a baseline PID control solution in several simulations and real flight tests. Finally, the pros and cons are summarized and the future direction of development is mentioned. © 2011 ieee.

关键词： Three term control systems

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Optimization of engine speed neural network PID controller based on genetic algorithm

Optimization of engine speed neural network PID controller b...

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2011 4th International symposium on computational intelligence and Design, ISCID 2011

作者： Cao, Hua-Yun Peng, Fu-Ming School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9780769545004

Engine-Dynamometer system is a two-input, dual output system with nonlinear, time-varying characteristics of large inertia, and exists coupling within the system input and output. Using the traditional PID controller, the control is often difficult to achieve the desired effect. In addition, at the production site, because of being cumbersome and precision tuning effects, the traditional PID parameter tuning methods can lead to poor control of engine speed. In this paper, an engine speed neural network PID controller based on genetic algorithm which use genetic algorithm to optimizate three control parameters of neural network PID and achieving the system input and output decoupling control is studied. Simulation results show that the genetic algorithm optimizating engine speed neural network PID control system can effectively improve the accuracy, enhance stability and fast of the system, and also have increased the engine speed control effect. © 2011 ieee.

关键词： Three term control systems

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Effects of parameter values and noise on PSO-based predictive control: An empirical study

Effects of parameter values and noise on PSO-based predictiv...

引用

ieee symposium on computational intelligence in control and automation, cica

作者： Muhammad S. Yousuf Hussain N. Al-Duwaish Department of Electrical Engineering King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

In this paper, a Particle Swarm Optimization (PSO) based Model Predictive control (MPC) scheme is studied through a variety of tests to better understand its behavior and characteristics. The technique has already been presented in the literature. Here, the PSO and MPC parameters are varied to study the effects on the quality of control and system dynamics. Model mismatch and noise are also introduced to test the controller performance. The results from various tests are compared and conclusions are drawn.

关键词： Noise Process control Cost function Time frequency analysis Automatic generation control Optimal control Particle swarm optimization

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Evidence based decision analysis and support

Evidence based decision analysis and support

引用

ieee symposium on computational intelligence in control and automation, cica

作者： Jian-Bo Yang Dong-Ling Xu Manchester Business School University of Manchester Institute of Science and Technology Manchester UK

The evidential reasoning (ER) approach was developed to support multiple criteria decision analysis (MCDA). It is based on the Dampster's combination rule for criteria aggregation and belief function for treating ignorance. In the original ER approach, however, alternative ranking depends on the accurate estimation of a value function, which may be difficult in certain decision environments. In this paper, the link and difference between the ER algorithm and Dampster's combination rule are analysed first. A new alternative ranking method is then investigated as an integrated part of the enhanced ER approach.

关键词： Erbium Equations Mathematical model Manganese Cognition Artificial intelligence Upper bound

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Extensions in Symmetrical Optimum design method. Advantages, applications and perspectives

Extensions in Symmetrical Optimum design method. Advantages,...

引用

6th ieee International symposium on Applied computational intelligence and Informatics, SACI 2011

作者： Preitl, Stefan Precup, Radu-Emil Stinean, Alexandra-Iulia Dragos, Claudia-Adina Radac, Mircea-Bogdan Dept. of Automation and Applied Informatics Politehnica University of Timisoara Timisoara Romania

ISBN: (纸本)9781424491094

The paper deals with theoretical and applicative aspects concerning two extensions of the Symmetrical Optimum method (SO-m) for controller tuning in speed and position control of mechatronics systems. The methods, introduced by the authors and called Extended form of the Symmetrical Optimum method (ESO-m) and Double parameterization of the Symmetrical Optimum method (2p-SO-m), are briefly presented and some approaches regarding the methods are highlighted. Due to the applicability of the methods in the field of mechatronics applications (speed and positioning control of rapid plants), the presented aspects are of permanent actuality. © 2011 ieee.

关键词： Position control

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Adaptive Unified Predictive control applied to first-order-plus-dead-time processes to eliminate unknown class of deterministic disturbance

Adaptive Unified Predictive Control applied to first-order-p...

引用

ieee symposium on computational intelligence in control and automation, cica

作者： Shu-Kai Lin An-Chen Lee Department of Mechanical Engineering National Chiao Tung University Hsinchu Taiwan

A novel Adaptive Unified Predictive control method (Adaptive-UPC) is proposed and applied to the first-order-plus-dead-time (FOPDT) process models with model uncertainties under unknown deterministic disturbances. Based on Indirect Self-tuning Regulator, this new control structure is constructed by combining UPC controller, Recursive Least-Squares Estimation with forgetting factor (RLS with forgetting factor) and Variable Regression Estimation (VRE). The simulation results show that the method can be effectively applied to first-order-plus-dead-time (FOPDT) process models with uncertain process parameters (gain, time constant and dead-time) and unknown deterministic disturbance.

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Experimental determination of an optimal control law of a small windgenerator

Experimental determination of an optimal control law of a sm...

引用

6th ieee International symposium on Applied computational intelligence and Informatics, SACI 2011

作者： Boraci, Radu Koch-Ciobotaru, Cosmin Prostean, Octavian Budisan, Nicolae Politehnica University Department of Automation and Applied Informatics Timisoara Romania

ISBN: (纸本)9781424491094

The paper presents a study for determination the optimal control law for a small horizontal axis windgenerator. Experimentally, using a mixed physical-analog laboratory model, were determined the state variables, corresponding to different wind speed values, for the optimum regimes of the wind turbine, based on the turbine theoretical estimated mathematical model. From a large multitude of possible optimum control laws, the relation between the current and the voltage of the DC-DC converter was considered for control law. This relation - control law, was expressed by a regression function, using the experimental obtained DC-DC converter (HDC from Fig.1) current-voltage pairs VHDC, IHDC. The obtained control law was implemented and verified on Matlab/Simulink model considering different wind speed variations. The simulation results confirm the opportunity end the quality of the adopted optimal control law. © 2011 ieee.

关键词： Wind turbines

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Intelligent predictive control methods for synchronous power system

Intelligent predictive control methods for synchronous power...

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ieee symposium on computational intelligence in control and automation, cica

作者： Muhammad S. Yousuf S.Z. Rizvi Hussain N. Al-Duwaish Electrical Engineering Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

In this paper, an intelligent Model Predictive controller (MPC) for a Synchronous Power Machine on Infinite Bus (SMIB) is proposed. Owing to the nonlinear and multi-variable nature of the SMIB system, calculating optimal control signals can be difficult. To solve this problem, a novel scheme of predictive controller in tandem with heuristic optimization algorithms is proposed. Numerical simulations are carried out and performance of the controller under different conditions and in combination with different optimizers is analysed in detail. Comparison is made with the performance of existing SMIB controllers present in the literature and improvements are observed.

关键词： Predictive models Equations Mathematical model Genetic algorithms Optimization Power system stability

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Accelerating the Artificial Bee Colony Algorithm by Hardware Parallel Implementations

Accelerating the Artificial Bee Colony Algorithm by Hardware...

引用

ieee Latin American symposium on Circuits and Systems

作者： Daniel M. Mufioz Carlos H. Llanos Leandro dos S. Coelho Mauricio Ayala-Rincon Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Parana Department of Computer Science and Mathematics University of Brasilia

ISBN: (纸本)9781467312073

Artificial bee colony (ABC) is an optimization algorithm inspired on the intelligent behavior of honey bee swarms. It is suitable to be applied when mathematical techniques are impractical or provide suboptimal solutions. As a population-based algorithm, the ABC suffers on large execution times specifically for embedded optimization problems with computational limitations. For that we propose a hardware parallel architecture of the opposition-based ABC algorithm (HPOABC) that facilitates the implementation in Field Programmable Gate Arrays (FPGAs). Numerical simulations using four well-known benchmark problems demonstrate that the opposition-based approach allows the algorithm to improve its functionality, preserving the swarm diversity. Additionally, synthesis results point outs that the HPOABC architecture is effectively mapped in hardware and is suitable for embedded applications.

关键词： Global optimization Swarm intelligence Artificial bee colony FPGAs Floating-point arithmetic

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