A PSO-based adaptive fuzzy PID-controllers

Simulation Modelling Practice and Theory

Volumn 26, Issue , 2012, Pages 49-59

  Chiou, Juing Shian ^a   Tsai, Shun Hung ^b   Liu, Ming Tang ^a  

^a SOUTHERN TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b NATIONAL TAIPEI UNIVERSITY OF TECHNOLOGY   (Taiwan)

Author keywords

Evolutionary programming (EP); Fuzzy logic controllers (FLCs); Integral of Absolute Error (IAE); Membership functions; Particle swarm optimization (PSO); Proportional integral derivative (PID); Q learning

Indexed keywords

ABSOLUTE ERROR; ACTIVE AUTOMOBILE SUSPENSION; ADAPTIVE FUZZY; COOPERATION METHOD; EVOLUTION TIME; EXISTING PROBLEMS; FUZZY LOGIC CONTROLLERS; FUZZY PID CONTROLLER; FUZZY-PID; GLOBAL EXPLORATION; HIGH DIMENSIONAL SPACES; LOCAL OPTIMUM SOLUTION; NON-LINEARITY; NOVEL ALGORITHM; NOVEL DESIGN; PID-CONTROLLERS; PROPORTIONAL INTEGRAL DERIVATIVES; Q-LEARNING; RIDE COMFORTS; RULE SET; SEARCH PROBLEM; SUSPENSION SYSTEM; TIRE DEFLECTION;

ALGORITHMS; COMPUTER PROGRAMMING; EVOLUTIONARY ALGORITHMS; FUZZY LOGIC; FUZZY SYSTEMS; MEMBERSHIP FUNCTIONS; PARTICLE SWARM OPTIMIZATION (PSO); PROBLEM SOLVING; PROPORTIONAL CONTROL SYSTEMS; THREE TERM CONTROL SYSTEMS;

SUSPENSIONS (COMPONENTS);

EID: 84862564264     PISSN: 1569190X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.simpat.2012.04.001     Document Type: Article

References (24)

1. J. Kennedy, and R.C. Eberhart Particle swarm optimization Proceedings IEEE International Conference on Neural Networks IV 1995 1942 1948
2. J.-S. Chiou, and M.-T. Liu Numerical simulation for fuzzy-PID controllers and helping EP reproduction with PSO hybrid algorithm Simulation Modelling Practice and Theory 17 10 2009 1555 1565
3. M. Clerc, The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization, in: Proceedings of the ICEC 1999, Washington, DC, 1999, pp. 1951-1957.
4. R.C. Eberhart, Y. Shi, Comparing inertia weights and constriction factors in particle swarm optimization, in: Proceedings of the IEEE Congress on Evolutionary Computation, San Diego, USA, 2000, pp. 84-88.
5. L.-L. Li, L. Wang, and L.-L. Liu An effective hybrid PSOSA strategy for optimization and its application to parameter estimation Applied Mathematics and Computation 179 1 2006 135 146
6. M.M. Noel, T.C. Jannett, Simulation of a new hybrid particle swarm optimization algorithm, in: Proc 36th Southeastern Symposium on System Theory, 2004, pp. 150-153.
7. C.F. Juang A hybrid of genetic algorithm and particle swarm optimization for recurrent network design IEEE System, Man, and Cybernetics: B 34 2 2004 997 1006
8. T. Huang, and A.S. Mohan A hybrid boundary condition for robust particle swarm optimization IEEE Antennas and Wireless Propagation Letters 4 2005 112 117
9. D. Liu, K.C. Tan, C.K. Goh, and W.K. Ho A multiobjective memetic algorithm based on particle swarm optimization IEEE System, Man, and Cybernetics: B 37 1 2007 42 50
10. A. Beghi, M. Liberati, S. Mezzalira, and S. Peron Grey-box modeling of a motorcycle shock absorber for virtual prototyping applications Simulation Modelling Practice and Theory 15 8 2007 894 907
11. R. Bellman, and R. Kalaba Dynamic Programming and Modern Control Theory 1965 Academic Press Inc. New York
12. J.-S. Chiou, and K.-Y. Wang Application of a hybrid controller to a mobile robot Simulation Modelling Practice and Theory 16 7 2008 783 795
13. X. Guo, Y. Zhou, Dunwei Gong, Optimization of fuzzy sets of fuzzy control system based on hierarchical genetic algorithms, in: Proceedings of IEEE TENCON'02.
14. İkbal Eski, and Şahin Yildirim Vibration control of vehicle active suspension system using a new robust neural network control system Simulation Modelling Practice and Theory 17 5 2009 778 793
15. K. Gulez, and R. Guclu CBA-neural network control of a non-linear full vehicle model Simulation Modelling Practice and Theory 16 9 2008 1163 1176
16. J. Cao, H. Liu, P. Li, and D.J. Brown State of the art in vehicle active suspension adaptive control systems based on intelligent methodologies IEEE Intelligent Transportation Systems 9 3 2008 392 405
17. Carlos D. Paternina-Arboleda, and Tapas K. Das A multi-agent reinforcement learning approach to obtaining dynamic control policies for stochastic lot scheduling problem Simulation Modelling Practice and Theory 13 5 2005 389 406
18. J.Z. Feng, J. Li, and F. Yu GA-based PID and fuzzy logic control for active vehicle suspension system International Journal of Automotive Technology 4 4 2003 181 191
19. Y.-T. Juang, Y.-T. Chang, and C.-P. Huang Design of fuzzy PID controllers using modified triangular membership functions Information Sciences 178 5 2008 1325 1333
20. Y.-P. Kuo, M.-T. Liu, J.-S. Chiou, Design of an evolutionary-based fuzzy system, in: Asian Simulation Conference, 2006.
21. S.-K. Oh, and W. Pedrycz Genetic optimization-driven multi-layer hybrid fuzzy neural networks Simulation Modelling Practice and Theory 14 5 2006 597 613
22. Y.-P. Kuo, and T.-H. Li GA-based fuzzy PI/PD controller for automotive active suspension system IEEE Industrial Electronics 46 6 1999 1051 1056
23. I. Youn, and A. Hac Preview control of active suspension with integral action International Journal of Automotive Technology 7 5 2006 547 554
24. J.-S. Chiou, and M.-T. Liu Using fuzzy logic controller and evolutionary genetic algorithm for automotive active suspension system International Journal of Automotive Technology 10 6 2009 703 710