Fuzzy control optimized by a Multi-Objective Particle Swarm Optimization algorithm for vibration suppression of smart structures

Smart structures include elements of active, passive or hybrid control. In this paper, a new Multi-Objective Particle Swarm Optimization (MOPSO), with a different velocity equation, for the calculation of the free parameters in active control systems is proposed and tested. A fuzzy control system is considered. Fuzzy control is a suitable tool for the systematic development of nonlinear active control strategies and can be fine tuned if no experience exists or if one designs more complicated control schemes. The usage of MOPSO with a combination of continuous and discrete variables for the optimal design of the controller is proposed. Numerical applications on smart piezoelastic beams are presented.

[1]  Georgios E. Stavroulakis,et al.  Design and robust optimal control of smart beams with application on vibrations suppression , 2005, Adv. Eng. Softw..

[2]  Chia-Feng Juang,et al.  Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm , 2008, Fuzzy Sets Syst..

[3]  Russell C. Eberhart,et al.  Particle swarm with extended memory for multiobjective optimization , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[4]  M. Clerc,et al.  Particle Swarm Optimization , 2006 .

[5]  Mehrdad Moallem,et al.  Parameter selection and control design for vibration suppression using piezoelectric transducers , 2004 .

[6]  Hung-Tat Tsui,et al.  Autonomous agent response learning by a multi-species particle swarm optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[7]  Q. Henry Wu,et al.  MCPSO: A multi-swarm cooperative particle swarm optimizer , 2007, Appl. Math. Comput..

[8]  Ben Niu,et al.  A multi-swarm optimizer based fuzzy modeling approach for dynamic systems processing , 2008, Neurocomputing.

[9]  L. Jain,et al.  Evolutionary multiobjective optimization : theoretical advances and applications , 2005 .

[10]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[11]  Chih-Hsun Chou,et al.  Genetic algorithm-based optimal fuzzy controller design in the linguistic space , 2006, IEEE Trans. Fuzzy Syst..

[12]  An-Pei Wang,et al.  VIBRATION CONTROL OF A TALL BUILDING SUBJECTED TO EARTHQUAKE EXCITATION , 2007 .

[13]  Damon G. Reigles,et al.  Supervisory fuzzy control of a base‐isolated benchmark building utilizing a neuro‐fuzzy model of controllable fluid viscous dampers , 2006 .

[14]  Konstantinos E. Parsopoulos,et al.  MULTIOBJECTIVE OPTIMIZATION USING PARALLEL VECTOR EVALUATED PARTICLE SWARM OPTIMIZATION , 2003 .

[15]  Martin C. Cooper Reduction operations in fuzzy or valued constraint satisfaction , 2003, Fuzzy Sets Syst..

[16]  W. Renhart,et al.  Pareto optimality and particle swarm optimization , 2004, IEEE Transactions on Magnetics.

[17]  M Reyes Sierra,et al.  Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art , 2006 .

[18]  Khaled Belarbi,et al.  Genetic algorithm for the design of a class of fuzzy controllers: an alternative approach , 2000, IEEE Trans. Fuzzy Syst..

[19]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[20]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[21]  Daniel Merkle,et al.  A New Multi-objective Particle Swarm Optimization Algorithm Using Clustering Applied to Automated Docking , 2005, Hybrid Metaheuristics.

[22]  Chin-Teng Lin,et al.  Genetic Reinforcement Learning through Symbiotic Evolution for Fuzzy Controller Design , 2022 .

[23]  Jürgen Teich,et al.  Covering Pareto-optimal fronts by subswarms in multi-objective particle swarm optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[24]  Nasser Sadati,et al.  Design of a fractional order PID controller for an AVR using particle swarm optimization , 2009 .

[25]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.

[26]  Lakhmi C. Jain,et al.  Evolutionary Multiobjective Optimization , 2005, Evolutionary Multiobjective Optimization.

[27]  Georgios E. Stavroulakis,et al.  Classical and soft robust active control of smart beams , 2009 .

[28]  Morteza Montazeri,et al.  Design of genetic-fuzzy control strategy for parallel hybrid electric vehicles , 2008 .

[29]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[30]  Jonathan E. Fieldsend,et al.  A Multi-Objective Algorithm based upon Particle Swarm Optimisation, an Efficient Data Structure and , 2002 .

[31]  Hasan A. Yousef,et al.  Adaptive fuzzy APSO based inverse tracking-controller with an application to DC motors , 2009, Expert Syst. Appl..

[32]  Ananth Ramaswamy,et al.  Optimal fuzzy logic control for MDOF structural systems using evolutionary algorithms , 2009, Eng. Appl. Artif. Intell..

[33]  Georgios E. Stavroulakis,et al.  Robust H/sub 2/ vibration control of beams with piezoelectric sensors and actuators , 2003, 2003 IEEE International Workshop on Workload Characterization (IEEE Cat. No.03EX775).

[34]  Xiaodong Li,et al.  A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization , 2003, GECCO.

[35]  Tarunraj Singh,et al.  Distributed Control of Laminated Beams: Timoshenko Theory vs. Euler-Bernoulli Theory , 1997 .

[36]  Roger Ohayon,et al.  PIEZOELECTRIC ACTIVE VIBRATION CONTROL OF DAMPED SANDWICH BEAMS , 2001 .

[37]  Shaikh Faruque Ali,et al.  Hybrid structural control using magnetorheological dampers for base isolated structures , 2009 .

[38]  Chukwudi Anyakoha,et al.  A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications , 2008, Natural Computing.

[39]  Georgios E. Stavroulakis,et al.  Shape control and damage identification of beams using piezoelectric actuation and genetic optimization , 2006 .

[40]  M.N. Vrahatis,et al.  Particle swarm optimizers for Pareto optimization with enhanced archiving techniques , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[41]  Andries Petrus Engelbrecht,et al.  Locating multiple optima using particle swarm optimization , 2007, Appl. Math. Comput..

[42]  O. A. Sebakhy,et al.  Adaptive fuzzy APSO based inverse tracking-controller for DC motors , 2007 .

[43]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[44]  Raghuveer M. Rao,et al.  Darwinian Particle Swarm Optimization , 2005, IICAI.

[45]  Dipti Srinivasan,et al.  Particle Swarm Inspired Evolutionary Algorithm (PS-EA) for Multi-Criteria Optimization Problems , 2003, Evolutionary Multiobjective Optimization.

[46]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[47]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[48]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[49]  M. N. Vrahatis,et al.  Particle swarm optimization method in multiobjective problems , 2002, SAC '02.

[50]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .

[51]  Prospero C. Naval,et al.  An effective use of crowding distance in multiobjective particle swarm optimization , 2005, GECCO '05.

[52]  Cheng-Jian Lin,et al.  A GA-based neural fuzzy system for temperature control , 2004, Fuzzy Sets Syst..

[53]  Chukwudi Anyakoha,et al.  A review of particle swarm optimization. Part I: background and development , 2007, Natural Computing.

[54]  S. Pourzeynali,et al.  Active control of high rise building structures using fuzzy logic and genetic algorithms , 2007 .

[55]  Carlos A. Coello Coello,et al.  Using Clustering Techniques to Improve the Performance of a Multi-objective Particle Swarm Optimizer , 2004, GECCO.

[56]  Shiyou Yang,et al.  A particle swarm optimization-based method for multiobjective design optimizations , 2005, IEEE Transactions on Magnetics.

[57]  Michael Reinfrank,et al.  An introduction to fuzzy control (2nd ed.) , 1996 .