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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 10    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 10, October 2011 Edition
Control and System Identification via Swarm and Evolutionary Algorithms
Full Text(PDF, 3000)  PP.  
Author(s)
Tayebeh Mostajabi, Javad Poshtan
KEYWORDS
adaptive control; evolutionary algorithm; global minimum, local minima, robotics; swarm intelligence; system identification.
ABSTRACT
A central topic of swarm intelligence is the investigation of different types of emergent collective behaviors in swarms. This article focus on the swarm intelligence applications in control and system identification. Particle swarm optimization (PSO), a novel population based stochastic optimizer with fast convergence speed and simple implementation and genetic algorithm, have been successfully applied to solve system identification optimization problems. In addition, PSO and ant colony optimization (ACO) have been applied as a navigation algorithm in swarm robots. Some of the recently proposed swarm based metaheuristics such as bacterial foraging optimization algorithm (BFOA), wasp optimization algorithm (WOA), bee optimization algorithm (BOA) and Physarum Solver will need further investigation to assess their potential for generating state-of-the-art algorithms that are useful for this area
References
[1] R. L. Haupt, S. E. Haupt., Practical Genetic Algorithms. 2th ed. Wiley, New Jersey and Canada. 2004.

[2] M.Dorigo, M.Birattari, T.St¨utzle ., “Ant Colony Optimization ,”. IEEE Computational Intelligence Magazine, pp.28-39,Nov 2006.

[3] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” Proceedings of IEEE International Conference on Neural Networks, Piscataway,NJ. pp. 1942– 1948, 1995.

[4] K.M. Passino, “Biomimicry of bacterial foraging for distributed optimization and control,”IEEE Control Systems Mag., vol. 22, no. 3, pp.52–67, June 2002.

[5] D.Merkle and M.Middendorf., “Swarm Intelligence and Signal Processing,” IEEE Signal Processing Magazine, November 2008.

[6] Ch.Dai, W.Chen and Y.Zhu., “Seeker Optimization Algorithm for Digital IIR Filter Design,” IEEE Trans Industrial Electronics, VOL. 57, NO. 5, 2009.

[7] E. Atashpaz‐Gargari, C. Lucas, ""Imperialist Competitive Algorithm: An Algoritm for Optimization Inspired by Imperialistic Competition"", IEEE congress on Evolutionary Computation, pp 4661‐ 4667,2007

[8] Ş.İ.Birbil and Sh.Ch.Fang., "" An Electromagnetism-like Mechanism for Global Optimization,"" Journal of Global Optimization, Vol 25, NO 3,pp 263-282, 2003.

[9] LXie, J.Zeng and Zh.Cui., "" The Vector Model of Artificial Physics Optimization Algorithm for Global Optimization Problems,"" Intelligent Data Engineering and Automated Learning - IDEAL 2009. Lecture Notes in Computer Science, Vol 5788/,pp 610-617.

[10] A. Kaveh and S. Talatahari., "" A novel heuristic optimization method: charged system search,"" Acta Mechanica, Vol 213, NO 3-4, pp267-289, 2009 springer.

[11] Ganesh K. Kumar Venayagamoorthy., “A Successful Interdisciplinary Course On Coputational Intelligence,”. IEEE Computational Intelligence Magazine, pp.14-23,Feb 2009.

[12] D. J. Krusienski and W. K. Jenkins, “A particle swarm optimization- LMS hybrid algorithm for adaptive filtering,” Proc. of the 38th Asilomar Conf. on Signals, Systems, and Computers, Nov. 2004.

[13] D. J. Krusienski and W. K. Jenkins, “Design and performance of adaptive systems based on structured stochastic optimization strategies” IEEE Circuits And Systems Magazine, First Quarter 2005.

[14] D. J. Krusienski, “Enhanced structured stochastic global optimization algorithms for IIR and nonlinear adaptive filtering,” Ph.D. Thesis,Dept. of Electrical Eng., The Pennsylvania State University, University Park, PA, 2004.

[15] L.Ljung , System Identification: Theory for The User. Prentice Hall, 1987.

[16] S. Haykin , Adaptive filter theory. 4th ed. Prentice Hall, 2001.

[17] Zh.Chen, Y.Zhong, J.Li , “Parameter Identification of Induction Motors Using Ant Colony Optimization,” (2008) IEEE Congress on Evolutionary Computation.

[18] L.Li, Y.Yang, H.Peng, X.Wang., "" Parameters identification of chaotic systems via chaotic ant swarm,"" Chaos, Solitons and Fractals 28 (2006) 1204–1211.

[19] Y.Tang, M.Cui, L.Li, H.Peng, X.Guan., "" Parameter identification of time-delay chaotic system using chaotic ant swarm,"" Chaos, Solitons and Fractals 41 (2009) 2097–2102.

[20] L.d.S.Coelho and B.M.Herrera., "" Fuzzy Identification Based on a Chaotic Particle Swarm Optimization Approach Applied to a Nonlinear Yo-yo Motion System,"" Ieee Transactions On Industrial Electronics, Vol. 54, No. 6, December 2007.

[21] K. Kristinsson and G. Dumont, “ System identification and control using genetic algorithms, ” IEEE Trans. Syst, Man, Cybernet, vol. 22, no. 5, pp. 1033-1046, 1992.

[22] S. C. Ng, S.H. Leung, C. Y. Chung, A. Luk, and W. H. Lau, “The genetic search approach : A new learning algorithm for adaptive IIR filtering, ” IEEE Signal Processing Magazine, pp .38–46, Nov.1996.

[23] L.Yao and W.A. Sethares, “ Nonlinear Parameter Estimation via the Genetic Algorithm,” IEEE Trans. Signal Processing, vol. 42, april 1994.

[24] ].W. Lennon and K. Passino, “Genetic adaptive identification and control,” Eng. Applicat. Artif. Intell., vol. 12, pp. 185-200, Apr. 1999.

[25] O.Montiel, O.Castillo, R.Sep_ulveda, P.Melin., "" Application of a breeder genetic algorithm for finite impulse filter optimization,"" Information Sciences 161 (2004) 139–158.

[26] W.D.Chang, "" Coefficient Estimation of IIR Filter by a Multiple Crossover Genetic Algorithm,"" Computers and Mathematics with Applications 51 (2006) 1437-1444.

[27] Ch.W.Tsai, Ch.H.Huang, Ch.L.Lin., "" Structure-specified IIR filter and control design using real structured genetic algorithm,"" Applied Soft Computing 9 (2009) 1285–1295.

[28] F.Tong, X.M.Xu, B.L.Luk, K.P.Liu, “Equalization of ultrasonic transducers based on evolutionary algorithm and IIR lattice filter,” Eng. Applicat. Artif. Intell., vol. 21, (2008) 1409– 1415.

[29] K.Theofilatos, G.Beligiannis, S.Likothanassis., ""Combining evolutionary and stochastic gradient techniques for system identification,"" Journal of Computational and Applied Mathematics 227 (2009) 147_160.

[30] D. J. Krusienski and W. K. Jenkins, “Adaptive filtering via particle swarm optimization,” Proc. of the 37th Asilomar Conf. on Signals, Systems, and Computers, pp. 571–575, Nov. 2003

[31] D. J. Krusienski and W. K. Jenkins, “Particle swarm optimization for adaptive IIR filter structures,” Proc. of the 2004 Congress on Evolutionary Computation, pp. 965–970, June 2004

[32] D. J. Krusienski and W. K. Jenkins, “The application of particle swarm optimization to adaptive IIR phase equalization,” Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Montreal, Canada, pp. II-693–II-696, 17–21 May 2004.

[33] D. J. Krusienski., W. K. Jenkins., “Particle Swarm Optimization for Adaptive IIR Filter Structures,” 2004 IEEE

[34] Siddharth Pal, D. J. Krusienski, and W. K. Jenkins., “ Structured Stochastic Optimization Strategies for Problems with Ill-conditioned Error Surfaces,” 2005 IEEE.

[35] W.Fang, J.Sun, and W.Xu.,""A New Mutated Quantum-Behaved Particle Swarm Optimizer for Digital IIR Filter Design,"" EURASIP Journal on Advances in Signal Processing Volume 2009, Article ID 367465, 7 pages.

[36] B.Majhi and G.Panda., "" Identification of IIR systems using comprehensive learning particle swarm optimisation,"" Int. J. Power and Energy Conversion, Vol. 1, No. 1, 2009.

[37] B.Luitel, G.K.Venayagamoorthy., ""Particle swarm optimization with quantum infusion for system identification,"" Engineering Applications of Artificial Intelligence 23 (2010) 635–649.

[38] N. Karaboga, A. Kalinli, D. Karaboga., ""Designing digital IIR filters using ant colony optimisation algorithm,"" Engineering Applications of Artificial Intelligence 17 (2004) 301–309.

[39] G.Panda, P.M.Pradhan, B.Majhi., "" IIR system identification using cat swarm optimization,"" Expert Systems with Applications (2011) in press.

[40] Nurhan Karaboga., “A new design method based on artificial bee colony algorithm for digital IIR filters,” Journal of the Franklin Institute 346 (2009) 328–348.

[41] F.Kang, J.Li, Q.Xu., ""Structural inverse analysis by hybrid simplex artificial bee colony algorithms,"" Computers and Structures 87 (2009) 861–870.

[42] B.Majhi, G. Panda., "" Development of efficient identification scheme for nonlinear dynamic systems using swarm intelligence techniques,"" Expert Systems with Applications 37 (2010) 556–566.

[43] L.Li, Y.Yang, H.Peng., ""Fuzzy system identification via chaotic ant swarm,"" Chaos, Solitons and Fractals 41 (2009) 401–409

[44] B.Majhi, G. Panda., "" Robust identification of nonlinear complex systems using low complexity ANN and particle swarm optimization techniquem"" Expert Systems with Applications 38 (2011) 321–333.

[45] H.Tang, S.Xue, C.Fan., "" Differential evolution strategy for structural system identification,"" Computers and Structures 86 (2008) 2004–2012.

[46] R.Ahmad, H.Jamaluddin, M.A.Hussain., "" Application of memetic algorithm in modelling discrete-time multivariable dynamics systems,"" Mechanical Systems and Signal Processing 22 (2008) 1595–1609.

[47] Ch.H.Liu., Y.Y.Hsu., “Design of a Self-Tuning PI Controller for a STATCOM Using Particle Swarm Optimization,” IEEE Transactions on Industrial Electronics, Feb. 2010.Volume: 57 Issue: 2.702 - 715.

[48] K.D.Sharma, A.Chatterjee, A.Rakshit., "" A Hybrid Approach for Design of Stable Adaptive Fuzzy Controllers Employing Lyapunov Theory and Particle Swarm Optimization,"" IEEE Transactions On Fuzzy Systems, Vol. 17, No. 2, April 2009.

[49] Ch.F.Juang, Ch.H.Hsu., "" Temperature Control by Chip-Implemented Adaptive Recurrent Fuzzy Controller Designed by Evolutionary Algorithm,"" IEEE Transactions On Circuits And Systems: Regular Papers, Vol. 52, No. 11, November 2005

[50] Ch.F.Juang, Ch.M.Lu., "" Ant Colony Optimization Incorporated With Fuzzy Q-Learning for Reinforcement Fuzzy Control,"" IEEE Transactions On Systems, Man, And Cybernetics—Part A: Systems And Humans, Vol. 39, No. 3, May 2009.

[51] Ch.F.Juang, Ch.M.Lu, Ch.Lo, Ch.Y.Wang., "" Ant Colony Optimization Algorithm for Fuzzy Controller Design and Its FPGA Implementation,"" IEEE Transactions On Industrial Electronics, Vol. 55, No. 3, March 2008.

[52] Ch.F.Juang, Ch.H.Hsu., ""Reinforcement Interval Type-2 Fuzzy Controller Design by Online Rule Generation and Q-Value-Aided Ant Colony Optimization,"" (2009) Ieee Transactions On Systems, Man, And Cybernetics—Part B: Cybernetics Volume: 39 Issue: 6, 1528 - 1542.

[53] John S.Bay.,“ Design of the “Army-Ant” Cooperative Lifting Robot ,”. IEEE Robotics & Automation Magazine, March 1995.

[54] Mondada.F, Gambardella.L.M, Floreano.D, Nolfi.S, Deneubourg.J.L, Dorigo. M.,“ The Cooperation of Swarm-Bots ,”. IEEE Robotics & Automation Magazine, June 2005.

[55] L. Smith, G. K. Venayagamoorthy, and P. Holloway., “ Obstacle avoidance in collective robotic search using particle swarm optimization ,” IEEE Swarm Intelligence. Symp. , Indianapolis, May 2006.

[56] M.Dorigo, M.Birattari, Th.St¨utzle ., “A PSO Based Mobile Robot for Odor Source Localization in Dynamic Advection Diffusion with Obstacles Environment ,”. IEEE Computational Intelligence Magazine, pp.37-51,May 2007

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