Abstract

Proportional integral derivative (PID) controllers have been used for industrial processes for long, and PID tuning has been a field of active research for a long time. An interactive, evolution, particle swarm optimization (IEPSO) algorithm was proposed based on linear weight decrease particle swarm optimization (LWDPSO) and stochastic particle swarm optimization (SPSO). The particle swarm was divided into two groups that is standard PSO and SPSO employed for global search and local search respectively. Parallel variables were dynamically adapted according to the evolution stage. The simulations proved that the IEPSO had better performance than LWDPSO and SPSO-PID controller tuning test proved IEPSO had the better control effect than Ziegler-Nichols,LWDPSO and SPSO.

Keyword

IEPSO, PSO, PID, Fitness, Tuning, Punitive measures.

Cite this article

Refference

[1][1]Bennett S. A history of control engineering, 1930-1955. IET; 1993.

[2][2]Bennett S. Nicholas minorsky and the automatic steering of ships. IEEE Control Systems Magazine. 1984; 4(4):10-5.

[3][3]Araki M. PID control:control systems, robotics and automation: system analysis and control: classical approaches II, Unbehauen, H.(Ed.). EOLSS Publishers Co. Ltd., Oxford, UK; 2009.

[4][4]Åström KJ, Hägglund T. PID controllers: theory, design, and tuning. Research Triangle Park, NC: ISA; 1995.

[5][5]Miller RK, Michel AN, Farrell JA. Quantizer effects on steady-state error specifications of digital feedback control systems. IEEE Transactions on Automatic Control. 1989; 34(6):651-4.

[6][6]Visioli A. Tuning of PID controllers with fuzzy logic. IEE Proceedings-Control Theory and Applications. 2001; 148(1):1-8.

[7][7]Seng TL, Khalid MB, Yusof R. Tuning of a neuro-fuzzy controller by genetic algorithm. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 1999; 29(2):226-36.

[8][8]Mitsukura Y, Yamamoto T, Kaneda M. A design of self-tuning PID controllers using a genetic algorithm. In proceedings of the American control conference 1999 (pp. 1361-5). IEEE.

[9][9]Krohling RA, Rey JP. Design of optimal disturbance rejection PID controllers using genetic algorithms. IEEE Transactions on Evolutionary Computation. 2001; 5(1):78-82.

[10][10]Wang L. Application of adaptive genetic algorithms in PID controller design. Journal of Xian University of Science and Technology. 2005; 25(1):93-5.

[11][11]Xiaowei W, Liang YX, Zhaoping L, Xiangmin J. Study on parameter tuning of fin stall PID controller based on PSO algorithm. Journal of Computer Control Technology and Applications. 2016; 23(8): 1197-202.

[12][12]Heng L, Xing G,Wei L. PID controller parameter optimization based on improved glowworm swarm optimization. Computer Applications and Software. 2017; 34(7):227-30.

[13][13]Zigeler JG, Nichols NB. Optimization setting for automatic controller. Transactions of the ASME. 1942; 64(11):756-69.

[14][14]Cohen G, Coon G. Theoretical consideration of retarded control. Transactions of ASME. 1953; 75:827-34.

[15][15]Zhi-cheng XU. Parameter tuning method of robust PID controller based on particle swarm optimization algorithm [J]. Control and Instruments in Chemical Industry. 2006; 33(5):22-5.

[16][16]J Kennedy, R Eberhart. Particle swarm optimization. Proceedings of the IEEE international conference on neural networks 1995 (pp.1942-8). IEEE.

[17][17]Kim DH. Tuning of PID controller using gain/phase margin and immune algorithm. In proceedings of the soft computing in industrial applications 2005 (pp. 69-74). IEEE.

[18][18]Kim DH, Cho JH. Intelligent tuning of PID controller with disturbance function using immune algorithm. In annual meeting of the fuzzy information processing NAFIPS 2004 (pp. 286-91). IEEE.

[19][19]Kim DH, Hong WP, Park JI. Auto-tuning of reference model based PID controller using immune algorithm. In proceedings of the congress on evolutionary computation 2002 (pp. 483-8). IEEE.

[20][20]Dorigo M, Di Caro G. Ant colony optimization: a new meta-heuristic. In proceedings of the congress on evolutionary computation 1999 (pp. 1470-7). IEEE.

[21][21]Dorigo M, Di Caro G, Gambardella LM. Ant algorithms for discrete optimization. Artificial Life. 1999; 5(2):137-72.

[22][22]Chiha I, Borne P. Multi-Objective ant colony optimization to tuning PID controller. Proceedings of the International Journal of Engineering. 2010.

[23][23]Pham DT, Ghanbarzadeh A, Koc E, Otri S, Rahim S, Zaidi M. The bees algorithm-a novel tool for complex optimisation. In I PROMS virtual international conference on intelligent production machines and systems 2006 (pp.454-9).

[24][24]Jones KO, Bouffet A. Comparison of bees algorithm, ant colony optimization and particle swarm optimization for PID controller tuning. In proceedings of the international conference on computer systems and technologies and workshop for PhD students in computing 2008. ACM.

[25][25]Shi Y, Eberhart R. A modified particle swarm optimizer. In evolutionary computation proceedings. In international conference on World congress on computational intelligence. 1998 (pp. 69-73). IEEE.

[26][26]Zeng JC, Cui ZH. A guaranteed global convergence particle swarm optimizer. Journal of Computer Research and Development. 2004; 8:1333-8.

[27][27]Long W, Liang Xm, Xiao Jh, Yan G. Dynamic hierarchical hybrid particle swarm optimization algorithm [J]. Control and Decision. 2009; 10:015.

[28][28]Clerc M, Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation. 2002; 6(1):58-73.

[29][29]Tiankun W. Fuzzy Self-tuning PID control of main steam temperature in thermal power boilers based on UMDA. Journal of Chinese Society of Power Engineering. 2017; 37(7): 546-51.

[30][30]Shaowei F, Yunsheng Y, Jing Z. Research on control strategy of giant magnetostrictive actuator based on genetic algorithm and PID tuning. Journal of Naval University of Engineering. 2017; 29(3): 22-5.

[31][31]Kai X. Modular mobile robot PID tuning feedback self-tuning control. Machinery Design & Manufacture. 2017:230-3.

[32][32]Lei L, Guobao Z, Yongming H. PID parameter tuning based on bat algorithm. Control Engineering of China. 2017; 24(3):548-53.

[33][33]Ling M, Jie L. PID parameters optimization with improved cooperative coevolution algorithm. Computer Technology and Development. 2017; 27(8): 37-42.

[34][34]Sen R, Fei JX, Wei L. Three phase dynamic voltage restorer based on RBF_PID control. Journal of Electrical Engineering. 2017; 12(8):28-33.