Novel Method on Using Evolutionary Algorithms for PD Optimal Tuning

Abstract:

This paper presents a method to solve the Evolutionary Algorithm (EA) problems for optimal tuning of the Proportional-Deferential (PD) controller parameters. The major efficiency of the proposed method is the Genetic Algorithm (GA) stuck avoidance as well an appropriate estimation for GA lower and upper bounds. Also by this method for the Particle Swarm Optimization (PSO) methodology the initial choice of the controller parameters can be fulfilled to achieve the acceptable performance accuracies. For both GA and PSO methods, the Linear Quadratic Regulator (LQR) obtained trend is used as the reference for the determination of the aforementioned bounds and initial guess. The presented algorithm was applied to regulate a PD controller for the attitude control of a virtual satellite and also with Hardware-in-the-loop (HIL) reaction wheels. Heavy burden trying and error was eliminated from the PD controller design which can be mentioned as the important merit of the presented study.

• Periodical: Applied Mechanics and Materials (Volumes 110-116)
• Pages: 4977-4984
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.110-116.4977
• Online since: October 2011
• Authors: R.A. Khoshrooz, M.A.D. Vahid, M. Mirshams, M.R. Homaeinezhad, A.H. Ahadi
• Keywords: Attitude Control, Genetic Algorithm (GA), Hardware in the Loop (HIL), Optimal Parameter Tuning, Particle Swarm Optimization Algorithm (PSO), PD Controller

References

[1] Back, T., Fogel, D. and Michalewicz, Z. (eds. ), Handbook of Evolutionary Computation, Institute of Physics Pub. And Oxford Univ. Press, (1997).

[2] Elbetagi, E., Hegazy, T., and Grierson, D., Comparison among five evolutionary-based optimization algorithms, Advanced Engineering Information, 19, PP 43-53(2005).

DOI: 10.1016/j.aei.2005.01.004

[3] Goldberg, D. (1989). Genetic algorithms in searching optimization and Machine learning, Reading, MA: Addison-Wesley.

[4] Holland, J. H. (1992). Adaptation in natural and artificial systems, Cambridge, MA: MIT Press.

[5] Kennedy, J., and Eberhart, R., Particle swarm optimization, proc. Of the IEEE Int. Conf. on Neural Networks, Piscataway, NJ, PP 1942-1948(1995).

[6] Wang, Y. P., Watson, R. N, Chong, H. H, Modified genetic algorithm approach to design of an optimal PID controller for AC-DC transmission systems, Electrical Power and Energy Systems 24 (2002) 59-69.

DOI: 10.1016/s0142-0615(01)00006-0

[7] Chang, W. D, Nonlinear system identification and control using a real-coded genetic algorithm, Applied Mathematical Modeling, 31 (2007) 541–550.

DOI: 10.1016/j.apm.2005.11.024

[8] Kao, Ch. Ch, Chaung, Ch. W, Fung, R. F, The self-tuning PID control in a slider–crank mechanism system by applying particle swarm optimization approach.

DOI: 10.1016/j.mechatronics.2006.03.007

[9] Mukherjee, V., Ghoshal, S. P, Intelligent particle swarm optimized fuzzy PID controller for AVR system, Electric Power Systems Research 77 (2007) 1689–1698.

DOI: 10.1016/j.epsr.2006.12.004

[10] Astrom, K. J., and Hagglund, T., PID Controllers: Theory, Design, and Tuning, ISA, Research Triangle Park, NC, (1995).

[11] Al-Saggaf, U. M., An approximation technique to tune PID controllers, Computers Elect. Engng Vol. 17, No. 4, pp.313-320, (1991).

DOI: 10.1016/0045-7906(91)90015-r

[12] Yuhui Shi, Particle Swarm Optimization, IEEE neural network society, 2004, Feature Article(cont. ), 8-9.

[13] K. Ogata, Modern Control Engineering, Prentice Hall, 3rd edition, (1997).

[14] J. C Basilio and S. R Matos, Design of PI and PID Controllers With Transient Performance Specifications, IEEE transaction on education, vol 45, No 4, November (2002).

DOI: 10.1109/te.2002.804399

[15] Topland, M. P., Nonlinear Attitude control of the Micro-Satellite ESEO", Master, s thesis, Department of Engineering Cybermetics, Norweigian University of Science and Technology, Trondheim, Norway, (2004).