Abstract
A design for a model-free learning adaptive control (MFLAC) based on pseudo-gradient concepts and optimization procedure by particle swarm optimization (PSO) is presented in this paper. PSO is a method for optimizing hard numerical functions on metaphor of social behavior of flocks of birds and schools of fish. A swarm consists of individuals, called particles, which change their positions over time. Each particle represents a potential solution to the problem. In a PSO system, particles fly around in a multi-dimensional search space. During its flight each particle adjusts its position according to its own experience and the experience of its neighboring particles, making use of the best position encountered by itself and its neighbors. The performance of each particle is measured according to a pre-defined fitness function, which is related to the problem being solved. The PSO has been found to be robust and fast in solving non-linear, non-differentiable, multi-modal problems. Motivation for application of PSO approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the plant has different gains for the operational range when designed by trial-and-error by user. Numerical results of the MFLAC with particle swarm optimization for a nonlinear control valve are showed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Karray, F., Gueaieb, W., Al-Sharhan, S.: The hierarchical expert tuning of pid controllers using tools of soft computing. IEEE Transactions on Systems, Man, and Cybernetics – Part B 32(1), 77–90 (2002)
Åström, K.J., Hägglund, T.: PID controllers: theory, design, and tuning. Instrument Society of America, ISA (1995)
Bisowarno, B.H., Tian, Y.-C., Tade, M.O.: Model gain scheduling control of an ethyl tert-butyl ether reactive distillation column. Ind. Eng. Chem. Res. 42, 3584–3591 (2003)
Hou, Z., Huang, W.: The model-free learning adaptive control of a class of siso nonlinear systems. In: Proceedings of the American Control Conference, Albuquerque, NM, pp. 343–344 (1997)
Hou, Z., Han, C., Huang, W.: The model-free learning adaptive control of a class of MISO nonlinear discrete-time systems. In: IFAC Low Cost Automation, Shenyang, P.R. China, pp. 227–232 (1998)
Kennedy, J.F., Eberhart, R.C., Shi, R.C.: Swarm intelligence. Morgan Kaufmann, San Francisco (2001)
Shi, Y., Eberhart, R.C.: Parameter selection in PSO optimization. In: Proceedings of the 7th Annual Conf. Evolutionary Programming, San Diego, CA, USA, pp. 25–27 (1998)
Yasuda, K., Ide, A., Iwasaki, N.: Adaptive particle swarm optimization. In: Proceedings of IEEE Int. Conf. on Systems, Man and Cybernetics, vol. 2, Washington, DC, USA, pp. 1554–1559. IEEE Computer Society Press, Los Alamitos (2003)
Devicharan, D., Mohan, C.K.: Particle swarm optimization with adaptive linkage learning. In: Proceedings of the IEEE Congress on Evol. Computation, Portland, OR, USA, pp. 530–535. IEEE Computer Society Press, Los Alamitos (2004)
Mendes, R., Kennedy, J.F.: The fully informed particle swarm: simper, maybe better. IEEE Transactions on Evolutionary Computation 8(3), 204–210 (2004)
Krohling, R.A., Hoffmann, F., Coelho, L.S.: Co-evolutionary particle swarm optimization for min-max problems using Gaussian distribution. In: Proceedings of Congress on Evolutionary Computation, Portland, USA, pp. 959–964 (2004)
Kennedy, J.F., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948. IEEE Computer Society Press, Los Alamitos (1995)
Eberhart, R.C., Kennedy, J.F.: A new optimizer using particle swarm theory. In: Proceedings of International Symposium on Micro Machine and Human Science, Japan, pp. 39–43 (1995)
Ratnaweera, A., Halgamuge, S.K., Watson, H.C.: Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation 8(3), 240–255 (2004)
Wigren, T.: Recursive prediction error identification using the nonlinear Wiener model. Automatica 29(4), 1011–1025 (1993)
Spall, J.C., Cristion, J.A.: Model-free control of nonlinear systems with discrete time measurements. IEEE Transactions on Automatic Control 43, 1198–1210 (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
dos Santos Coelho, L., Guerra, F.A. (2007). Applying Particle Swarm Optimization to Adaptive Controller. In: Saad, A., Dahal, K., Sarfraz, M., Roy, R. (eds) Soft Computing in Industrial Applications. Advances in Soft Computing, vol 39. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70706-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-70706-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70704-2
Online ISBN: 978-3-540-70706-6
eBook Packages: EngineeringEngineering (R0)