Abstract
In this paper, a novel optimal design method for PID controller is proposed based on the ant system (AS) algorithm. In this method, for a given control system with a PID controller, by taking the overshoot, settling time, and steady-state error of unit step response of the system as the performance indexes and using the AS algorithm, the optimal PID controller parameters K p *, T i *, and T d * can be obtained. The proposed method has excellent features, including easy implementation, good convergence property, and efficient tuning of PID controller parameters. The PID controller designed using this method is called the AS-PID controller. In order to verify the good performance of the AS-PID controller, four typical control systems were tested. The simulation results show that the proposed method is indeed adaptive and robust in quick search of the optimal PID controller parameters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ziegler, J.G., Nichols, N.B.: Optimum Setting for Automatic Controllers. ASME Transactions, 759–768 (1942)
Hang, C.C., Astrom, K.J., Ho, W.K.: Refinements of the Ziegler-Nichols Tuning Formula. IEE Proceedings of Control Theory and Applications, Series D 138, 111–118 (1991)
Astrom, K.J., Hagglund, T.: Automatic Tuning of Simple Regulators with Specifications on Phase and Amplitude Margins. Automatica 20(5), 645–651 (1984)
Hagglund, T., Astrom, K.J.: Industrial Adaptive Controllers Based on Frequency Response Techniques. Automatica 27(4), 597–609 (1991)
Ho, W., Hang, C.C., Cao, L.S.: Tuning of PID Controllers Based on Gain and Phase Margin in Specifications. In: Proceedings of the 12th IFAC World Congress, Sydney, Australia, vol. 5, pp. 267–270 (1993)
Colorni, A., Dorigo, M., Maniezzo, V.: Distributed Optimization by Ant Colonies. In: Proceedings of the First Europe Conference on Artificial Life, Paris, France, pp. 134–142 (1991)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B 26(1), 29–41 (1996)
Dorigo, M., Gambardella, L.M.: Ant Colony system: a Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)
Maniezzo, V., Colorni, A.: The Ant System Applied to the Quadratic Assignment Problem. IEEE Transactions on Knowledge Data Engineering 11(5), 769–778 (1999)
Colorni, A., Dorigo, M., Maniezzo, V., Trubian, M.: Ant System for Job-Shop Scheduling. Belgian J. Oper. Res. Statist. Comput. Sci. 34(1), 34–53 (1994)
Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant Algorithms for Discrete Optimization. Artificial Life 5(2), 137–172 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tan, G., Zeng, Q., He, S., Cai, G. (2005). Adaptive and Robust Design for PID Controller Based on Ant System Algorithm. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_113
Download citation
DOI: https://doi.org/10.1007/11539902_113
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
Online ISBN: 978-3-540-31863-7
eBook Packages: Computer ScienceComputer Science (R0)