Abstract
Optimization is the selection process of the best elements with respect to some criterion from a feasible set of variables. There may be single or multiple objectives to be considered during optimization. The optimization process generally involves the minimization of a cost or maximization of a profit. Sliding mode controller design problem ends up with the selection of values for its parameters which may include trials and errors. In this chapter, using intelligent optimization approaches, the parameters of sliding mode controllers are optimized to minimize the effect of chattering while the tracking error is minimized. Since these two objective functions are conflicting objective functions, it is required to use multiobjective intelligent optimization approaches.
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
References
Chen, C.-L., Chang, M.-H.: Optimal design of fuzzy sliding-mode control: a comparative study. Fuzzy Sets Syst. 93(1), 37–48 (1998)
Alfaro-Cid, E., McGookin, E., Murray-Smith, D., Fossen, T.: Genetic algorithms optimisation of decoupled sliding mode controllers: simulated and real results. Control Eng. Pract. 13(6), 739–748 (2005)
Fraser, A.S.: Simulation of genetic systems by automatic digital computers i. introduction. Aust. J. Biolog. Sci. 10(4), 484–491 (1957)
Fraser, A.S.: Simulation of genetic systems by automatic digital computers ii. effects of linkage on rates of advance under selection. Aust. J. Biolog. Sci. 10(4), 492–500 (1957)
Wright, A.H., et al.: Genetic algorithms for real parameter optimization. Found. Gen. Algorithms 1, 205–218 (1991)
Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii. In: International Conference on Parallel Problem Solving from Nature, pp. 849–858. Springer (2000)
Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)
Zitzler, E., Laumanns, M., Thiele, L.: Spea2: Improving the strength pareto evolutionary algorithm. TIK-report, vol. 103 (2001)
Yu, X., Kaynak, O.: Sliding-mode control with soft computing: a survey. IEEE Trans. Ind. Electr. 56(9), 3275–3285 (2009)
Chen, C.-K., Lin, C.-J., Yao, L.-C.: Input-state linearization of a rotary inverted pendulum. Asian J. Control 6(1), 130–135 (2004)
Khanesar, M.A., Teshnehlab, M., Shoorehdeli, M.A.: Sliding mode control of rotary inverted pendulum. In: Mediterranean Conference on Control & Automation, 2007, MED’07, pp. 1–6, IEEE (2007)
Wong, C.-C., Chang, S.-Y.: Parameter selection in the sliding mode control design using genetic algorithms. Tamkang J. Sci. Eng. 1, 115–122 (1998)
Trebi-Ollennu, A., White, B.: Multiobjective fuzzy genetic algorithm optimisation approach to nonlinear control system design. IEE Proceed.-Control Theory Appl. 144(2), 137–142 (1997)
Paraskevopoulos, P.: Modern Control Engineering. CRC Press, New York (2001)
Ryu, S.-H., Park, J.-H.: Auto-tuning of sliding mode control parameters using fuzzy logic. In: American Control Conference, 2001. Proceedings of the 2001, vol. 1, pp. 618–623. IEEE (2001)
Erbatur, K., Kawamura, A.: Chattering elimination via fuzzy boundary layer tuning. In: IECON 02 [Industrial Electronics Society, IEEE: 28th Annual Conference of the], vol. 3, pp. 2131–2136. IEEE (2002)
Eiben, A.E., Smit, S.K.: Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol. Comput. 1(1), 19–31 (2011)
Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173–195 (2000)
Czyzzak, P., Jaszkiewicz, A.: Pareto simulated annealing metaheuristic technique for multiple objective combinatorial optimization. J. Multi-Criteria Decis. Anal. 7(1), 34–47 (1998)
Ulungu, E., Teghem, J., Ost, C.: Efficiency of interactive multi-objective simulated annealing through a case study. J. Oper. Res. Soc. 49(10), 1044–1050 (1998)
Tan, K.C., Goh, C.K., Yang, Y., Lee, T.H.: Evolving better population distribution and exploration in evolutionary multi-objective optimization. Eur. J. Oper. Res. 171(2), 463–495 (2006)
Acknowledgements
The authors would like to acknowledgement Dr. Bibi Elham Fallah Tafti for her contribution in writing the source code for the optimization made in this chapter.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Ahmadieh Khanesar, M., Kaynak, O., Kayacan, E. (2021). Intelligent Optimization of Sliding-Mode Fuzzy Logic Controllers. In: Sliding-Mode Fuzzy Controllers. Studies in Systems, Decision and Control, vol 357. Springer, Cham. https://doi.org/10.1007/978-3-030-69182-0_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-69182-0_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69181-3
Online ISBN: 978-3-030-69182-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)