Optimization Study of Nonlinear Fuzzy Control System Based on Accelerated Evolutionary Programming

Xiaoyao Lu

doi:10.54097/

Authors

Xiaoyao Lu

DOI:

https://doi.org/10.54097/

Keywords:

Accelerated evolutionary programming, Nonlinear, Fuzzy control systems, Optimization studies

Abstract

The objective of this study is to enhance the responsiveness and precision of control systems through the acceleration of evolutionary programming in the optimization of nonlinear fuzzy control systems. Nonlinear systems are ubiquitous in engineering practice, presenting significant challenges due to their complexity and uncertainty for traditional control methodologies. Fuzzy control, an intelligent control approach rooted in empirical rules, possesses the capability to navigate uncertain and nonlinear issues. However, conventional fuzzy controllers struggle to achieve optimal control performance when confronted with highly complex nonlinear systems. Consequently, this paper introduces the application of accelerated evolutionary programming (RGA) for the optimization of fuzzy controllers, aiming to augment their performance in nonlinear systems. By merging genetic algorithms with a fuzzy adaptive PID controller, the optimized fuzzy controller is better equipped to adapt to the dynamic changes of nonlinear systems, offering robust and efficient control strategies. The results of simulation experiments demonstrate a marked improvement in response time, stability, and error reduction with the fuzzy control system based on RGA optimization. This study offers novel insights and methodologies for the further optimization of control within nonlinear systems, while providing theoretical support for the practical applications of complex systems.

References

[1] Cabrera J A, Castillo J J, Carabias E, et al. Evolutionary optimization of a motorcycle traction control system based on fuzzy logic[J]. IEEE Transactions on Fuzzy Systems, 2014, 23(5): 1594-1607.

[2] Zeng G Q, **e X Q, Chen M R, et al. Adaptive population extremal optimization-based PID neural network for multivariable nonlinear control systems[J]. Swarm and evolutionary computation, 2019, 44: 320-334.

[3] Lamamra K, Batat F, Mokhtari F. A new technique with improved control quality of nonlinear systems using an optimized fuzzy logic controller[J]. Expert Systems with Applications, 2020, 145: 113148.

[4] Zhao T, Chen C, Cao H. Evolutionary self-organizing fuzzy system using fuzzy-classification-based social learning particle swarm optimization[J]. Information Sciences, 2022, 606: 92-111.

[5] Jeon J Y, Kim J H, Koh K. Experimental evolutionary programming-based high-precision control[J]. IEEE Control Systems Magazine, 1997, 17(2): 66-74.

[6] Luo S, Lewis F L, Song Y, et al. Accelerated adaptive fuzzy optimal control of three coupled fractional-order chaotic electromechanical transducers[J]. IEEE Transactions on Fuzzy Systems, 2020, 29(7): 1701-1714.

[7] Ali S F, Ramaswamy A. Optimal fuzzy logic control for MDOF structural systems using evolutionary algorithms[J]. Engineering Applications of Artificial Intelligence, 2009, 22(3): 407-419.

[8] Hoffmann F. Evolutionary algorithms for fuzzy control system design[J]. Proceedings of the IEEE, 2001, 89(9): 1318-1333.

[9] Nagarkar M P, Bhalerao Y J, Vikhe Patil G J, et al. GA-based multi-objective optimization of active nonlinear quarter car suspension system—PID and fuzzy logic control[J]. International Journal of Mechanical and Materials Engineering, 2018, 13: 1-20.

[10] El-Shorbagy M A, Mousa A A, Nasr S M. A chaos-based evolutionary algorithm for general nonlinear programming problems[J]. Chaos, Solitons & Fractals, 2016, 85: 8-21.