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

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

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Published

28-09-2024

Issue

Section

Articles

How to Cite

Lu, X. (2024). Optimization Study of Nonlinear Fuzzy Control System Based on Accelerated Evolutionary Programming. Journal of Computing and Electronic Information Management, 14(2), 10-15. https://doi.org/10.54097/