Genetic Design of Fuzzy Mapped PID Controllers for Non-linear Plants

The optimization of PID controllers for Continuous Stirred Tank Reactors (CSTRs) is critical for ensuring stable and efficient chemical processing under varying operational conditions and external disturbances. This study presents a novel approach that integrates advanced tuning techniques, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and their hybrid combinations with a Machine Learning (ML) surrogate model, to improve PID controller performance in disturbed environments. A unique evolutionary framework is employed, where populations of both controllers and plants are co-evolved to handle the most challenging plant models. An adversarial testing approach is utilized to evaluate the best-tuned controller against the four most difficult CSTR plants, with disturbances such as feed concentration and temperature fluctuations. The results demonstrate that both GA and PSO, when enhanced with the ML surrogate model, effectively tune PID controllers to manage disturbances, with the GA tuned controller achieving faster convergence and the PSO tuned controller showing greater robustness. Additionally, the hybrid ML surrogate model significantly improved control performance and disturbance rejection. The findings highlight the ability of the ML_GA and ML_PSO controllers to maintain stability and accuracy across a range of challenging conditions, providing a robust solution for optimizing control systems in nonlinear dynamic environments. This research contributes to the field of process control, showcasing the potential of combining evolutionary algorithms with machine learning surrogate models for adaptive, resilient control strategies in complex chemical systems.

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Genetic Design of Fuzzy Mapped PID Controllers for Non-linear Plants

Cited by 2 publications

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Bio-Network Inspired Cooperative Intelligent Controllers

Bio-Network Inspired Cooperative Intelligent Controllers

Enhancing PID Control Robustness in CSTRs: A Hybrid Approach to Tuning Under External Disturbances with GA, PSO, and Machine Learning

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