Robust Fine Tuning of Optimal PID Controller With Guaranteed Robustness

Abstract

The proportional-integral-derivative (PID) controller is a widely used controller in automation industries. Several advanced PID tuning/design methods, such as response-based design, internal model control, and controller optimization by stochastic algorithms, have been proposed in the literature. However, regardless of the advantages and accuracy of developed tuning methods, due to the process modeling error and parametric uncertainties, the experimental response always differs from the theoretical response, which required online fine-tuning of the PID parameter. Manual adjustment of three PID parameters disturbs the robustness of the controller from its desired value; also, it does not guarantee the stability of the process. Therefore, this paper focuses on online PID controller tuning with the guaranteed robustness of the controller. A new single variable tuning method is developed for the online robustness and performance adjustment. Moreover, the proposed rules only depend upon the previously optimized PID parameters. The proposed method is an additional feature to all existing PID tuning methods, including optimal PID controller with stochastic optimization algorithms. The proposed method is validated with the help of optimal PID controller design by existing optimal tuning rules, optimized PID with particle swarm optimization and experimental validation on the canonical tank system.