Model-Assisted Online Optimization of Gain-Scheduled PID Control Using NSGA-II Iterative Genetic Algorithm

In the practical control of nonlinear valve systems, PID control, as a model-free method, continues to play crucial role thanks its simple structure and performance-oriented tuning process. To improve performance, advanced gain-scheduling methods are used schedule gains based on operating conditions and/or tracking error. However, determining scheduled gain is major challenge, need be determined at each condition. this paper, model-assisted online optimization method proposed modified Non-Dominated Sorting Genetic Algorithms-II (NSGA-II) obtain optimal gain-scheduled controller. Model-assisted offline through computer-in-the-loop simulation provides initial for an algorithm, which then uses iterative NSGA-II algorithm automatically tune by searching parameter space. As summary, approach presents controller optimized both learning prior model knowledge learning. The demonstrated in case system able with given structure. performance improvement comparing it fixed-gain controllers under multiple conditions.