Noniterative Data-Driven Gain-Scheduled Controller Design Based on Fictitious Reference Signal

This paper proposes a noniterative direct data-driven gain-scheduled control. Gain-scheduled proportional–integral–derivative (PID) control is one of the most popular approaches for nonlinear systems. However, compared to fixed PID, gain-scheduled PID has considerably more scheduler parameters and requires time to tune them, which is the problem in conventional methods that include hand-tuning and model-based control design. To solve these problems, a noniterative data-driven tuning method for a gain-scheduled controller with polynomials via tuning approach using a fictitious reference signal is proposed. The proposed method enables tuning of the parameters from one-shot data obtained from a test without the system model to be controlled. To verify this method, the numerical simulation for two types of nonlinear systems is conducted. Therefore, the proposed method enables the parameters of the gain-scheduled controller to realize high tracking performance.