Recurrent Polynomial-Based FBLS for Adaptive Predictive PID Control of Nonlinear Discrete-Time Systems: Comparative Studies on Control Performance and Time Complexity

This paper proposes an adaptive predictive PID control approach using recurrent polynomial-based fuzzy broad learning systems (RP-FBLS) for nonlinear discrete-time dynamic systems. The RP-FBLS combines polynomial-based fuzzy logics, broad learning systems, and recurrent networks to enhance modeling capabilities and adaptive control performance. The RP-FBLS-based adaptive predictive PID controller is evaluated through three simulation case studies on two renowned discrete-time dynamic systems and one experimental study on a heating oven in semiconductor manufacturing. The comparative studies analyze the time complexity and control performance of the proposed approach over existing methods in the aspects of setpoint tracking, disturbance rejection, and robustness. The paper also quantifies the computational effort per sampling instance by considering real-time constraints and highlighting suitability for embedded systems. Simulation results demonstrate the RP-FBLS-based PID controller's efficacy in handling nonlinear dynamics while offering favorable computational performance characteristics. Experimental validations using the heating oven are provided for illustration of the practicability of the proposed method requiring computational efficiency and memory optimization.