Equivalent Piecewise Derivative Adaptive Control With Fuzzy Rules Emulated Network and Mitigation of Catastrophic Forgetting Learning

Abstract

This article presents a novel adaptive control approach for a class of unknown discrete-time systems using piecewise derivatives derived from experimentally obtained input-output characteristics of the controlled plant. The control law is formulated using a multi-input fuzzy rules emulated network (MiFREN). The learning law is developed to address the issue of catastrophic forgetting, in alignment with the proposed information matrix. Closed-loop analysis demonstrates convergence of the tracking error and weight parameters under feasible conditions. Validation through experiments with a DC-motor torque control system, alongside comparative controllers, demonstrates the superior tracking performance of the proposed method and its effective mitigation of forgetting during tracking tasks.