Supervised Adaptive Control of Unknown Nonlinear Systems Using Fuzzily Blended Time-Varying Canonical Model

Abstract

In spite of the prosperous literature in adaptive control, application of this promising control strategy has been restricted by the lack of assurance in closed-loop stability. This paper proposes an adaptive control architecture, which is augmented by a supervising controller, to enhance the robustness of an adaptive PID control system in the face of exaggerated variation in system parameters, disturbances, or parameter drift in the adaptation law. Importantly, the supervising controller is designed based on an on-line identified model in a fuzzily blended time-varying canonical form. This model largely simplified the identification process, and the design of both the supervising controller and the adaptation law. Numerical studies of the tracking control of an uncertain Duffing–Holmes system demonstrate the effectiveness of the proposed control strategy.