Recursive Estimator-Based Fuzzy Adaptive Control for Discrete-Time Uncertain Systems With State Saturations and Missing Measurements

Abstract

This article studies the recursive state estimator-based fuzzy adaptive control scheme for discrete-time uncertain nonlinear systems with state saturations and missing measurements. A fuzzy extended state Kalman filter is proposed to obtain the estimated states of the system. First, an auxiliary function on the nonlinear rate of change is constructed and approximated using a fuzzy logic system, which reduces the error caused by directly given the upper bound of the autocorrelation function. Subsequently, the real-time gain and upper bounds on the error covariance of the estimator are obtained, and the stability analysis of the estimation algorithm is given. Furthermore, a recursive estimator-based control strategy is developed, where the virtual control function and adaptive law are designed to enhance the performance of the controller. The proposed control method ensures that the closed-loop system signals are bounded and the errors are converged. Finally, the validity of the scheme is demonstrated by illustrative example.