Recursive Estimator-Based Fuzzy Adaptive Control for Discrete-Time Uncertain Systems With State Saturations and Missing Measurements
Year: 2024
Type: article
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.
Authors: Weiguo Shi, Jiapeng Liu, Hak-Keung Lam, Jinpeng Yu
Institutions: Qingdao University, King's College London
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G6421470455, G7534966998
OpenAlex ID: W4404238082