A filter design for T-S fuzzy systems based on moving horizon estimator with measurement noise

Abstract

In this article, a filter based on moving horizon estimator is proposed with Takagi-Sugeno (T-S) fuzzy controllers for a kind of unknown discrete-time system. The T-S fuzzy control algorithm is employed to handle the unknown system dynamics, thus ensuring the property of input-to-state stability (ISS) of the system, which guarantees the boundedness of all states. Besides, the proposed filter and controller can significantly improve the robustness of the system with external disturbance, even if the disturbance has non-Gaussian characteristics. Finally, the effectiveness of the presented algorithm is demonstrated by simulation examples under two kind of noise situations.

SCSO-MHEF: Sand Cat Swarm Optimization based MHEF for Nonlinear LTI-IoT Sensor Data Enhancement

“…Moving horizon estimating (MHE) is an optimization technique that yields estimates of unknown variables or parameters by utilizing a sequence of measurements taken over time that may contain noise (random changes) and other errors [8,9]. In contrast to deterministic methods, MHE necessitates an iterative strategy that uses solvers for either linear or nonlinear programming to find a solution [10].…”