Anti‐disturbance adaptive sampled‐data observers for a class of nonlinear systems with unknown hysteresis. (15th February 2021)

• Record Type: Journal Article
• Authors: Li, Pengpeng; Shen, Yanjun
• Is Part Of: International journal of robust and nonlinear control. Volume 31:Number 8(2021)
• Page Start: 3212
• Page End: 3229
• Publication Date: 2021-02-15
• Subjects: adaptive sampled‐data observer -- DE -- PI hysteresis -- RBFNN -- UUB; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.5449
• Languages: English
• ISSNs: 1049-8923

Abstract

Abstract: In this article, based on radial basis function neural network (RBFNN) and disturbance estimator (DE), an adaptive sampled‐data observer design scheme is proposed for a class of nonlinear systems with unknown Prandtl–Ishlinskii (PI) hysteresis and unknown multiple disturbances. To begin with, we investigate a class of sampled‐data nonlinear systems and present corresponding sufficient conditions ensuring ultimate uniform boundedness (UUB). Subsequently, a sampled‐data observer and a DE are designed to estimate the unknown states and compounded disturbances, respectively. Additionally, the unknown hysteresis and the unknown unmatched disturbances are approximated by RBFNNs. Meanwhile, we also give the learning laws of the weights of RBFNNs. The estimation errors of the states and the weights are verified to be UUB in the light of the obtained sufficient conditions and a special constructing Lyapunov–Krasovskii function. Finally, the effectiveness of the proposed design method is verified by numerical simulations.