Anti‐disturbance adaptive sampled‐data observers for a class of nonlinear systems with unknown hysteresis. (15th February 2021) - Li, Pengpeng; Shen, Yanjun
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.