Neural-Observer-Based Terminal Sliding Mode Control: Design and Application
Shixi Hou, Cheng Wang, Yundi Chu, Juntao Fei
Abstract
In view of the fact that most devices are nonlinear systems, this article proposes a recurrent probabilistic compensation fuzzy neural network (RPCFNN) control scheme based on global fast terminal sliding mode control (GFTSMC) for a class of nonlinear systems with uncertainties. First, GFTSMC is developed to impose a finite-time convergence feature for the considered systems. Second, a novel RPCFNN framework is designed to further enhance the ability to deal with uncertainty. Due to the added probabilistic estimation and dynamic fuzzy operator, the developed RPCFNN controller possesses superior nonlinearity handling capability. Meanwhile, the stability of RPCFNN is proved by the Lyapunov theory. Finally, active power filter is taken as the representative of nonlinear systems to verify the effectiveness and feasibility of the proposed method. Simulation and experimental results show that the novel RPCFNN controller has good steady-state and dynamic performance and has excellent robustness to deal with uncertain disturbances.