Neural Network Identification and Sliding Mode Control for Hysteresis Nonlinear System with Backlash-Like Model

Abstract

A new neural network sliding mode control (NNSMC) is proposed for backlash-like hysteresis nonlinear system in this paper. Firstly, only one neural network is designed to estimate the unknown system states and hysteresis section instead of multiscale neural network at former researches since that can save computation and simplify the controller design. Secondly, a new NNSMC is proposed for the hysteresis nonlinearity where it does not need tracking error transformation. Finally, the Lyapunov functions are adopted to guarantee the stabilities of the identification and control strategies semiglobally uniformly ultimately bounded (UUB). Two cases simulations are proved the effectiveness of the presented identification approach and the performance of the NNSMC.

U-Model Based Adaptive Neural Networks Fixed-Time Backstepping Control for Uncertain Nonlinear System

“…Recently, neural networks control has increasingly attracted attention and intensive research has been performed in adaptive law for training neural networks weights and application in different fields [1][2][3]. Neural network technique is a typical data-driven modelling method [4][5][6], which used measured data to find proper control in reversion of some expected closed-loop performance [7][8][9].…”

Convergence Time Calculation for Supertwisting Algorithm and Application for Nonaffine Nonlinear Systems

“…Sliding mode control (SMC) has become one of the most e cient techniques to control uncertain complex systems and engineering [1][2][3]. eoretically, such controllers are able to compensate and match disturbances by con ning the systems' trajectories in a properly chosen hypersurface (the so-called sliding manifold) [4][5][6] and, under the chosen surface, make the origin of the state space an asymptotically stable equilibrium point for the closed-loop systems [7][8][9].…”