Predictor-Based Adaptive Neural Network Control of Unknown Nonlinear Systems With Time-Varying Input Delay

Stability is a prerequisite for safe and reliable system operation; however, time-varying input delays (TVIDs) and unknown nonlinearities pose significant challenges for stable control. To overcome these challenges, a predictor-based adaptive neural network control method is proposed. First, to mitigate the adverse effects of TVIDs on control performance, an observer-form predictor (OFP) is constructed, and a corresponding state feedback control strategy is designed. Second, a radial basis function neural network (RBFNN) with the predicted state as input is employed to approximate the unknown system nonlinearity and eliminate acausality in the controller and OFP. Third, to improve control performance, a prediction error compensation term is added to the OFP-based controller. Furthermore, a Lyapunov-Krasovskii (L-K) functional is designed for stability analysis, and less conservative sufficient conditions (SCs) are derived based on the generalized free-weighting matrix inequality (GFMI). Finally, simulation examples are provided to verify the effectiveness of the proposed method.