Sampled-data stabilization analysis of neural-network-based control systems: A discontinuous bilateral looped-functional approach

Abstract

This work is engaged in investigating the problem of stabilization of neural-network-based control systems (NNBCSs) via sampled-data (SD) control. Firstly, a time-dependent discontinuous bilateral looped-functional (DBLF) approach is proposed by fully considering the sampling state information, which relaxes the restriction of monotonicity. Then, a new lemma regarding sampled-data-based Lyapunov stability result is established for nonlinear systems, which is strictly proved by the Cauchy-Schwarz inequality. Moreover, the proposed lemma improves the conventional Lyapunov theorem and weakens the asymptotic stability conditions of continuous-time systems into discrete conditional expression. In addition, a SD three-layer fully connected feed-forward neural network (TLFCFFNN) controller is considered to reach the maximum interval of sampling. Finally, two numerical examples are presented to illustrate the effectiveness of the theoretical results.