Event-triggered neuro sliding mode control for an uncertain Euler–Lagrange system: An impulsive dynamical system approach

Abstract

This paper investigates the design of a neural network-based sliding mode control (SMC) with event-based sampling for an uncertain Euler–Lagrange (EL) system. The EL system considered here is connected to the controller over a shared communication network. The event-based SMC is designed to ensure the reduced usage of the communication and computational resources with robust tracking performance. The controller is designed under the assumption that the EL system dynamics are unknown; thus, a radial basis function neural network (RBFNN) is used to estimate it. The discrete weight update dynamics of RBFNN is formulated for a continuous-time EL system which makes the closed-loop system hybrid. Hence, the stability of the closed-loop system is investigated using the theory of impulsive dynamical systems with the admissible inter sampling time. Finally, a numerical example of an EL system is simulated to establish the effectiveness of the proposed control strategy.