Neural network-based fault detection for nonlinear networked systems with uncertain medium access constraint: Application to motor systems

The fault detection for a class of continuous-time nonlinear networked control systems with medium access constraint is concerned in this paper, where the occurring probability of transition from one sensor to another is allowed to be partially unknown and uncertain. First of all, a Markovian system approach is adopted to describe the access process of sensors, in which only one sensor is allowed to access the communication channel. A robust filter based residual generator is proposed to generate the residual signal such that it can be used to indicate whether the fault has occurred or not. The nonlinear term is approximated by a neural network, and the Lyapunov-Krasovskii functional is introduced to analyze the fault detection system, three sufficient conditions for the stochastic stability of fault detection error system are given, and the fault detection filter gains are calculated via solving some matrix inequalities. In the simulation, a second-order DC motor system is used to validate of the main results, which shows the effectiveness of the fault detector design. (c) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper focuses on fault detection for a class of continuous-time nonlinear networked control systems with medium access constraints. The access process of sensors is described using a Markovian system approach, and a robust filter based residual generator is proposed. The effectiveness of the fault detector design is validated through simulation with a second-order DC motor system.