Event-Triggered Adaptive Neural Network Sensor Failure Compensation for Switched Interconnected Nonlinear Systems With Unknown Control Coefficients

In this article, a decentralized adaptive neural network (NN) event-triggered sensor failure compensation control issue is investigated for nonlinear switched large-scale systems. Due to the presence of unknown control coefficients, output interactions, sensor faults, and arbitrary switchings, previous works cannot solve the investigated issue. First, to estimate unmeasured states, a novel observer is designed. Then, NNs are utilized for identifying both interconnected terms and unstructured uncertainties. A novel fault compensation mechanism is proposed to circumvent the obstacle caused by sensor faults, and a Nussbaum-type function is introduced to tackle unknown control coefficients. A novel switching threshold strategy is developed to balance communication constraints and system performance. Based on the common Lyapunov function (CLF) method, an event-triggered decentralized control scheme is proposed to guarantee that all closed-loop signals are bounded even if sensors undergo failures. It is shown that the Zeno behavior is avoided. Finally, simulation results are presented to show the validity of the proposed strategy.