Event-triggered neural control for non-strict-feedback systems with actuator failures

This study is concerned with an adaptive event-triggered control problem for non-linear non-strict-feedback systems subject to actuator failures. For actuator failures, both total loss of effectiveness (TLOE) and partial loss of effectiveness (PLOE) are considered. The event-triggered mechanism is proposed in this study, which may influence measurement errors. Neural networks (NNs) are used to approximate unknown non-linear functions, and a neural observer is designed to estimate unknown state variables. Then a neural tracking controller is constructed to reduce the communication burden via backstepping technique. The new controller ensures that the output of the system reaches to the same trajectory with the reference signal, and it also guarantees the boundedness of all the closed-loop signals. Finally, a simulation example is used to testify the results.