NN event-triggered finite-time consensus control for uncertain nonlinear Multi-Agent Systems with dead-zone input and actuator failures.

This paper develops a Neural Network (NN) event-triggered finite-time consensus control method for uncertain nonlinear Multi-Agent Systems (MASs) with dead-zone input and actuator failures. In practical applications, actuator failures would inevitably arise in MASs. And the time, pattern, and value of the failures are unknown. Besides, the actuators of MASs also suffer from dead-zone nonlinearity. No matter actuator failures or dead-zone input would dramatically affect the performance and stability of MASs. To address these issues, finite-time adaptive controllers capable of simultaneously compensating for actuator failures and dead-zone input are constructed by adopting the backstepping technology. Meanwhile, the NN control scheme is adopted to handle the unknown nonlinear dynamics of each agent. Furthermore, an event-triggered control mechanism is established that no longer requires continuous communication on the control network. Under the proposed control method, all followers achieve finite-time synchronization, irrespective of the presence of limited bandwidth, unknown failures, and dead-zone input. These results are demonstrated by simulations.

Finite-time consensus control for multi-agent systems with full-state constraints and actuator failures.

Aiming at a class of uncertain nonlinear multi-agent systems (MASs) with full-state constraints and actuator failures, a finite-time consensus control method is developed. Full-state constraints and actuator failures are ubiquitous in practical engineering applications. Violation of constraints would drastically affect the performance of MASs, even arise security problems. It is challenging to guarantee the performance of the MASs when undergoing actuator failures. To tackle these problems, an adaptive consensus control method is established by applying the Backstepping technique and Barrier Lyapunov functions (BLFs) to ensure the performance of the MASs with full-state constraints no matter actuator failures occur. Simultaneously, for the uncertain nonlinear MASs, a finite-time neural network (NN) consensus control scheme is established to ensure system's signals are synchronized in finite time. Moreover, an event-triggered control strategy is constructed to relieve the communication pressure of each agent. Finally, numerical and practical examples are employed to verify the effectiveness of the proposed control strategy.