Optimized adaptive event‐triggered tracking control for multi‐agent systems with full‐state constraints

In this article, the event-triggered optimized adaptive tracking control problem is investigated for a class of multi-agent systems subject to full-state constraints. To address the full-state constraints problem, a nonlinear mapping technique is applied, which can release the feasibility conditions for virtual controllers. Based on the mean-value theorem, the nonaffine nonlinear terms generated by transformed system are separated, which overcomes the obstacle of solving optimized solution. The neural network based reinforcement learning (RL) algorithm with the identifier-critic-actor architecture is introduced to obtain the optimal solution of systems with unknown dynamics. It is worth noting that the RL algorithm in this article is simplified, which can reduce the computational burden. To reducing the communication burden, an event-triggered mechanism with time-varying threshold related to the optimized control signal is developed. By applying the Lyapunov stability method, it is proved that the desired optimized tracking performance and the stability of the closed-loop systems can be guaranteed. Finally, a simulation example demonstrates that the proposed control strategy is effective.