Practical Optimized Time-Varying Formation Tracking Control for Nonlinear Multi-Agent Systems with Unknown Dynamics and Non-Cooperative Leader

This paper presents a practical optimized time-varying formation tracking control method for nonlinear multi-agent systems. In general, solving the HJB equation for nonlinear system is difficult and even no analytical solution can be guaranteed. For multi-agent systems, the solution is more challenging due to the state coupling problem. In the proposed optimized scheme, the problems are solved using the reinforcement learning (RL) of identifier-critic-actor architecture. Furthermore, the unknown dynamics are approximated by neural networks, which are integrated into the controller. Based on the principle of optimality and Lyapunov theory, the optimality and stability of multi-agent systems are proven, respectively. Finally, the simulation results illustrate the effectiveness of the acquired theories.