不确定非线性多智能体系统的最优协同控制

Optimal Coordination Control for Uncertain Nonlinear Multi-agent Systems

针对不确定非线性多智能体系统的最优协同控制问题,提出了基于辨识-评价结构的在线自适应最优控制方案。由于存在不确定性,针对每一个智能体,分别使用一个辨识神经网络用于估计多智能体系统的动态,以及一个评价神经网络用于近似耦合哈密顿-雅可比方程的解,继而导出最优协同控制律。基于辨识-评价结构,辨识神经网络和评价神经网络可以同步调整权值。通过李雅普诺夫(Lyapunov)直接法同时保证辨识神经网络和评价神经网络的权值误差一致最终有界,及闭环系统的稳定。最后通过仿真验证了所提方案的有效性。

An online adaptive optimal control scheme based on identification-evaluation architecture is presented for the optimal coordination control of uncertain, non-linear, multi-agent systems. In light of systematic uncertainty, an identification Neural Network （NN） is utilized to each agent to approximate the uncertain system dynamics, and an evaluation NN is used to estimate the solution of the coupled Hamilton- Jacobi （ H J） equations. Then, the optimal control law can be derived. Based on the developed architecture, the weights of the identification NN and the evaluation NN can be updated synchronously. By using Lyapunov＇s direct method, it is guaranteed that the weight errors of the identification NN and the evaluation NN are uniformly and ultimately bounded, and that the closed-loop system is stable. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed control scheme.