Distributed Optimization Approach for Solving Continuous-Time Lyapunov Equations With Exponential Rate of Convergence
提出一种基于分布式优化的方法，让多智能体网络中的每个智能体利用局部信息和邻居通信，共同求解连续时间李雅普诺夫方程，算法具有指数收敛速度，并通过数值仿真验证了效果。
This article establishes an approach, based on distributed optimization, for solving continuous-time Lyapunov equations (CTLE) over multiagent networks. Each agent in the network knows partial information of the CTLE and has a dynamical system to estimate exact or least-squares solutions. The aim of agents is to find a solution to CTLE by sharing information with connected agents over a network. This article develops distributed algorithms with an exponential rate of convergence for CTLE via the convex optimization design. Finally, this article presents numerical simulations to show the efficacy of the main results.