A DDPG-based solution for optimal consensus of continuous-time linear multi-agent systems

Modeling a system in engineering applications is a time-consuming and labor-intensive task,as system parameters may change with temperature,component aging,etc.In this paper,a novel data-driven model-free optimal controller based on deep deterministic policy gradient(DDPG)is proposed to address the problem of continuous-time leader-following multi-agent consensus.To deal with the problem of the dimensional explosion of state space and action space,two different types of neural nets are utilized to fit them instead of the time-consuming state iteration process.With minimal energy consumption,the proposed controller achieves consensus only based on the consensus error and does not require any initial admissible policies.Besides,the controller is self-learning,which means it can achieve optimal control by learning in real time as the system parameters change.Finally,the proofs of convergence and stability,as well as some simulation experiments,are provided to verify the algorithm’s effectiveness.