Reinforcement learning based privacy-preserving consensus tracking control of nonstrict-feedback discrete-time multi-agent systems

Reinforcement learning based privacy-preserving consensus tracking control of nonstrict-feedback discrete-time multi-agent systems

연구 분야: Verification

학회: Frontiers of Information Technology & Electronic Engineering

This paper investigates a privacy-preserving consensus tracking problem for a class of nonstrict-feedback discrete-time multi-agent systems (MASs). An improved Liu cryptosystem is developed to alleviate the errors between encryption and decryption on the plaintext, which ensures satisfactory recovery of the plaintext information. A reinforcement learning (RL) technique is then employed to compensate for unknown dynamics and errors between true signals and decrypted ones. Based on the backstepping and graph theory, an RL-based privacy-preserving consensus tracking control strategy is further designed. By virtue of graph theory and Lyapunov stability theory, it is shown that the consensus tracking errors and all signals in the MAS are ultimately bounded. Finally, simulation examples are presented for verification of the effectiveness of the control strategy.