Deterministic Reinforcement Learning Consensus Control of Nonlinear Multi-Agent Systems via Autonomous Convergence Perception
Shigen Gao, Chaoan Xu, Hairong Dong · IEEE Transactions on Circuits & Systems II Express Briefs · 2023
This brief addresses an approximate optimal consensus control problem for multi-agent systems (MAS) using an autonomous perception empowered deterministic reinforcement learning scheme. An autonomous perception module is designed to online detect the convergence of NNs and tracking performance, and determine proper time moments that reinforcement learning (RL)-based method can be switched to deterministic RL (DRL) mode with constant NNs weight vectors, saving computing resources and reducing computational amount for devices equipped on MAS. Furthermore, once non-convergence of NNs or consensus errors is detected again, the designed autonomous perception module would switch DRL back to RL mode without chattering in the closed-loop system, though switch behaviour and non-smooth function are involved. Simulation results are finally presented to demonstrate the effectiveness.