Simplified reinforcement learning control algorithm for p-norm multiagent systems with full-state constraints - Wang, Min; Cao, Liang; Liang, Hongjing; Xiao, Wenbin (Neurocomputing (Amsterdam), 2023)
This paper studies the bipartite consensus tracking control problem with full-state constraints for p-norm multiagent systems. For the full-state constraints problem of p-norm multiagent systems, a transformed function is utilized to achieve the objective of the constraints, which has the property of low complexity because it avoids the intervention of log-type functions or trigonometric functions in the controllers. Meanwhile, the bipartite control performance of p-norm multiagent systems is also guaranteed. Moreover, under the simplified reinforcement learning framework, a compensation strategy is utilized to compensate the unknown ideal weights caused by the simplified reinforcement learning algorithm of critic-actor method, and greatly improve the accuracy of the tracking performance for p-norm multiagent systems. Furthermore, the effectiveness of the proposed strategy is illustrated by an actual simulation.