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Simplified reinforcement learning control algorithm for p-norm multiagent systems with full-state constraints
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DOI：10.1016/j.neucom.2023.126504.png)
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Abstract
En 中文
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.
Keywords:
Birpartite tracking control
Full -state constraints
Optimized backstepping
p -norm multiagent systems
Simplified reinforcement learning