强化学习
二部图
计算机科学
数学优化
有界函数
控制器(灌溉)
标识符
梯度下降
控制理论(社会学)
数学
人工神经网络
人工智能
理论计算机科学
图形
数学分析
控制(管理)
农学
生物
程序设计语言
作者
Lei Yan,Junhe Liu,Guanyu Lai,C. L. Philip Chen,Zongze Wu,Zhi Liu
标识
DOI:10.1109/tnnls.2024.3379503
摘要
Developing a distributed bipartite optimal consensus scheme while ensuring user-predefined performance is essential in practical applications. Existing approaches to this problem typically require a complex controller structure due to adopting an identifier–actor–critic framework and prescribed performance cannot be guaranteed. In this work, an adaptive critic learning (ACL)-based optimal bipartite consensus scheme is developed to bridge the gap. A newly designed error scaling function, which defines the user-predefined settling time and steady accuracy without relying on the initial conditions, is then integrated into a cost function. The backstepping framework combines the ACL and integral reinforcement learning (IRL) algorithm to develop the adaptive optimal bipartite consensus scheme, which contributes a critic-only controller structure by removing the identifier and actor networks in the existing methods. The adaptive law of the critic network is derived by the gradient descent algorithm and experience replay to minimize the IRL-based residual error. It is shown that a compute-saving learning mechanism can achieve the optimal consensus, and the error variables of the closed-loop system are uniformly ultimately bounded (UUB). Besides, in any bounded initial condition, the evolution of bipartite consensus is limited to a user-prescribed boundary under bounded initial conditions. The illustrative simulation results validate the efficacy of the approach.
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