Optimized backstepping‐based finite‐time containment control for nonlinear multi‐agent systems with prescribed performance

Li Tang, Liang Zhang, Ning Xu
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Abstract

In this article, a finite‐time optimal containment control method is proposed for nonlinear multi‐agent systems with prescribed performance. First, a neural network‐based reinforcement learning algorithm is developed under the optimized backstepping framework. The algorithm employs an identifier‐critic‐actor architecture, where the identifiers, critics and actors are used to estimate the unknown dynamics, evaluate the system performance, and optimize the system, respectively. Subsequently, in order to guarantee the transient performance of the tracking error, the original system is converted into an equivalent unconstrained system. Then, the tracking errors are allowed to converge to a prescribed set of residuals in finite time by combining prescribed performance control and finite‐time optimal control techniques. Furthermore, by using the Lyapunov stability theorem, it is verified that all signals are semi‐globally practical finite‐time stable, and all followers can converge to a convex region formed by multiple leaders. Finally, the effectiveness of the proposed scheme is demonstrated by a practical example.
为具有规定性能的非线性多代理系统优化基于后步法的有限时间遏制控制
本文针对具有规定性能的非线性多代理系统提出了一种有限时间最优遏制控制方法。首先,在优化反步态框架下开发了一种基于神经网络的强化学习算法。该算法采用识别器-批判者-行动者架构,其中识别器、批判者和行动者分别用于估计未知动态、评估系统性能和优化系统。随后,为了保证跟踪误差的瞬态性能,原始系统被转换为等效的无约束系统。然后,结合规定性能控制和有限时间优化控制技术,使跟踪误差在有限时间内收敛到规定的残差集。此外,通过利用 Lyapunov 稳定性定理,验证了所有信号都是半全局实用有限时间稳定的,并且所有跟随者都能收敛到由多个领导者形成的凸区域。最后,通过一个实际例子证明了所提方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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