Jiao Shanshan, Wei Qinglai, Wang Fei-Yue
IEEE Trans Cybern. 2024 Oct;54(10):5912-5925. doi: 10.1109/TCYB.2024.3403690. Epub 2024 Oct 9.
This work concentrates on the initial introduction of parallel control to investigate an optimal consensus control strategy for continuous-time nonlinear multiagent systems (MASs) via adaptive dynamic programming (ADP). First, the control input is integrated into the feedback system for parallel control, facilitating an augmented system's optimal consensus control with an appropriate augmented performance index function to be established, which is identical to the original system's suboptimal control with a conventional performance index. Second, the feasibility of the proposed control scheme is evaluated based on the policy iteration algorithm, and the convergence of the algorithm is demonstrated. Then, an online learning algorithm becomes available to implement the ADP-based optimal parallel consensus control protocol without prior knowledge of the system. The Lyapunov approach is employed to indicate that the signals are convergent. Ultimately, the experimental data support the theoretical results.
这项工作专注于并行控制的初步引入，以通过自适应动态规划（ADP）研究连续时间非线性多智能体系统（MASs）的最优一致性控制策略。首先，将控制输入集成到用于并行控制的反馈系统中，通过建立适当的增广性能指标函数来促进增广系统的最优一致性控制，该函数与具有传统性能指标的原系统次优控制相同。其次，基于策略迭代算法评估所提出控制方案的可行性，并证明算法的收敛性。然后，无需系统的先验知识，就可以使用在线学习算法来实现基于ADP的最优并行一致性控制协议。采用李雅普诺夫方法表明信号是收敛的。最终，实验数据支持了理论结果。
