Narayanan Vignesh, Sahoo Avimanyu, Jagannathan Sarangapani, George Koshy
IEEE Trans Neural Netw Learn Syst. 2019 May;30(5):1512-1522. doi: 10.1109/TNNLS.2018.2869896. Epub 2018 Oct 8.
In this paper, approximate optimal distributed control schemes for a class of nonlinear interconnected systems with strong interconnections are presented using continuous and event-sampled feedback information. The optimal control design is formulated as an N -player nonzero-sum game where the control policies of the subsystems act as players. An approximate Nash equilibrium solution to the game, which is the solution to the coupled Hamilton-Jacobi equation, is obtained using the approximate dynamic programming-based approach. A critic neural network (NN) at each subsystem is utilized to approximate the Nash solution and novel event-sampling conditions, that are decentralized, are designed to asynchronously orchestrate the sampling and transmission of state vector at each subsystem. To ensure the local ultimate boundedness of the closed-loop system state and NN parameter estimation errors, a hybrid-learning scheme is introduced and the stability is guaranteed using Lyapunov-based stability analysis. Finally, implementation of the proposed event-based distributed control scheme for linear interconnected systems is discussed. For completeness, Zeno-free behavior of the event-sampled system is shown analytically and a numerical example is included to support the analytical results.
本文利用连续和事件采样反馈信息，针对一类具有强互联性的非线性互联系统，提出了近似最优分布式控制方案。最优控制设计被表述为一个N人非零和博弈，其中子系统的控制策略作为参与者。使用基于近似动态规划的方法，获得了该博弈的一个近似纳什均衡解，它是耦合哈密顿 - 雅可比方程的解。每个子系统使用一个批评神经网络（NN）来近似纳什解，并设计了新颖的、分散的事件采样条件，以异步协调每个子系统状态向量的采样和传输。为确保闭环系统状态和NN参数估计误差的局部最终有界性，引入了一种混合学习方案，并使用基于李雅普诺夫的稳定性分析来保证稳定性。最后，讨论了所提出的基于事件的分布式控制方案在线性互联系统中的实现。为了完整性，通过解析证明了事件采样系统的无芝诺行为，并给出了一个数值例子来支持分析结果。
