Data-Based Optimal Control for Weakly Coupled Nonlinear Systems Using Policy Iteration
针对动态未知的弱耦合连续时间非线性系统，提出一种基于数据的在线学习算法，利用弱耦合理论将原问题分解为多个降阶子问题，并通过策略迭代和神经网络实现无模型最优控制。
In this paper, a data-based online learning algorithm is established to solve the optimal control problem for weakly coupled continuous-time nonlinear systems with completely unknown dynamics. Using the weak coupling theory, we reformulate the original problem into three reduced-order optimal control problems. We establish an online model-free integral policy iteration algorithm to solve the decoupled optimal control problems without system dynamics. To implement the data-based online learning algorithm, the actor-critic technique based on neural networks and the least squares method are used. Two simulation examples are given to verify the effectiveness of the developed algorithm.