基于强化学习的一类具有输入约束非线性系统最优控制

针对部分系统存在输入约束和不可测状态的最优控制问题,本文将强化学习中基于执行–评价结构的近似最优算法与反步法相结合,提出了一种最优跟踪控制策略.首先,利用神经网络构造非线性观测器估计系统的不可测状态.然后,设计一种非二次型效用函数解决系统的输入约束问题.相比现有的最优方法,本文提出的最优跟踪控制方法不仅具有反步法在处理...

Optimal control for a class of nonlinear systems with input constraints based on reinforcement learning

In this paper, by incorporating the approximate optimization algorithm, which is derived from actor-critic structure in reinforcement learning, into the backstepping, an optimal tracking control strategy is proposed for a class of nonlinear systems with immeasurable states and input constraints. First, a nonlinear observer is constructed with neural network to estimate the immeasurable states. Then, a non-quadratic cost function is designed to solve the problem of controller constraints. Compared with the existing optimization methods, the optimal tracking control method proposed in this paper not only has the advantage of backstepping technique in addressing the n-order system tracking problem, but also ensures that all virtual controllers are optimal. And this method simplifies the controller design. Finally, according to Lyapunov stability theory, it is proven that all signals in the closed-loop system are uniformly ultimately bounded. The effectiveness of the proposed method is verified by the simulation results.