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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Actor–Critic-Based Optimal Tracking for Partially Unknown Nonlinear Discrete-Time Systems
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Actor–Critic-Based Optimal Tracking for Partially Unknown Nonlinear Discrete-Time Systems

机译：基于Actor-Critic的部分未知非线性离散时间最优跟踪

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摘要

This paper presents a partially model-free adaptive optimal control solution to the deterministic nonlinear discrete-time (DT) tracking control problem in the presence of input constraints. The tracking error dynamics and reference trajectory dynamics are first combined to form an augmented system. Then, a new discounted performance function based on the augmented system is presented for the optimal nonlinear tracking problem. In contrast to the standard solution, which finds the feedforward and feedback terms of the control input separately, the minimization of the proposed discounted performance function gives both feedback and feedforward parts of the control input simultaneously. This enables us to encode the input constraints into the optimization problem using a nonquadratic performance function. The DT tracking Bellman equation and tracking Hamilton–Jacobi–Bellman (HJB) are derived. An actor–critic-based reinforcement learning algorithm is used to learn the solution to the tracking HJB equation online without requiring knowledge of the system drift dynamics. That is, two neural networks (NNs), namely, actor NN and critic NN, are tuned online and simultaneously to generate the optimal bounded control policy. A simulation example is given to show the effectiveness of the proposed method.
机译：本文针对存在输入约束的确定性非线性离散时间（DT）跟踪控制问题，提出了一种部分无模型的自适应最优控制解决方案。首先将跟踪误差动力学和参考轨迹动力学结合起来以形成增强系统。然后，针对最优非线性跟踪问题，提出了一种基于增强系统的折现性能函数。与标准解决方案（分别找到控制输入的前馈项和反馈项）相比，所建议的折现性能函数的最小化同时提供了控制输入的反馈和前馈部分。这使我们能够使用非二次性能函数将输入约束编码为优化问题。推导了DT跟踪Bellman方程和跟踪Hamilton-Jacobi-Bellman（HJB）。基于行为者-批评者的强化学习算法用于在线学习跟踪HJB方程的解，而无需了解系统漂移动力学。也就是说，两个神经网络（即演员NN和评论者NN）在网络上同时进行调整，以生成最佳的有界控制策略。仿真实例表明了该方法的有效性。

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