This paper addresses an optimal output regulation problem for linear time-invariant systems with unknown dynamics. First, a new augmented virtual system is designed to replace the original system and the internal model. Then, by incorporating the data from the augmented virtual system with adaptive dynamic programming (ADP) method, a new iterative learning equation without requiring integral operations or constructing internal model in advance is proposed for establishing the data-driven learning algorithm. Compared with existing ADP-based learning algorithms for linear continuous-time systems, the proposed learning algorithm relaxes both requirements on recording the complete continuous data and setting an initial stabilizing control policy. Finally, the effectiveness of the proposed algorithm is illustrated by an example.

本文解决了动态未知的线性时不变系统的最优输出调节问题。首先，设计了一个新的增强虚拟系统来替代原有系统和内部模型。然后，通过将来自增强虚拟系统的数据与自适应动态规划（ADP）方法相结合，提出了一种新的迭代学习方程，无需积分运算或预先构建内部模型，用于建立数据驱动的学习算法。与现有的基于 ADP 的线性连续时间系统学习算法相比，所提出的学习算法放宽了记录完整连续数据和设置初始稳定控制策略的要求。最后通过一个例子说明了所提算法的有效性。