In this paper, we present an approximate optimal dynamic output feedback control learning algorithm to solve the linear quadratic regulation problem for unknown linear continuous-time systems. First, a dynamic output feedback controller is designed by constructing the internal state. Then, an adaptive dynamic programming based learning algorithm is proposed to estimate the optimal feedback control gain by only accessing the input and output data. By adding a constructed virtual observer error into the iterative learning equation, the proposed learning algorithm with the new iterative learning equation is immune to the observer error. In addition, the value iteration based learning equation is established without storing a series of past data, which could lead to a reduction of demands on the usage of memory storage. Besides, the proposed algorithm eliminates the requirement of repeated finite window integrals, which may reduce the computational load. Moreover, the convergence analysis shows that the estimated control policy converges to the optimal control policy. Finally, a physical experiment on an unmanned quadrotor is given to illustrate the effectiveness of the proposed approach.

中文翻译：

---

自适应动态规划未知线性连续时间系统的最优动态输出反馈控制

在本文中，我们提出了一种近似最优动态输出反馈控制学习算法来解决未知线性连续时间系统的线性二次调节问题。首先，通过构造内部状态来设计动态输出反馈控制器。然后，提出了一种基于自适应动态规划的学习算法，通过仅访问输入和输出数据来估计最优反馈控制增益。通过将构造的虚拟观察者误差添加到迭代学习方程中，所提出的具有新迭代学习方程的学习算法不受观察者误差的影响。此外，建立基于值迭代的学习方程不需要存储一系列过去的数据，这可以减少对内存存储使用的需求。此外，所提出的算法消除了重复有限窗积分的要求，这可以减少计算量。此外，收敛分析表明估计控制策略收敛于最优控制策略。最后，在无人四旋翼飞行器上进行了物理实验，说明了该方法的有效性。