Output Feedback Reinforcement Learning Control for the Continuous-Time Linear Quadratic Regulator Problem
Output Feedback Reinforcement Learning Control for the Continuous-Time Linear Quadratic Regulator Problem
复制标题
连续时间线性二次调节器问题的输出反馈强化学习控制
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Zongli Lin
中科院分区：
文献类型：
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作者：
Syed Ali Asad Rizvi;Zongli Lin
在本文中，我们提出了一个输出反馈强化学习方案来解决连续时间线性系统的LQR问题。该问题包括寻找最优反馈增益，以实现渐近稳定的知识，系统的动态和完整的状态信息。提出了一种输出反馈策略迭代算法，迭代求解ADP Bellman方程以找到最优控制参数。与现有的方法不同，该方案不需要任何离散近似，并且不受激励噪声偏置的影响。因此，消除了过去在实现稳定性保证方面一直是瓶颈的贴现因子的需要。学习的控制参数是最优的，完全匹配的LQR Riccati方程的解决方案。仿真结果表明了该方案的有效性。
In this paper, we present an output feedback reinforcement learning scheme to solve the LQR problem for continuous-time linear systems. The problem consists of finding the optimal feedback gain to achieve asymptotic stability without the knowledge of system dynamics and the information of the full state. An output feedback policy iteration algorithm is proposed that iteratively solves the ADP Bellman equation to find the optimal control parameters. Unlike the existing methods, the proposed scheme does not require any discrete approximation, and is not affected by the excitation noise bias. As a result, the need of a discounting factor, which has been a bottleneck in the past in achieving stability guarantee, is eliminated. The learned control parameters are optimal and match exactly the solution of the LQR Riccati equation. Simulation results show the effectiveness of the proposed scheme.