Linear Quadratic Tracker with Integrator using Integral Reinforcement Learning
Linear Quadratic Tracker with Integrator using Integral Reinforcement Learning
复制标题
使用积分强化学习的带有积分器的线性二次跟踪器
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
A. Tsourdos
中科院分区：
文献类型：
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作者：
On Park;Hyo;A. Tsourdos
本文描述了一种强化学习（RL）应用，使用基于线性二次型调节器（LQR）的跟踪控制器，该控制器增加了跟踪误差项。为了处理稳态误差，在状态变量中加入跟踪误差的积分项，设计了带积分器的线性二次型跟踪器（LQTI）。基于LQTI，在线学习使用积分强化学习（IRL）的跟踪问题，通过调整增广状态变量的部分未知的连续时间系统找到最优控制。通过两个应用实例的数值仿真，验证了该方法的最优控制解和性能。
This paper describes a Reinforcement Learning (RL) application using Linear Quadratic Regulator (LQR) based tracking controller, which is augmented with a tracking error term. In order to deal with the steady-state errors, Linear Quadratic Tracker with Integrator (LQTI) is designed by adding an integration term of the tracking error in the state variable. Based on the LQTI, an online learning using the Integral Reinforcement Learning (IRL) is applied for the tracking problem to find the optimal control on the partially unknown continuous-time systems by regulating the augmented state variable. The optimal control solution and the performance of the method are verified through numerical simulation on two applications.