A Data-driven Approach for Constrained Infinite-Horizon Linear Quadratic Regulation
A Data-driven Approach for Constrained Infinite-Horizon Linear Quadratic Regulation
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DOI：
10.1109/cdc42340.2020.9304046
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Bo Pang;Zhong-Ping Jiang
中科院分区：
文献类型：
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作者：
Bo Pang;Zhong-Ping Jiang
针对一类具有状态约束和控制约束的离散线性定常系统，提出了一种数据驱动算法，用于求解无穷视界线性二次调节问题。该问题分为有约束的有限视界LQR子问题和无约束的无限视界LQR子问题，可分别从采集的输入/状态数据中直接求解。在一定条件下，子问题的解的组合收敛于原问题的最优解。通过数值算例验证了该方法的有效性。
This paper presents a data-driven algorithm to solve the problem of infinite-horizon linear quadratic regulation (LQR), for a class of discrete-time linear time-invariant systems subjected to state and control constraints. The problem is divided into a constrained finite-horizon LQR subproblem and an unconstrained infinite-horizon LQR subproblem, which can be solved directly from collected input/state data, separately. Under certain conditions, the combination of the solutions of the subproblems converges to the optimal solution of the original problem. The effectiveness of the proposed approach is validated by a numerical example.