arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2201.10197math.OCcs.SYeess.SYmath.DS

未知系统模型下线性二次调节的在线执行器选择与控制器设计

Online Actuator Selection and Controller Design for Linear Quadratic Regulation with Unknown System Model

Lintao Ye, Ming Chi, Zhi-Wei Liu, Vijay Gupta

AI总结:

针对未知模型的带高斯噪声有限时域线性二次调节问题,提出结合多臂老虎机执行器选择与确定性等价控制的在线算法,证明回合/非回合设置下的遗憾界,验证可扩展性并通过数值实验佐证理论。

摘要:

我们研究了时长为$T$的有限时域下、系统模型未知且含高斯噪声的线性二次调节问题中,执行器选择与控制器设计的同步求解问题。我们同时考虑了该问题的回合式与非回合式设置,并提出了在线算法,可在基数约束下确定待使用的执行器集合,并给出对应所选执行器集合的控制量。在回合式设置中,与系统的交互分为$N$个回合,每个回合从给定初始条件重启,时长为$T$;在非回合式设置中,交互持续进行。我们的在线算法利用多臂老虎机算法选择执行器集合,采用确定性等价方法设计对应控制器。我们证明,该在线算法在回合式设置下的遗憾为$\text{regret}=\text{O}(\text{sqrt}(N))$,非回合式设置下的遗憾为$\text{regret}=\text{O}(T^{2/3})$。我们拓展了算法设计与分析,证明其在候选执行器总数和基数约束维度上具备可扩展性。我们通过数值实验验证了理论结果。

英文摘要:

We study the simultaneous actuator selection and controller design problem for linear quadratic regulation with Gaussian noise over a finite horizon of length $T$ and unknown system model. We consider both episodic and non-episodic settings of the problem and propose online algorithms that specify both the sets of actuators to be utilized under a cardinality constraint and the controls corresponding to the sets of selected actuators. In the episodic setting, the interaction with the system breaks into $N$ episodes, each of which restarts from a given initial condition and has length $T$. In the non-episodic setting, the interaction goes on continuously. Our online algorithms leverage a multiarmed bandit algorithm to select the sets of actuators and a certainty equivalence approach to design the corresponding controls. We show that our online algorithms yield $\sqrt{N}$-regret for the episodic setting and $T^{2/3}$-regret for the non-episodic setting. We extend our algorithm design and analysis to show scalability with respect to both the total number of candidate actuators and the cardinality constraint. We numerically validate our theoretical results.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑