Myopic Control of Systems with Unknown Dynamics
Myopic Control of Systems with Unknown Dynamics
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DOI：
10.23919/acc.2019.8814482
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发表时间：
2019-07
期刊：
影响因子：
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通讯作者：
Melkior Ornik;Steven Carr;Arie Israel;U. Topcu
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文献类型：
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作者：
Melkior Ornik;Steven Carr;Arie Israel;U. Topcu
本文介绍了一种策略，满足基本控制目标的系统，其动态几乎完全未知。这种设置的动机是一个系统经历了一个严重的故障，从而显着改变其动态的情况。在这种情况下，保持满足基本控制目标（如达到-避免）的能力是必要的。为了解决我们对系统动力学知识的重大限制，我们提出了一种近视控制理论。近视控制的主要目标是，在任何给定的时间，优化系统轨迹的当前方向，仅给定到该时间为止获得的关于系统的有限信息。基于这一概念，我们提出了一种控制算法，同时使用小扰动的控制努力学习当地的系统动态，而移动的方向，这似乎是最佳的基础上先前获得的知识。我们表明，该算法的结果在一个轨迹，这是近最佳的近视的意义上，即，它正朝着一个方向发展，在给定的时间内似乎是最好的，并为次优性提供了正式的界限。我们证明了所提出的算法的实用性，高保真度模拟损坏的波音747寻求保持水平飞行。
This paper introduces a strategy for satisfying basic control objectives for systems whose dynamics are almost entirely unknown. This setting is motivated by a scenario where a system undergoes a critical failure, thus significantly changing its dynamics. In such a case, retaining the ability to satisfy basic control objectives such as reach-avoid is imperative. To deal with significant restrictions on our knowledge of system dynamics, we develop a theory of myopic control. The primary goal of myopic control is to, at any given time, optimize the current direction of the system trajectory, given solely the limited information obtained about the system until that time. Building upon this notion, we propose a control algorithm which simultaneously uses small perturbations in the control effort to learn local system dynamics while moving in the direction which seems to be optimal based on previously obtained knowledge. We show that the algorithm results in a trajectory that is nearly optimal in the myopic sense, i.e., it is moving in a direction that seems to be nearly the best at the given time, and provide formal bounds for suboptimality. We demonstrate the usefulness of the proposed algorithm on a high-fidelity simulation of a damaged Boeing 747 seeking to remain in level flight.