Feasibility-Guided Learning for Constrained Optimal Control Problems
Feasibility-Guided Learning for Constrained Optimal Control Problems
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DOI：
10.1109/cdc42340.2020.9303857
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Wei Xiao;C. Belta;C. Cassandras
中科院分区：
文献类型：
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作者：
Wei Xiao;C. Belta;C. Cassandras
通过使用控制屏障函数 (CBF) 和控制李亚普诺夫函数 (CLF)，可以将具有确保安全性的约束的最优控制问题映射到一系列实时优化问题。这些方法的主要挑战之一是在系统仿射控制的情况下确保所得到的二次规划 (QP) 的可行性。在本文中，我们通过定义高阶CBF（HOCBF）来提高可行性鲁棒性（即存在时变和未知不安全集的情况下的可行性维护）；这是通过使用机器学习技术提出的可行性引导学习方法来实现的。所提出的可行性引导学习方法的有效性在机器人控制问题上得到了证明。
Optimal control problems with constraints ensuring safety can be mapped onto a sequence of real time optimization problems through the use of Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). One of the main challenges in these approaches is ensuring the feasibility of the resulting quadratic programs (QPs) if the system is affine in controls. In this paper, we improve the feasibility robustness (i.e., feasibility maintenance in the presence of time-varying and unknown unsafe sets) through the definition of a High Order CBF (HOCBF); this is achieved by a proposed feasibility-guided learning approach using machine learning techniques. The effectiveness of the proposed feasibility-guided learning approach is demonstrated on a robot control problem.