论文标题

使用混合原理解决刚体动力学的最佳控制问题

Solving Optimal Control Problems of Rigid-Body Dynamics with Collisions Using the Hybrid Minimum Principle

论文作者

Hu, Wei, Long, Jihao, Zang, Yaohua, E, Weinan, Han, Jiequn

论文摘要

在许多具有实际应用的动态系统中,碰撞很常见。它们可以作为混合动力学系统配方,当横向某些歧管横向时,它们会自动触发不连续性的混合动力学系统。我们提出了一种基于求解从混合最小原理(HMP)得出的方程的此类混合动力学系统最佳控制问题的算法。该算法是遵循连续近似方法(MSA)精神的迭代方案,并且在初始猜测中观察到的不希望的碰撞是可靠的。我们提出了几种技术,以应对不连续性引入的其他数值挑战。该算法对盘式碰撞问题进行了测试,其最佳解决方案表现出一种或多种碰撞。当算法通过前向欧拉方案实施算法时,就可以根据迭代步骤和渐近的一阶准确性在时间离散化方面进行线性收敛。数值结果表明,与基于梯度下降的直接方法相比,所提出的算法具有更好的准确性和收敛性。此外,该算法比深厚的增强学习方法更简单,更准确,更稳定。

Collisions are common in many dynamical systems with real applications. They can be formulated as hybrid dynamical systems with discontinuities automatically triggered when states transverse certain manifolds. We present an algorithm for the optimal control problem of such hybrid dynamical systems based on solving the equations derived from the hybrid minimum principle (HMP). The algorithm is an iterative scheme following the spirit of the method of successive approximations (MSA), and it is robust to undesired collisions observed in the initial guesses. We propose several techniques to address the additional numerical challenges introduced by the presence of discontinuities. The algorithm is tested on disc collision problems whose optimal solutions exhibit one or multiple collisions. Linear convergence in terms of iteration steps and asymptotic first-order accuracy in terms of time discretization are observed when the algorithm is implemented with the forward-Euler scheme. The numerical results demonstrate that the proposed algorithm has better accuracy and convergence than direct methods based on gradient descent. Furthermore, the algorithm is also simpler, more accurate, and more stable than a deep reinforcement learning method.

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