论文标题
快速准确的数值最佳控制的神经求解器
Neural Solvers for Fast and Accurate Numerical Optimal Control
论文作者
论文摘要
为动态系统的合成最佳控制器的合成通常涉及通过艰苦的实时约束解决优化问题。这些约束确定了可以应用的数值方法类别的类别：计算昂贵但准确的数值例程被快速和不准确的方法取代，以解决解决方案准确性的推理时间。本文提供了鉴于固定的计算预算，提高优化控制政策质量的技术。我们通过高空方法实现上述方法，该方法杂交了微分方程求解器和神经网络。在低维度和高维度的直接和退缩最佳控制任务中评估了该性能，其中提出的方法在解决方案的准确性和控制性能方面表现出一致的帕累托改善。
Synthesizing optimal controllers for dynamical systems often involves solving optimization problems with hard real-time constraints. These constraints determine the class of numerical methods that can be applied: computationally expensive but accurate numerical routines are replaced by fast and inaccurate methods, trading inference time for solution accuracy. This paper provides techniques to improve the quality of optimized control policies given a fixed computational budget. We achieve the above via a hypersolvers approach, which hybridizes a differential equation solver and a neural network. The performance is evaluated in direct and receding-horizon optimal control tasks in both low and high dimensions, where the proposed approach shows consistent Pareto improvements in solution accuracy and control performance.
