论文标题
使用收缩指标学习认证的控制
Learning Certified Control using Contraction Metric
论文作者
论文摘要
在本文中，我们解决了找到认证的控制策略的问题，该策略将机器人从任何给定的初始状态和对所需参考轨迹的任何有限的干扰下驱动，并保证跟踪错误的收敛性或界限。这样的控制器对于安全运动计划至关重要。我们利用控制收缩度量的高级理论，并设计基于神经网络的学习框架，以合成合成的收缩度量和控制膜系统的控制器。我们进一步提供了验证收敛性和有限误差保证的方法。我们使用一套具有挑战性的机器人模型（包括具有学习动态的神经网络的模型）来证明方法的性能。我们将方法与使用方案总和编程，强化学习和模型预测控制的领先方法进行比较。结果表明，我们的方法确实可以处理更广泛的系统，这些系统具有较少的跟踪错误和更快的执行速度。代码可在https://github.com/sundw2014/c3m上找到。
In this paper, we solve the problem of finding a certified control policy that drives a robot from any given initial state and under any bounded disturbance to the desired reference trajectory, with guarantees on the convergence or bounds on the tracking error. Such a controller is crucial in safe motion planning. We leverage the advanced theory in Control Contraction Metric and design a learning framework based on neural networks to co-synthesize the contraction metric and the controller for control-affine systems. We further provide methods to validate the convergence and bounded error guarantees. We demonstrate the performance of our method using a suite of challenging robotic models, including models with learned dynamics as neural networks. We compare our approach with leading methods using sum-of-squares programming, reinforcement learning, and model predictive control. Results show that our methods indeed can handle a broader class of systems with less tracking error and faster execution speed. Code is available at https://github.com/sundw2014/C3M.
