论文标题
神经随机收缩指标，用于基于学习的控制和估计
Neural Stochastic Contraction Metrics for Learning-based Control and Estimation
论文作者
论文摘要
我们提出了神经随机收缩指标（NSCM），这是一个新的设计框架，可证明对一类随机非线性系统的稳定控制和估计。它使用频谱归一化的深神经网络来构建收缩度量，并通过随机设置中的简化凸优化采样。光谱归一化将度量的状态衍生物限制为Lipschitz的连续，从而确保在随机干扰下，系统轨迹平均距离的指数界限。 NSCM框架允许自主剂实时近似最佳的稳定控制和估计策略，并且胜过现有的非线性控制和估计技术，包括国家依赖性Riccati方程，迭代LQR，EKF，EKF，EKF，以及确定性的神经收缩量值，如模拟结果所示。
We present Neural Stochastic Contraction Metrics (NSCM), a new design framework for provably-stable robust control and estimation for a class of stochastic nonlinear systems. It uses a spectrally-normalized deep neural network to construct a contraction metric, sampled via simplified convex optimization in the stochastic setting. Spectral normalization constrains the state-derivatives of the metric to be Lipschitz continuous, thereby ensuring exponential boundedness of the mean squared distance of system trajectories under stochastic disturbances. The NSCM framework allows autonomous agents to approximate optimal stable control and estimation policies in real-time, and outperforms existing nonlinear control and estimation techniques including the state-dependent Riccati equation, iterative LQR, EKF, and the deterministic neural contraction metric, as illustrated in simulation results.
