Neural Stochastic Contraction Metrics for Learning-based Control and Estimation

• URL: http://arxiv.org/abs/2011.03168v4
• Date: Sun, 3 Jan 2021 14:12:28 GMT
• Title: Neural Stochastic Contraction Metrics for Learning-based Control and Estimation
• Authors: Hiroyasu Tsukamoto and Soon-Jo Chung and Jean-Jacques E. Slotine
• Abstract summary: The NSCM framework allows autonomous agents to approximate optimal stable control and estimation policies in real-time. It outperforms existing nonlinear control and estimation techniques including the state-dependent Riccati equation, iterative LQR, EKF, and the neural contraction.
• Score: 13.751135823626493
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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