Neural Identification for Control

• URL: http://arxiv.org/abs/2009.11782v4
• Date: Wed, 16 Mar 2022 03:01:11 GMT
• Title: Neural Identification for Control
• Authors: Priyabrata Saha, Magnus Egerstedt, and Saibal Mukhopadhyay
• Abstract summary: The proposed method relies on the Lyapunov stability theory to generate a stable closed-loop dynamics hypothesis and corresponding control law. We demonstrate our method on various nonlinear control problems such as n-link pendulum balancing and trajectory tracking, pendulum on cart balancing, and wheeled vehicle path following.
• Score: 30.91037635723668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new method for learning control law that stabilizes an unknown nonlinear dynamical system at an equilibrium point. We formulate a system identification task in a self-supervised learning setting that jointly learns a controller and corresponding stable closed-loop dynamics hypothesis. The input-output behavior of the unknown dynamical system under random control inputs is used as the supervising signal to train the neural network-based system model and the controller. The proposed method relies on the Lyapunov stability theory to generate a stable closed-loop dynamics hypothesis and corresponding control law. We demonstrate our method on various nonlinear control problems such as n-link pendulum balancing and trajectory tracking, pendulum on cart balancing, and wheeled vehicle path following.

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