Meta-Adaptive Nonlinear Control: Theory and Algorithms

• URL: http://arxiv.org/abs/2106.06098v1
• Date: Fri, 11 Jun 2021 00:39:07 GMT
• Title: Meta-Adaptive Nonlinear Control: Theory and Algorithms
• Authors: Guanya Shi, Kamyar Azizzadenesheli, Soon-Jo Chung, Yisong Yue
• Abstract summary: We present an online multi-task learning approach for adaptive nonlinear control, which we call Online Meta-environment Control (OMAC) We provide instantiations of our approach under varying conditions, leading to the first non-asymptotic end-to-end convergence guarantee for multi-task adaptive nonlinear control.
• Score: 47.122874727499216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present an online multi-task learning approach for adaptive nonlinear control, which we call Online Meta-Adaptive Control (OMAC). The goal is to control a nonlinear system subject to adversarial disturbance and unknown $\textit{environment-dependent}$ nonlinear dynamics, under the assumption that the environment-dependent dynamics can be well captured with some shared representation. Our approach is motivated by robot control, where a robotic system encounters a sequence of new environmental conditions that it must quickly adapt to. A key emphasis is to integrate online representation learning with established methods from control theory, in order to arrive at a unified framework that yields both control-theoretic and learning-theoretic guarantees. We provide instantiations of our approach under varying conditions, leading to the first non-asymptotic end-to-end convergence guarantee for multi-task adaptive nonlinear control. OMAC can also be integrated with deep representation learning. Experiments show that OMAC significantly outperforms conventional adaptive control approaches which do not learn the shared representation.

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