Learning over All Stabilizing Nonlinear Controllers for a Partially-Observed Linear System
| Autor: | Ruigang Wang, Nicholas H. Barbara, Max Revay, Ian R. Manchester |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Control and Optimization Optimization and Control (math.OC) Control and Systems Engineering FOS: Electrical engineering electronic engineering information engineering FOS: Mathematics Systems and Control (eess.SY) Electrical Engineering and Systems Science - Systems and Control Mathematics - Optimization and Control Machine Learning (cs.LG) |
| Popis: | This paper proposes a nonlinear policy architecture for control of partially-observed linear dynamical systems providing built-in closed-loop stability guarantees. The policy is based on a nonlinear version of the Youla parameterization, and augments a known stabilizing linear controller with a nonlinear operator from a recently developed class of dynamic neural network models called the recurrent equilibrium network (REN). We prove that RENs are universal approximators of contracting and Lipschitz nonlinear systems, and subsequently show that the the proposed Youla-REN architecture is a universal approximator of stabilizing nonlinear controllers. The REN architecture simplifies learning since unconstrained optimization can be applied, and we consider both a model-based case where exact gradients are available and reinforcement learning using random search with zeroth-order oracles. In simulation examples our method converges faster to better controllers and is more scalable than existing methods, while guaranteeing stability during learning transients. |
| Databáze: | OpenAIRE |
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