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- W4365600796 abstract "This paper presents a policy parameterization for learning-based control on nonlinear, partially-observed dynamical systems. The parameterization is based on a nonlinear version of the Youla parameterization and the recently proposed Recurrent Equilibrium Network (REN) class of models. We prove that the resulting Youla-REN parameterization automatically satisfies stability (contraction) and user-tunable robustness (Lipschitz) conditions on the closed-loop system. This means it can be used for safe learning-based control with no additional constraints or projections required to enforce stability or robustness. We test the new policy class in simulation on two reinforcement learning tasks: 1) magnetic suspension, and 2) inverting a rotary-arm pendulum. We find that the Youla-REN performs similarly to existing learning-based and optimal control methods while also ensuring stability and exhibiting improved robustness to adversarial disturbances." @default.
- W4365600796 created "2023-04-15" @default.
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- W4365600796 date "2023-04-12" @default.
- W4365600796 modified "2023-09-28" @default.
- W4365600796 title "Learning Over All Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems" @default.
- W4365600796 doi "https://doi.org/10.48550/arxiv.2304.06193" @default.
- W4365600796 hasPublicationYear "2023" @default.
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