Learning Quasi-LPV Models and Robust Control Invariant Sets with Reduced Conservativeness

Abstract

We present an approach to identify a quasi Linear Parameter Varying (qLPV) model of a plant, with the qLPV model guaranteed to admit a robust control invariant (RCI) set. It builds upon the concurrent synthesis framework presented in [1], in which the requirement of existence of an RCI set is modeled as a control-oriented regularization. Here, we reduce the conservativeness of the approach by bounding the qLPV system with an uncertain LTI system, which we derive using bound propagation approaches. The resulting regularization function is the optimal value of a nonlinear robust optimization problem that we solve via a differentiable algorithm. We numerically demonstrate the benefits of the proposed approach over two benchmark approaches.

arXiv comment12 pages, 3 figures

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• Safe Reinforcement Learning Using Robust MPC
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• Learning Quasi-LPV Models and Robust Control Invariant Sets with Reduced Conservativeness

Cited by in corpus (1)

• Learning Quasi-LPV Models and Robust Control Invariant Sets with Reduced Conservativeness