Controlled Invariant Sets for Gaussian Process State Space Models

Paul Griffioen; Bingzhuo Zhong; Murat Arcak; Majid Zamani; Marco Caccamo

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arXiv · 2407.11256

Controlled Invariant Sets for Gaussian Process State Space Models

Paul Griffioen·Bingzhuo Zhong·Murat Arcak·Majid Zamani·Marco Caccamo

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Abstract

We compute probabilistic controlled invariant sets for nonlinear systems using Gaussian process state space models, which are data-driven models that account for unmodeled and unknown nonlinear dynamics. We propose a semidefinite programming scheme for designing state-feedback controllers that maximize the probability of the trajectories staying within a probabilistic controlled invariant set while satisfying input constraints. The results are validated on a quadrotor, both in simulation and on a physical platform.

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Paul Griffioen, Bingzhuo Zhong, Murat Arcak, Majid Zamani, Marco Caccamo. 2024-07-15. Controlled Invariant Sets for Gaussian Process State Space Models. https://arxiv.org/abs/2407.11256

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