no code implementations • 28 Sep 2023 • Xubo Lyu, Hanyang Hu, Seth Siriya, Ye Pu, Mo Chen
We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operator, and associated linear controller within an iterative loop.
no code implementations • 2 Apr 2023 • Seth Siriya, Jingge Zhu, Dragan Nešić, Ye Pu
We consider the problem of adaptive stabilization for discrete-time, multi-dimensional linear systems with bounded control input constraints and unbounded stochastic disturbances, where the parameters of the true system are unknown.
no code implementations • 15 Sep 2022 • Seth Siriya, Jingge Zhu, Dragan Nešić, Ye Pu
We propose a certainty-equivalence scheme for adaptive control of scalar linear systems subject to additive, i. i. d.
no code implementations • 4 Nov 2020 • Xubo Lyu, Site Li, Seth Siriya, Ye Pu, Mo Chen
On the other end, "classical methods" such as optimal control generate solutions without collecting data, but assume that an accurate model of the system and environment is known and are mostly limited to problems with low-dimensional (lo-dim) state spaces.