no code implementations • 21 Mar 2024 • Yujia Yang, Chris Manzie, Ye Pu
The agents within a multi-agent system (MAS) operating in marine environments often need to utilize task payloads and avoid collisions in coordination, necessitating adherence to a set of relative-pose constraints, which may include field-of-view, line-of-sight, collision-avoidance, and range constraints.
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 • 13 Apr 2023 • Yujia Yang, Chris Manzie, Ye Pu
Moving horizon estimation (MHE) offers benefits relative to other estimation approaches by its ability to explicitly handle constraints, but suffers increased computation cost.
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.
1 code implementation • 31 Mar 2023 • Jinghe Yang, Mingming Gong, Girish Nair, Jung Hoon Lee, Jason Monty, Ye Pu
This paper proposes a cross-modal knowledge distillation framework for training an underwater feature detection and matching network (UFEN).
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 • 26 Aug 2022 • Yujia Yang, Ye Wang, Chris Manzie, Ye Pu
The cyclic-small-gain theorem is used to derive sufficient conditions on the quantization parameters for guaranteeing the stability of the system under a limited data rate.
1 code implementation • 25 Aug 2022 • Ye Wang, Yujia Yang, Ye Pu, Chris Manzie
Constraint handling during tracking operations is at the core of many real-world control implementations and is well understood when dynamic models of the underlying system exist, yet becomes more challenging when data-driven models are used to describe the nonlinear system at hand.
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.
no code implementations • 24 Oct 2017 • Jingge Zhu, Ye Pu, Vipul Gupta, Claire Tomlin, Kannan Ramchandran
As an application of the results, we demonstrate solving optimization problems using a sequential approximation approach, which accelerates the algorithm in a distributed system with stragglers.