From the 2 of 6 linked papers with an AI index.
6 papers
Non-asymptotic Bounds of Learning-based Linear MPC With Input Constraints and Unbounded Stochastic Noise
Changyi Lei, Seth Siriya, Dragan NeÅ¡iÄ +1
The paper proposes a certainty‑equivalence, switching model predictive control scheme that learns unknown linear dynamics online via regularized least‑squares, handling hard input…
Learning-based Homothetic Tube MPC with Non-Asymptotic Guarantees
Changyi Lei, Seth Siriya, Dragan NeÅ¡iÄ +1
The paper proposes a model predictive control method that builds a confidence set for unknown linear system parameters using regularized least‑squares, and incorporates this set in…
A Framework for Adaptive Stabilisation of Nonlinear Stochastic Systems
Seth Siriya, Jingge Zhu, Dragan NeÅ¡iÄ +1
We consider the adaptive control problem for discrete-time, nonlinear stochastic systems with linearly parameterised uncertainty. Assuming access to a parameterised family of contr…
Non-Asymptotic Bounds for Closed-Loop Identification of Unstable Nonlinear Stochastic Systems
Seth Siriya, Jingge Zhu, Dragan NeÅ¡iÄ +1
We consider the problem of least squares parameter estimation from single-trajectory data for discrete-time, unstable, closed-loop nonlinear stochastic systems, with linearly param…
Towards Fast and Safety-Guaranteed Trajectory Planning and Tracking for Time-Varying Systems
Seth Siriya, Mo Chen, Ye Pu
When deploying autonomous systems in unknown and changing environments, it is critical that their motion planning and control algorithms are computationally efficient and can be re…
Optimal Control-Based Baseline for Guided Exploration in Policy Gradient Methods
Xubo Lyu, Site Li, Seth Siriya +2
In this paper, a novel optimal control-based baseline function is presented for the policy gradient method in deep reinforcement learning (RL). The baseline is obtained by computin…