5 papers
Thompson Sampling-Based Learning and Control for Unknown Dynamic Systems
Kaikai Zheng, Dawei Shi, Yang Shi +1
Thompson sampling (TS) is a Bayesian randomized exploration strategy that samples options (e.g., system parameters or control laws) from the current posterior and then applies the…
Open-/Closed-loop Active Learning for Data-driven Predictive Control
Shilun Feng, Dawei Shi, Yang Shi +1
An important question in data-driven control is how to obtain an informative dataset. In this work, we consider the problem of effective data acquisition of an unknown linear syste…
Information-triggered Learning with Application to Learning-based Predictive Control
Kaikai Zheng, Dawei Shi, Sandra Hirche +1
Learning-based control has attracted significant attention in recent years, especially for plants that are difficult to model based on first-principles. A key issue in learning-bas…
Event-triggered Observability: A Set-membership Perspective
Kaikai Zheng, Dawei Shi, Tongwen Chen
This work attempts to discuss the observability of linear time-invariant systems with event-triggered measurements. A new notion of observability, namely, -observability is defi…
Learning Hidden Markov Models for Linear Gaussian Systems with Applications to Event-based State Estimation
Kaikai Zheng, Dawei Shi, Ling Shi
This work attempts to approximate a linear Gaussian system with a finite-state hidden Markov model (HMM), which is found useful in solving sophisticated event-based state estimatio…