31 citations · 40 across the 5 of their papers we have counts for
6 papers
Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning
Loris Di Natale, Yingzhao Lian, Emilio T. Maddalena +2
This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian processes, adaptive data-drive…
Resource-Aware Stochastic Self-Triggered Model Predictive Control
Yingzhao Lian, Yuning Jiang, Naomi Stricker +2
This paper considers the control of uncertain systems that are operated under limited resource factors, such as battery life or hardware longevity. We consider here resource-aware…
From System Level Synthesis to Robust Closed-loop Data-enabled Predictive Control
Yinghao Lian, Colin N. Jones
Willems' fundamental lemma and system level synthesis both characterize a linear dynamic system by its input/output sequences. In this work, we extend the application of the fundam…
Koopman based data-driven predictive control
Yingzhao Lian, Renzi Wang, Colin N. Jones
Sparked by the Willems' fundamental lemma, a class of data-driven control methods has been developed for LTI systems. At the same time, the Koopman operator theory attempts to cast…
Robust Learning Model Predictive Control for Periodically Correlated Building Control
Jicheng Shi, Yingzhao Lian, Colin N. Jones
Accounting for more than 40% of global energy consumption, residential and commercial buildings will be key players in any future green energy systems. To fully exploit their poten…
Towards an Unified Structure for Reinforcement Learning: an Optimization Approach
Jicheng Shi, Yingzhao Lian, Colin N. Jones
Both the optimal value function and the optimal policy can be used to model an optimal controller based on the duality established by the Bellman equation. Even with this duality,…