Yingzhao Lian | EPFL Graph Search

Yingzhao graduated with a MSc in Mechanical Enginerring from EPFL in 2018. He specialized in optimization and learning theory. He jointly conducted his master’s thesis with the Automatic Control Lab, EPFL and ABB Corporate Research, Baden, where he investigated the problem of data-driven model based optimal control. In August 2018, he joined the Automatic Control Laboratory at EPFL as a PhD student under the supervision of Professor Colin Jones.

Data-driven Methods for Control: from Linear to Lifting

Yingzhao Lian

The progress towards intelligent systems and digitalization relies heavily on the use of automation technology. However, the growing diversity of control objects presents significant challenges for traditional control approaches, as they are highly dependent on expert knowledge and require substantial commissioning effort. In response to this challenge, data-driven methods have emerged as a promising alternative that reduces human involvement by incorporating knowledge extracted from data. This thesis follows a conventional control research path and investigates the application of data-driven methods to linear time-invariant dynamics and nonlinear dynamics.The first part of the thesis focuses on predictive control based on Willems' fundamental lemma. A tractable robust formulation based on the data-enabled predictive control (DeePC) framework is introduced, followed by a bi-level approach that aims to improve robustness and adaptivity. The focus then shifts to nonlinear dynamics, where reproducing kernel Hilbert space (RKHS) and Koopman operator-based heuristics are utilized to extend the applicability of Willems' fundamental lemma.The second part of the thesis concentrates on stability analysis, which is a fundamental aspect of control science. Stability analysis must be robust enough to account for the infinitely many possible realizations of underlying dynamics based on a fixed finite set of data. To this end, a robust stability guarantee for a piece-wise affine (PWA) Lyapunov function is provided, which is a generalization of the classical Lyapunov-Massera local asymptotic stability theorem. Additionally, a convex second-order cone program (SOCP) is proposed to learn a robust PWA Lyapunov function assuming the underlying dynamics are Lipschitz. This approach provides a new means of designing stable control systems without requiring significant human intervention.The last part of this thesis presents additional research on self-triggered control and real-time optimization algorithm design. These studies complement the primary investigation and provide a complete exposition of the research carried out during the Ph.D. program.

EPFL2023

Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning

Loris Di Natale, Colin Neil Jones, Yingzhao Lian, Emilio Maddalena, Jicheng Shi

This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian processes, adaptive data-driven control based on behavioral theory, and deep reinforcement learning. These techniques are compared in terms of data requirements, ease of use, computational burden, and robustness in the context of real-world applications. Our remarks and observations stem from a number of experimental investigations carried out in the field of building control in diverse environments, from lecture halls and apartment spaces to a hospital surgery center. The final goal is to support others in identifying what technique is best suited to tackle their own problems.

2023

Robust Resource-Aware Self-Triggered Model Predictive Control

Yuning Jiang, Colin Neil Jones, Yingzhao Lian

The wide adoption of wireless devices in the Internet of Things requires controllers that are able to operate with limited resources, such as battery life. Operating these devices robustly in an uncertain environment, while managing available resources, increases the difficultly of controller design. This letter proposes a robust self-triggered model predictive control approach to optimize a control objective while managing resource consumption. In particular, a novel zero-order-hold aperiodic discrete-time feedback control law is developed to ensure robust constraint satisfaction for continuous-time linear systems.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Data-driven Methods for Control: from Linear to Lifting

Yingzhao Lian

EPFL2023