Publications by Yingzhao Lian | EPFL Graph Search

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Data-driven control approaches for the minimization of energy consumption of buildings have the potential to significantly reduce deployment costs and increase the uptake of advanced control in this sector. A number of recent approaches based on the applic ...

This paper presents a novel distributed approach for solving AC power flow (PF) problems. The optimization problem is reformulated into a distributed form using a communication structure corresponding to a hypergraph, by which complex relationships between ...

In the field of model predictive control, Dataenabled Predictive Control (DeePC) offers direct predictive control, bypassing traditional modeling. However, challenges emerge with increased computational demand due to recursive data updates. This paper intr ...

Distributed embedded systems are pervasive components jointly operating in a wide range of applications. Moving toward energy harvesting powered systems enables their long-term, sustainable, scalable, and maintenance-free operation. When these systems are ...

Employing energy harvesting to power the Internet of Things supports their long-term, self-sustainable, and maintenance-free operation. These energy harvesting systems have an energy management subsystem to orchestrate the flow of energy and optimize their ...

A review of the heating, ventilation and air-conditioning control problem for buildings is presented with particular emphasis on its distinguishing features. Next, we not only examine how data-driven algorithms have been exploited to tackle the main challe ...

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, in ...

Reliable operation of automatic systems is heavily dependent on the ability to detect faults in the underlying dynamics. While traditional model-based methods have been widely used for fault detection, data-driven approaches have garnered increasing attent ...

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 depende ...

By providing various services, such as Incentive-based Demand Response (IBDR), buildings can play a crucial role in the energy market due to their significant energy consumption. However, effectively commissioning buildings for such desired functionalities ...

This letter proposes a data-driven input reconstruction method from outputs (IRO) based on the Willems' Fundamental Lemma. Given only output measurements, the unknown inputsestimated recursively by the IRO asymptotically converge to the true input without ...

Willems' fundamental lemma and system level synthesis both characterize a linear dynamic system by its input/output sequences. In this work, we show that the extension of the fundamental lemma in uncertain LTI systems is equivalent to the system level synt ...

Behavioral theory, which characterizes linear dynamics with measured trajectories, has found successful applications in controller design and signal processing. However, the extension of behavioral theory to general nonlinear system remains an open questio ...

Smart home appliances can time-shift and curtail their power demand to assist demand side management or allow operation with limited power, as in an off-grid application. This letter proposes a scheduling process to start appliances with time-varying deter ...

Cooperative control of connected and automated vehicles (CAVs) promises smoother traffic flow. In mixed traffic, where human-driven vehicles with unknown dynamics coexist, datadriven predictive control techniques allow for CAV safe and optimal control with ...

In many applications, resource-aware devices are connected through a network, such as in the Internet of Things and energy hubs. These devices require proper coordination to achieve a high performance without violation of their resource limits. In this pap ...

Data-driven control can facilitate the rapid development of controllers, offering an alternative to conventional approaches. In order to maintain consistency between any known underlying physical laws and a data-driven decision-making process, preprocessin ...

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 ...

Nonlinear model predictive control (NMPC) has been widely adopted to manipulate bilinear systems with dynamics that include products of the inputs and the states. These systems are ubiquitous in chemical processes, mechanical systems, and quantum physics, ...

In this technical article, we analyze the performance improvement and optimality properties of the learning model predictive control (LMPC) strategy for linear deterministic systems. The LMPC framework is a policy iteration scheme where closed-loop traject ...

Yingzhao Lian

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Adaptive Robust Data-Driven Building Control via Bilevel Reformulation: An Experimental Result

Hypergraph-Based Fast Distributed AC Power Flow Optimization

Efficient Recursive Data-enabled Predictive Control

Self-triggered Control with Energy Harvesting Sensor Nodes

Joint Energy Management for Distributed Energy Harvesting Systems

Data-driven methods for building control - A review and promising future directions

Robust Resource-Aware Self-Triggered Model Predictive Control

Fault Detection via Occupation Kernel Principal Component Analysis

Data-driven Methods for Control: from Linear to Lifting

Adaptive data-driven prediction in a building control hierarchy: A case study of demand response in Switzerland

Data-Driven Input Reconstruction and Experimental Validation

From System Level Synthesis to Robust Closed-loop Data-Enabled Predictive Control

Nonlinear Data-Enabled Prediction and Control

Scheduling Delays and Curtailment for Household Appliances With Deterministic Load Profiles Using MPC

Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow

Resource-Aware Asynchronous Multi-Agent Coordination via Self-Triggered MPC

Physically Consistent Multiple-Step Data-Driven Predictions Using Physics-Based Filters

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

A Proximal-Point Lagrangian-Based Parallelizable Nonconvex Solver for Bilinear Model Predictive Control

On the Optimality and Convergence Properties of the Iterative Learning Model Predictive Controller