60 citations · 193 across the 43 of their papers we have counts for
16 papers · 1 filter
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…
Data-driven input reconstruction and experimental validation
Jicheng Shi, Yingzhao Lian, Colin N. Jones
This paper addresses a data-driven input reconstruction problem based on Willems' Fundamental Lemma in which unknown input estimators (UIEs) are constructed directly from historica…
Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use
Emilio T. Maddalena, Silvio A. Muller, Rafael M. dos Santos +2
Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case study of an industrial plant responsible for cooling a hospital surgery center. The ado…
Adaptive Robust Data-driven Building Control via Bi-level Reformulation: an Experimental Result
Yingzhao Lian, Jicheng Shi, Manuel Koch +1
Data-driven control approaches for the minimization of energy consumption of buildings have the potential to significantly reduce deployment costs and increase uptake of advanced c…
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…
KPC: Learning-Based Model Predictive Control with Deterministic Guarantees
Emilio T. Maddalena, Paul Scharnhorst, Yuning Jiang +1
We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown syst…
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…
Convergence Certificate for Stochastic Derivative-Free Trust-Region Methods based on Gaussian Processes
Harsh A. Shukla, Tafarel de Avila Ferreira, Timm Faulwasser +2
In many machine learning applications, one wants to learn the unknown objective and constraint functions of an optimization problem from available data and then apply some techniqu…
Deterministic error bounds for kernel-based learning techniques under bounded noise
Emilio T. Maddalena, Paul Scharnhorst, Colin N. Jones
We consider the problem of reconstructing a function from a finite set of noise-corrupted samples. Two kernel algorithms are analyzed, namely kernel ridge regression and $\varepsil…
NSM Converges to a k-NN Regressor Under Loose Lipschitz Estimates
Emilio T. Maddalena, Colin N. Jones
Although it is known that having accurate Lipschitz estimates is essential for certain models to deliver good predictive performance, refining this constant in practice can be a di…
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,…
A Neural Network Architecture to Learn Explicit MPC Controllers from Data
E. T. Maddalena, C. G. da S. Moraes, G. Waltrich +1
We present a methodology to learn explicit Model Predictive Control (eMPC) laws from sample data points with tunable complexity. The learning process is cast in a special Neural Ne…
A Resource-Aware Approach to Self-Triggered Model Predictive Control: Extended Version
Stefan Wildhagen, Colin N. Jones, Frank Allgöwer
In this paper, we consider a self-triggered formulation of model predictive control. In this variant, the controller decides at the current sampling instant itself when the next sa…
An ADMM-based Coordination and Control Strategy for PV and Storage to Dispatch Stochastic Prosumers: Theory and Experimental Validation
Rahul Gupta, Fabrizio Sossan, Enrica Scolari +4
This paper describes a two-layer control and coordination framework for distributed energy resources. The lower layer is a real-time model predictive control (MPC) executed at 10 s…
Quantization Design for Distributed Optimization
Ye Pu, Melanie N. Zeilinger, Colin N. Jones
We consider the problem of solving a distributed optimization problem using a distributed computing platform, where the communication in the network is limited: each node can only…