Recherche

Afficher tous les résultats pour

Accueil

Personne

Colin Neil Jones

À propos
Confidentialité
Mentions légales

Graph Chatbot

Publications associées (246)

Efficient Recursive Data-enabled Predictive Control

Colin Neil Jones, Yingzhao Lian, Jicheng Shi

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 ...

IEEE2024

Using Flexibility Envelopes for the Demand-Side Hierarchical Optimization of District Heating Networks

Colin Neil Jones

The demand-side control of district heating networks is notoriously challenging due to the large number of connected users and the high number of states to be considered. To overcome these challenges, this paper presents a hierarchical optimization scheme ...

Institute of Electrical and Electronics Engineers Inc.2024

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

Colin Neil Jones, Yingzhao Lian, Yuning Jiang

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, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Uncertainty-aware Flexibility Envelope Prediction in Buildings with Controller-agnostic Battery Models

Baptiste Schubnel, Colin Neil Jones, Rafael Eduardo Carrillo Rangel, Pierre-Jean Alet, Paul Scharnhorst

Buildings are a promising source of flexibility for the application of demand response. In this work, we introduce a novel battery model formulation to capture the state evolution of a single building. Being fully data-driven, the battery model identificat ...

IEEE2023

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

Colin Neil Jones, Loris Di Natale, 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 ...

2023

Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints

Colin Neil Jones, Yuning Jiang, Wenjie Xu, Bratislav Svetozarevic

This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances. A primal- ...

IEEE2023

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

Colin Neil Jones, Yingzhao Lian, Jicheng Shi

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Self-triggered Control with Energy Harvesting Sensor Nodes

Colin Neil Jones, Yingzhao Lian, Yuning Jiang

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 ...

ASSOC COMPUTING MACHINERY2023

Relaxed Recentered Log-Barrier Function based Nonlinear Model Predictive Control

Colin Neil Jones, Yuning Jiang

This paper investigates the use of relaxed recentered logarithmic barrier functions in the context of nonlinear model predictive control. These functions are a variation of the regular log-barrier functions that are introduced in the objective function of ...

IEEE2023

CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraints

Colin Neil Jones, Yuning Jiang, Wenjie Xu, Bratislav Svetozarevic

In this paper, the CONFIG algorithm, a simple and provably efficient constrained global optimization algorithm, is applied to optimize the closed-loop control performance of an unknown system with unmodeled constraints. Existing Gaussian process based clos ...

Elsevier2023

Physically Consistent Neural ODEs for Learning Multi-Physics Systems

Colin Neil Jones, Loris Di Natale, Giancarlo Ferrari Trecate, Bratislav Svetozarevic, Muhammad Zakwan

2023

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

Colin Neil Jones, Yingzhao Lian, Yuning Jiang

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 ...

Pergamon-Elsevier Science Ltd2023

PIQP: A Proximal Interior-Point Quadratic Programming Solver

Colin Neil Jones, Daniel Kuhn, Yuning Jiang, Roland Schwan

This paper presents PIQP, a high-performance toolkit for solving generic sparse quadratic programs (QP). Combining an infeasible Interior Point Method (IPM) with the Proximal Method of Multipliers (PMM), the algorithm can handle ill-conditioned convex QP p ...

IEEE2023

Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models

Colin Neil Jones, Loris Di Natale, Bratislav Svetozarevic

2023

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Colin Neil Jones, Melanie Nicole Zeilinger, Xuan Truong Nghiem, Roland Schwan

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

IEEE2023

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

Colin Neil Jones, Yingzhao Lian, Jicheng Shi

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Hypergraph-Based Fast Distributed AC Power Flow Optimization

Colin Neil Jones, Yingzhao Lian, Yuning Jiang, Xinliang Dai

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 ...

IEEE2023

A Generalized Stopping Criterion for Real-Time MPC with Guaranteed Stability

Colin Neil Jones, Yuning Jiang

IEEE2023

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

Colin Neil Jones, Yingzhao Lian, Emilio Maddalena, Giancarlo Ferrari Trecate

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 ...

2023

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules

Colin Neil Jones, Loris Di Natale, Bratislav Svetozarevic

Model-free Reinforcement Learning (RL) generally suffers from poor sample complexity, mostly due to the need to exhaustively explore the state-action space to find well-performing policies. On the other hand, we postulate that expert knowledge of the syste ...

2023

Page 2 sur 13