Publications de Colin Neil Jones

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Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many edge devices to collaboratively train a global model without sharing their private data. To enhance the training efficiency of FL, various algorithms have been prop ...

This paper describes synthesis of controllers involving Quadratic Programming (QP) optimization problems for control of nonlinear systems. The QP structure allows an implementation of the controller as a piecewise affine function, pre-computed offline, whi ...

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 ...

This paper focuses on the distributionally robust dispatch for integrated transmission-distribution (ITD) systems via distributed optimization. Existing distributed algorithms usually require synchronization of all subproblems, which could be hard to scale ...

With technological advances, the number of cores integrated on a chip is increasing. This, in turn is leading to thermal constraints and thermal design challenges. Temperature gradients and hot-spots not only affect the performance of the system, but also ...

The rapid uptake of natural gas-fired units in energy systems poses significant challenges in coordinating the electricity and gas systems. Besides, the uncertainty caused by integrated renewable energy such as wind power raises more requirements on the ro ...

This paper introduces a distributed operational solution for integrated transmission-distribution (ITD) system management. A fundamental challenge in designing distributed approaches for AC optimal power flow (OPF) problems in ITD systems is the nonconvexi ...

This paper addresses the problem of overloading in power distribution networks, which stems from the transmission systems being incapable of delivering power from source to consumers during peak hours. This causes frequent power-outages (or blackouts), req ...

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 coordinated primal-dual interior point (PDIP) method for solving structured convex linear and quadratic programs (LP-QP) in a distributed man- ner. The considered class of problems represents a multi-agent setting, where the aggregate ...

Recent studies suggest that advanced optimization based control methods such as model predictive control (MPC) can increase energy efficiency of buildings. However, adoption of these methods by industry is still slow, as building operators are used to work ...

Model predictive control (MPC) strategies can be applied to the coordination of energy hubs to reduce their energy consumption. Despite the effectiveness of these techniques, their potential for energy savings are potentially underutilized due to the fact ...

This work presents an algorithmic scheme for solving the infinite-time constrained linear quadratic regulation problem. We employ an accelerated version of a popular proximal gradient scheme, commonly known as the Forward-Backward Splitting (FBS), and prov ...

We consider an unconstrained distributed optimization problem and assume that the bit rate of the communication in the network is limited. We propose a distributed optimization algorithm with an iteratively refining quantization design, which bounds the qu ...

Flexibility, particularly in terms of reserve services, is an essential requirement of power systems with high penetration of renewable electrical generation, which can reduce undesirable curtailment and enable higher integration of clean electrical power ...

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 adopted approach to guaranteeing thermal comfort and reducing electrical energy ...

We propose a non-parametric regression methodology that enforces the regressor to be fully consistent with the sample set and the ground-truth regularity assumptions. As opposed to the Nonlinear Set Membership technique, this constraint guarantees the atta ...

State-feedback model predictive control (MPC) of discrete-time linear periodic systems with possibly time-dependent state and control input dimension is considered. States and inputs are subject to hard, mixed, polytopic constraints. It is described how di ...

Research on sub-optimal Model Predictive Control (MPC) has led to a variety of optimization methods based on explicit or online approaches, or combinations thereof. Its foremost aim is to guarantee essential controller properties, i.e. recursive feasibilit ...

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 ...

Colin Neil Jones

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Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning

Technical Report: Control of Nonlinear Systems with Explicit-MPC-like Controllers

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

Distributionally Robust Joint Chance-Constrained Dispatch for Integrated Transmission-Distribution Systems via Distributed Optimization

Online Thermal Control Methods for Multi-Processor Systems

Distributed adjustable robust optimal power-gas flow considering wind power uncertainty

Advancing Distributed AC Optimal Power Flow for Integrated Transmission-Distribution Systems

A Demand Response-Based Solution to Overloading in Underdeveloped Distribution Networks

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

On Coordinated Primal-Dual Interior-Point Methods for Multi-Agent Optimization

Learning Near-optimal Decision Rules for Energy Efficient Building Control

Distributed model predictive control of buildings and energy hubs

Solving the Infinite-horizon Constrained LQR Problem using Accelerated Dual Proximal Methods

Quantization Design for Unconstrained Distributed Optimization

Use of model predictive control for short-term operating reserve using commercial buildings in the United Kingdom context

Experimental data-driven model predictive control of a hospital HVAC system during regular use

Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Regression Approach

Model predictive control of linear periodic systems - a unified framework including control of multirate and multiplexed systems

Input-to-state stabilization of feasible model predictive controllers for linear systems

Hypergraph-Based Fast Distributed AC Power Flow Optimization