Publications de Colin Neil Jones

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Two characteristics that make convex decomposition algorithms attractive are simplicity of operations and generation of parallelizable structures. In principle, these schemes require that all coordinates update at the same time, i.e., they are synchronous ...

Airborne wind energy systems are built to exploit the stronger and more consistent wind available at high altitudes that conventional wind turbines cannot reach. This however requires a reliable controller design that can keep the airborne system flying fo ...

In this paper, the problem of dispatching the operation of a distribution feeder comprising a set of heterogeneous resources is investigated. The main objective is to track a power trajectory, called the dispatch plan, which is computed the day before the ...

As the share of electricity generation from non-dispatchable sources like wind and photovoltaics grows, so does the need for demand response to stabilize the grid. Since the electricity consumption of heat pumps in buildings is both substantial and flexibl ...

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 ...

Wind resources tend to be significantly stronger and more consistent with increasing altitude. This effect creates a potential for power generation that can be reaped by an Airborne Wind Energy system positioned at elevations exceeding the height of conven ...

Recent results have suggested that online Model Predictive Control (MPC) can be computed quickly enough to control fast sampled systems. High-speed applications impose a hard real-time constraint on the solution of the MPC problem, which generally prevents ...

Deep Reinforcement Learning (DRL) recently emerged as a possibility to control complex systems without the need to model them. However, since weeks long experiments are needed to assess the performance of a building controller, people still have to rely on ...

Controlling the consumption profile of office buildings can be used to provide balancing services to the power grid at a financial benefit without violating its thermal comfort constraints. The economic advantage of such a service is a reduction in the tot ...

A stochastic model predictive control framework over unreliable Bernoulli communication channels, in the presence of unbounded process noise and under bounded control inputs, is presented for tracking a reference signal. The data losses in the control chan ...

In almost all algorithms for Model Predictive Control (MPC), the most time-consuming step is to solve some form of Linear Quadratic (LQ) Optimal Control Problem (OCP) repeatedly. The commonly recognized best option for this is a Riccati recursion based sol ...

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 sample should be taken and the optimization problem be solved anew. We incorpo ...

This paper describes an employee scheduling system for retail outlets; it is a constraint-based system that exploits forecasts and stochastic techniques to generate schedules meeting the demand for sales personnel. Uncertain scenarios due to fluctuating de ...

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 ...

Ancillary services constitute the cornerstone of the power grid. They allow for an efficient system operation, provide resilience to uncertainties and establish safeguards against unprecedented events. Their importance is growing due to the rise of grid de ...

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 ...

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 Network setting where the coefficients of two linear layers and a parametric q ...

Noise pollution from heat pumps (HPs) has been an emerging concern to their broader adoption, especially in densely populated areas. This paper explores a model predictive control (MPC) approach for climate control of buildings, aimed at minimizing the noi ...

This paper presents a method to verify closed-loop properties of optimization-based controllers for deterministic and stochastic constrained polynomial discrete-time dynamical systems. The closed-loop properties amenable to the proposed technique include g ...

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 difficult task especially when the input dimension is high. In this letter, we ...

Colin Neil Jones

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An Inertial Parallel and Asynchronous Forward-Backward Iteration for Distributed Convex Optimization

Optimization of an Airborne Wind Energy System using Constrained Gaussian Processes with Transient Measurements

Enhancing the dispatchability of distribution networks through utility-scale batteries and flexible demand

A commissioning-oriented approach to data-driven modeling of buildings with heat pumps for predictive demand response

Efficient Recursive Data-enabled Predictive Control

Optimization of an Airborne Wind Energy System Using Constrained Gaussian Processes

Real-time MPC - Stability through robust MPC design

Deep Reinforcement Learning for room temperature control: a black-box pipeline from data to policies

Economic Advantages of Office Buildings Providing Ancillary Services with Intraday Participation

Reference tracking stochastic model predictive control over unreliable channels and bounded control actions

Brunovsky Riccati Recursion for Linear Model Predictive Control

A Resource-Aware Approach to Self-Triggered Model Predictive Control

A two-stage stochastic programming approach to employee scheduling in retail outlets with uncertain demand

Self-triggered Control with Energy Harvesting Sensor Nodes

Providing ancillary service with commercial buildings: the Swiss perspective

Joint Energy Management for Distributed Energy Harvesting Systems

A Neural Network Architecture to Learn Explicit MPC Controllers from Data

Model predictive building climate control for mitigating heat pump noise pollution

Y Stability and performance verification of optimization-based controllers

NSM Converges to a k-NN Regressor Under Loose Lipschitz Estimates