Publications by Colin Neil Jones

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Alternating direction method of multipliers (ADMM) is a form of augmented Lagrangian optimisation algorithm that found its place in many new applications in recent years. This paper explores a possibility for an upgrade of the ADMM by extrapolation-based a ...

In some of the recently developed algorithms for convex parametric quadratic programs it is implicitly assumed that the intersection of the closures of two adjacent critical regions is a facet of both closures; this will be referred to as the facet-to-face ...

Federated learning (FL) is an emerging technique that enables privacy-preserving distributed learning. Most related works focus on centralized FL, which leverages the coordination of a parameter server to implement local model aggregation. However, this sc ...

This paper looks at the efficiency of the parame- terisation of the degrees of freedom within an optimal predictive control algorithm. It is shown that the conventional approach of directly determining each individual future control move is not efficient i ...

We present the identification of the non-linear dynamics of a novel hovercraft design, employing end-to-end deep learning techniques. Our experimental setup consists of a hovercraft propelled by racing drone propellers mounted on a lightweight foam base, a ...

A novel Nonlinear Model Predictive Control (NMPC) scheme is proposed in order to shape the harmonic response of constrained nonlinear systems. The salient ingredient is the short-time Fourier transform (STFT) of the system's output signal, which is constra ...

In this technical note, a new model predictive controller for tracking arbitrary periodic references is presented. The proposed controller is based on a single layer that unites dynamic trajectory planning and control. A design procedure to guarantee that ...

In this paper we use bilevel programming to find the maximum difference between a reference controller and a low-complexity controller in terms of the infinity-norm difference of their control laws. A nominal MPC for linear systems with constraints, and a ...

This paper proposes a real-time distributed operational architecture to coordinate integrated transmission and distribution systems (ITD). At the distribution system level, the distribution system operator (DSO) calculates the aggregated flexibility of all ...

This paper presents an investigation of how Model Predictive Control (MPC) and weather predictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ...

In this paper, an algorithm is introduced based on classical wavelet multiresolution analysis that returns a low complexity explicit model predictive control law built on a hierarchy of second order interpolating wavelets. It is proven that the resulting i ...

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 ...

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 ...

Fast model predictive control on embedded sys- tems has been successfully applied to plants with microsecond sampling times employing a precomputed state-to-input map. However, the complexity of this so-called explicit MPC can be prohibitive even for low-d ...

The application of nuclear norm regularization to system identification was recently shown to be a useful method for identifying low order linear models. In this paper, we consider nuclear norm regularization for identification of LTI systems with missing ...

Frequency control reserves are a fundamental mechanism in the current electric power system to preserve the balance between demand and generation of active power at all times. The traditional approach is to exploit conventional power plants for frequency r ...

Integrating heating and electricity networks offers extra flexibility to the energy system operation while improving energy utilization efficiency. This paper proposes a data-driven joint distributionally robust chance-constrained (DRCC) operation model fo ...

Predictive control is a flexible control methodology that can optimize performance while satisfying current and voltage constraints. Its application in the power electronics domain is however hampered by the high computational demands associated with it. I ...

During the energy transition, the significance of collaborative management among institutions is rising, confronting challenges posed by data privacy concerns. Prevailing research on distributed approaches, as an alternative to centralized management, ofte ...

Colin Neil Jones

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Accelerated ADMM based on Accelerated Douglas-Rachford Splitting

On the facet-to-facet property of solutions to convex parametric quadratic programs

Decentralized Over-the-Air Federated Learning by Second-Order Optimization Method

Alternative parameterisations for predictive control: How and why?

On identifying the non-linear dynamics of a hovercraft using an end-to-end deep learning approach

Constrained Spectrum Control

MPC for Tracking Periodic References

Bilevel programming for analysis of low-complexity control of linear systems with constraints

Real-time coordination of integrated transmission and distribution systems: Flexibility modeling and distributed NMPC scheduling

Increasing energy efficiency in building climate control using weather forecasts and model predictive control

A multiscale approximation scheme for explicit model predictive control with stability, feasibility, and performance guarantees

Computationally Efficient Reinforcement Learning: Targeted Exploration leveraging Simple Rules

Learning Near-optimal Decision Rules for Energy Efficient Building Control

Learning a feasible and stabilizing explicit model predictive control law by robust optimization

System identification with missing data via nuclear norm regularization

Experimental Implementation of Frequency Regulation Service Using Commercial Buildings

Robust Uncertainty Bounds in Reproducing Kernel Hilbert Spaces: A Convex Optimization Approach

Data-Driven Joint Distributionally Robust Chance-Constrained Operation for Multiple Integrated Electricity and Heating Systems

Embedded PWM Predictive Control of DC-DC Power Converters Via Piecewise-Affine Neural Networks

Ensuring data privacy in AC Optimal Power Flow with a distributed co-simulation framework