Publications by Colin Neil Jones

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The complexity and diversity of future energy systems will require co-simulation solutions that enable the integration of tools from multiple domains for research and development. We introduce an open-source framework, OpenBuildNet, for distributed co-simu ...

A model predictive control law is given by the solution to a parametric optimization problem that can be pre- computed offline and provides an explicit map from state to control input. In this paper, an algorithm is introduced based on wavelet multiresolut ...

We propose a non-parametric regression method that does not rely on the structure of the ground-truth, but only on its regularity properties. The methodology can be readily used for learning surrogate models of nonlinear dynamical systems from data, while ...

Multi-energy microgrid (MEMG) has the potential to improve the energy utilization efficiency. However, the uncertainty caused by distributed renewable energy resources brings an urgent need for multi-energy co -optimization to ensure secure operation. This ...

Replacing poorly performing existing controllers with smarter solutions will decrease the energy intensity of the building sector. Recently, controllers based on Deep Reinforcement Learning (DRL) have been shown to be more effective than conventional basel ...

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 ...

This paper introduces a new enumeration technique for (multi)parametric linear programs (pLPs) based on the reverse-search paradigm. We prove that the proposed algorithm has a computational complexity that is linear in the size of the output (number of so- ...

This paper addresses the problem of keeping an autonomous marine vehicle in a moving triangular formation by regulating its position with respect to two leader vehicles. The follower vehicle has no prior knowledge of the path described by the leaders but h ...

Well-known model predictive control (MPC) theory for constrained linear time-invariant (LTI) systems is extended to accommodate hard constraints and cost penalizations on the spectra of the system’s output trajectories. Thus the proposed method facilitates ...

This paper presents a method to solve the constrained infinite-time linear quadratic regulator (LQR) problem. We use an operator splitting technique, namely the alternating minimization algorithm (AMA), to split the problem into an unconstrained LQR proble ...

In many applications, resource-aware devices are connected through a network, such as in the Internet of Things and energy hubs. These devices require proper coordination to achieve a high performance without violation of their resource limits. In this pap ...

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 ...

This article addresses the problem of designing a sensor fault-tolerant controller for an observation process where a primary, controlled system observes, through a set of measurements, an exogenous system to estimate the state of this system. We consider ...

In this paper, the problem of dispatching the operation of a distribution feeder comprising a set of heterogeneous resources is investigated. In particular, the main objective is to track a 5-minute resolution trajectory, called the \textit{dispatch plan} ...

This paper presents a new Matlab toolbox, aimed at facilitating the use of polynomial optimization for stability analysis of nonlinear systems. In the past decade several decisive contributions made it possible to recast this type of problems as convex opt ...

The linear complementarity problem (LCP) is a general problem that unifies linear and quadratic programs and bimatrix games. In this paper, we present an efficient algorithm for the solution to multiparametric linear complementarity problems (pLCPs) that a ...

Limits on the storage space or the computation time restrict the applicability of model predictive controllers (MPC) in many real problems. Currently available methods either compute the optimal controller online or derive an explicit control law. In this ...

We study the problem of performance optimization of closed -loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for improving closed -loop performance by automatically tuning controller gains or re ...

Robust state-feedback model predictive control (MPC) of discrete-time periodic affine systems is considered. States and inputs are subject to periodically time-dependent, hard, convex, polyhedral constraints. Disturbances are additive, bounded and subject ...

Enabling analysis of non-linear systems in linear form, the Koopman operator has been shown to be a powerful tool for system identification and controller design. However, current data-driven methods cannot provide quantification of model uncertainty given ...

Colin Neil Jones

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OpenBuildNet Framework for Distributed Co-Simulation of Smart Energy Systems

A multiresolution approximation method for fast explicit model predictive control

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

Data-driven distributionally robust joint chance-constrained energy management for multi-energy microgrid

Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control

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

Reverse Search for Parametric Linear Programming

Flexible triangular formation keeping of marine robotic vehicles using range measurements

Constrained spectrum control using MPC

Solving the infinite-horizon constrained LQR problem using splitting techniques

Resource-Aware Asynchronous Multi-Agent Coordination via Self-Triggered MPC

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

A nonlinear model predictive control scheme for sensor fault tolerance in observation processes

Dispatching active distribution networks through electrochemical storage systems and demand side management

SOStab: A Matlab toolbox for transient stability analysis

Multiparametric Linear Complementarity Problems

Real-time suboptimal model predictive control using a combination of explicit MPC and online optimization

Violation-aware contextual Bayesian optimization for controller performance optimization with unmodeled constraints

Least-restrictive robust MPC of periodic affine systems with application to building climate control

On Gaussian Process Based Koopman Operators