Publications de Colin Neil Jones

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This paper introduces a framework for solving time-autonomous nonlinear infinite horizon optimal control problems, under the assumption that all minimizers satisfy Pontryagin's necessary optimality conditions. In detail, we use methods from the field of sy ...

Machine Learning (ML) and linear System Identification (SI) have been historically developed independently. In this paper, we leverage well-established ML tools - especially the automatic differentiation framework - to introduce SIMBa, a family of discrete ...

While rule based control (RBC) is current practice in most building automation systems that issue discrete control signals, recent simulation studies suggest that advanced, optimization based control methods such as hybrid model predictive control (HMPC) c ...

This paper focuses on the design of an asynchronous dual solver suitable for model predictive control (MPC) applications. The proposed solver relies on a state-of-the-art variance reduction (VR) scheme, previously used in the context of proximal stochastic ...

We demonstrate how charging infrastructure for electrical vehicles can be utilized for the provision of Ancillary Services to the Power Grid. Fast- charging stations that supply energy to vehicles at high power rates in- corporate energy storage to prevent ...

This paper proposes a parallelizable real-time algorithm for model predictive control (MPC). In contrast to existing distributed and parallel optimization algorithms for linear MPC such as dual decomposition or the alternating direction method of multiplie ...

Many real-time optimization schemes maximize process performance by performing a model-based optimization. However, due to plant-model mismatch, the model-based solution is often suboptimal. In modifier adaptation, measurements are used to correct the mode ...

Several distributed-optimization setups involve a group of agents coordinated by a central entity (coordinator), altogether operating in a collaborative framework. In such environments, it is often common that the agents solve proximal minimization problem ...

Explicit Model Predictive Control (EMPC) produces control laws defined over a set of polytopic regions in the state space. In this paper we present a method to create a binary search tree for point location in such polytopic sets, in order to provide a fas ...

While stability analysis is a mainstay for control science, especially computing regions of attraction of equilibrium points, until recently most stability analysis tools always required explicit knowledge of the model or a high-fidelity simulator represen ...

Modern energy infrastructure has evolved into an integrated electricity and natural gas systems (IEGS), which often encompasses multiple geographically-diverse energy areas. This paper focuses on the decentralized adjustable robust operation problem for mu ...

An explicit (or closed-form) solution to Model Predictive Control (MPC) results in a polyhedral subdivision of the state-space when the system and constraints are linear, and the cost is linear or quadratic. Within each region the optimal control law is an ...

This paper investigates the effect of different control strategies applied to electric thermal storage systems to provide demand response services. These results indicate how policymakers or manufacturers could target the implementation of advanced control ...

State-feedback model predictive control (MPC) of constrained discrete-time periodic affine systems is considered. The periodic systems’ states and inputs are subject to periodically time-dependent, hard, polyhedral constraints. Disturbances are additive, b ...

By providing various services, such as Incentive-based Demand Response (IBDR), buildings can play a crucial role in the energy market due to their significant energy consumption. However, effectively commissioning buildings for such desired functionalities ...

A novel manifold learning approach is presented to incorporate computationally efficient obstacle avoidance constraints in optimal control algorithms. The method presented provides a significant computational benefit by reducing the number of constraints r ...

This manuscript details and extends the system identification methods leveraging the backpropagation (SIMBa) toolbox presented in previous work, which uses well-established machine learning tools for discrete-time linear multistep-ahead state-space system ...

This letter proposes a data-driven input reconstruction method from outputs (IRO) based on the Willems' Fundamental Lemma. Given only output measurements, the unknown inputsestimated recursively by the IRO asymptotically converge to the true input without ...

In this brief paper, a Stochastic Model Predictive Control formulation tractable for large-scale systems is developed. The proposed formulation combines the use of Affine Disturbance Feedback, a formulation successfully applied in robust control, with a de ...

A method for certifying exact input trackability for constrained discrete time linear systems is introduced in this paper. A signal is assumed to be drawn from a reference set and the system must track this signal with a linear combination of its inputs. U ...

Colin Neil Jones

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Towards global optimal control via Koopman lifts

Stable Linear Subspace Identification: A Machine Learning Approach

Learning decision rules for energy efficient building control

SVR-AMA: An Asynchronous Alternating Minimization Algorithm With Variance Reduction for Model Predictive Control Applications

Provision of Ancillary Services utilizing a Network of Fast-Charging Stations for Electrical Buses

A Time Splitting Based Real-Time Iteration Scheme for Nonlinear MPC

Active Directional Modifier Adaptation with Trust Region - Application to Energy-Harvesting Kites

Learning Proximal Operators with Gaussian Processes

Optimal decision trees for point location in polytopic data sets - application to explicit MPC

Robustly Learning Regions of Attraction From Fixed Data

Asynchronous decentralized adjustable robust operation for multi-area integrated electricity-gas systems considering wind power uncertainty

Efficient point location via subdivision walking with application to explicit MPC

Evaluation of advanced control strategies of electric thermal storage systems in residential building stock

Least-restrictive robust periodic model predictive control applied to room temperature regulation

Adaptive data-driven prediction in a building control hierarchy: A case study of demand response in Switzerland

Manifold Learning and Optimal Control for Obstacle Avoidance in Autonomous Driving

SIMBa: System Identification Methods Leveraging Backpropagation

Data-Driven Input Reconstruction and Experimental Validation

Stochastic Model Predictive Control for Building Climate Control

Guaranteeing Input Tracking For Constrained Systems: Theory and Application to Demand Response