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Colin Neil Jones

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Related publications (246)

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

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 ...

2020

PolyMPC: An efficient and extensible tool for real‐time nonlinear model predictive tracking and path following for fast mechatronic systems

Colin Neil Jones, Petr Listov

This paper presents PolyMPC, an open‐source C++ library for pseudospectral‐based real‐time predictive control of nonlinear systems. It provides a necessary background on the computational aspects of the pseudospectral approximation of optimal control probl ...

2020

A Neural Network Architecture to Learn Explicit MPC Controllers from Data

Colin Neil Jones, Emilio Maddalena

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 ...

ELSEVIER2020

Distributed Multi-Building Coordination for Demand Response

Colin Neil Jones, Altug Bitlislioglu

This paper presents a distributed optimization algorithm tailored for solving optimal control problems arising in multi-building coordination. The buildings coordinated by a grid operator, join a demand response program to balance the voltage surge by usin ...

ELSEVIER2020

Regularized economic model predictive control with barrier functions

Colin Neil Jones, Altug Bitlislioglu

In this article, we consider the use of barrier functions as a regularizing cost in economic model predictive control (EMPC). We focus on a specific variant, EMPC with generalized terminal constraints (G-EMPC), as it is suitable for tackling large-scale pr ...

WILEY2020

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

Colin Neil Jones, Emilio Maddalena

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 ...

ELSEVIER2020

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

Colin Neil Jones, Yingzhao Lian, Yuning Jiang

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 ...

IEEE2020

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

Colin Neil Jones

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 ...

ELSEVIER2020

Experimental Verification of Sum-Of-Squares-Based Controller Tuning Technique with Extension to Parallel Multimodel Uncertainty Processing

Colin Neil Jones, Ivan Pejcic

Many control schemes require significant tuning effort to achieve desired performance targets, causing a need for general tools that can perform controller tuning in an automated fashion. This paper represents a continuation of the previous work in which a ...

IEEE2019

System and method for operating a combination of heterogeneous energy resources for fast grid services

Colin Neil Jones, Tomasz Tadeusz Gorecki, Faran Ahmed Qureshi, Ioannis Lymperopoulos, Luca Fabietti

The present invention concerns a method of controlling a set of electrical power resources in an electrical power grid managed by a power grid operator. The set comprises a first electrical power resource with a first electrical power response frequency an ...

2019

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

Colin Neil Jones, Yuning Jiang

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 ...

IEEE2019

Recurrent Neural Network based MPC for Process Industries

Colin Neil Jones

Autonomous operation of industrial plants requires a cheap and efficient way of creating dynamic process models, which can then be used to either be part of the autonomous systems or to serve as simulators for reinforcement learning. The trends of digitali ...

IEEE2019

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

Colin Neil Jones, Emilio Maddalena

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 ...

2019

Learning Feature Maps of the Koopman Operator: A Subspace Viewpoint

Colin Neil Jones, Yingzhao Lian

The Koopman operator was recently shown to be a useful method for nonlinear system identification and controller design. However, the scalability of current data-driven approaches is limited by the selection of feature maps. In this paper, we present a new ...

IEEE2019

Distributed state estimation for discrete-time linear time invariant systems: A survey

Colin Neil Jones, Francisco Fernandes Castro Rego

Motivated by the increasing availability and quality of miniaturized sensors, computers, and wireless communication devices arid given their enormous potential, the use of wireless sensor networks (WSN) has become widespread. Because in many applications o ...

2019

A Generalized Representer Theorem for Hilbert Space - Valued Functions

Colin Neil Jones, Sanket Sanjay Diwale

The necessary and sufficient conditions for existence of a generalized representer theorem are presented for learning Hilbert space - valued functions. Representer theorems involving explicit basis functions and Reproducing Kernels are a common occurrence ...

2019

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

Georgios Stathopoulos, Colin Neil Jones

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 ...

SPRINGER/PLENUM PUBLISHERS2019

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

Colin Neil Jones, Ye Pu

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 ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Manifold Learning and Optimal Control for Obstacle Avoidance in Autonomous Driving

Colin Neil Jones, Sanket Sanjay Diwale

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 ...

2019

An ADMM-Based Coordination and Control Strategy for PV and Storage to Dispatch Stochastic Prosumers: Theory and Experimental Validation

Mario Paolone, Colin Neil Jones, Emil Namor, Fabrizio Sossan, Enrica Scolari, Luca Fabietti, Rahul Kumar Gupta

This paper describes a two-layer control and coordination framework for distributed energy resources. The lower layer is a real-time model predictive control (MPC) executed at 10 s resolution to achieve fine tuning of a given energy setpoint. The upper lay ...

Power Systems Computation Conference2018

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