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View article: Partitioning techniques for non-centralized predictive control: A systematic review and novel theoretical insights
Partitioning techniques for non-centralized predictive control: A systematic review and novel theoretical insights Open
The partitioning problem is of central relevance for designing and implementing non-centralized Model Predictive Control (MPC) strategies for large-scale systems. These control approaches include decentralized MPC, distributed MPC, hierarc…
View article: Robust Adaptive Discrete-Time Control Barrier Certificate
Robust Adaptive Discrete-Time Control Barrier Certificate Open
This work develops a robust adaptive control strategy for discrete-time systems using Control Barrier Functions (CBFs) to ensure safety under parametric model uncertainty and disturbances. A key contribution of this work is establishing a …
View article: WaveletInception Networks for Drive-by Vibration-Based Infrastructure Health Monitoring
WaveletInception Networks for Drive-by Vibration-Based Infrastructure Health Monitoring Open
This paper presents a novel deep learning-based framework for infrastructure health monitoring using drive-by vibration response signals. Recognizing the importance of spectral and temporal information, we introduce the WaveletInception-Bi…
View article: Bidirectional Long Short-Term Memory Approach for Infrastructure Health Monitoring Using On-Board Vibration Response
Bidirectional Long Short-Term Memory Approach for Infrastructure Health Monitoring Using On-Board Vibration Response Open
The growing volume of available infrastructural monitoring data enables the development of powerful data-driven approaches to estimate infrastructure health conditions using direct measurements. This paper proposes a deep learning methodol…
View article: Uncertainty Partitioning with Probabilistic Feasibility and Performance Guarantees for Chance-Constrained Optimization
Uncertainty Partitioning with Probabilistic Feasibility and Performance Guarantees for Chance-Constrained Optimization Open
We propose a novel distribution-free scheme to solve optimization problems where the goal is to minimize the expected value of a cost function subject to probabilistic constraints. Unlike standard sampling-based methods, our idea consists …
View article: From learning to safety: A Direct Data-Driven Framework for Constrained Control
From learning to safety: A Direct Data-Driven Framework for Constrained Control Open
Ensuring safety in the sense of constraint satisfaction for learning-based control is a critical challenge, especially in the model-free case. While safety filters address this challenge in the model-based setting by modifying unsafe contr…
View article: Distributed model predictive control without terminal cost under inexact distributed optimization
Distributed model predictive control without terminal cost under inexact distributed optimization Open
This paper presents a novel distributed model predictive control (MPC) formulation without terminal cost and a corresponding distributed synthesis approach for distributed linear discrete-time systems with coupled constraints. The proposed…
View article: Probabilistically safe and efficient model-based reinforcement learning
Probabilistically safe and efficient model-based reinforcement learning Open
This paper proposes tackling safety-critical stochastic Reinforcement Learning (RL) tasks with a sample-based, model-based approach. At the core of the method lies a Model Predictive Control (MPC) scheme that acts as function approximation…
View article: Learning-Based MPC for Fuel Efficient Control of Autonomous Vehicles with Discrete Gear Selection
Learning-Based MPC for Fuel Efficient Control of Autonomous Vehicles with Discrete Gear Selection Open
Co-optimization of both vehicle speed and gear position via model predictive control (MPC) has been shown to offer benefits for fuel-efficient autonomous driving. However, optimizing both the vehicle's continuous dynamics and discrete gear…
View article: Optimization-based Coordination of Traffic Lights and Automated Vehicles at Intersections
Optimization-based Coordination of Traffic Lights and Automated Vehicles at Intersections Open
This paper tackles the challenge of coordinating traffic lights and automated vehicles at signalized intersections, formulated as a constrained finite-horizon optimal control problem. The problem falls into the category of mixed-integer no…
View article: Integrating Reinforcement Learning and Model Predictive Control for Mixed- Logical Dynamical Systems
Integrating Reinforcement Learning and Model Predictive Control for Mixed- Logical Dynamical Systems Open
This work proposes an approach that integrates reinforcement learning (RL) and model predictive control (MPC) to solve finite-horizon optimal control problems in mixed-logical dynamical systems efficiently. Optimization-based control of su…
View article: Reinforcement learning-based model predictive control for greenhouse climate control
Reinforcement learning-based model predictive control for greenhouse climate control Open
Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inp…
View article: Certainty-Equivalence Model Predictive Control: Stability, Performance, and Beyond
Certainty-Equivalence Model Predictive Control: Stability, Performance, and Beyond Open
Handling model mismatch is a common challenge in model-based controller design, particularly in model predictive control (MPC). While robust MPC is effective in managing uncertainties, its conservatism often makes it less desirable in prac…
View article: Nonmyopic Global Optimisation via Approximate Dynamic Programming
Nonmyopic Global Optimisation via Approximate Dynamic Programming Open
Unconstrained global optimisation aims to optimise expensive-to-evaluate black-box functions without gradient information. Bayesian optimisation, one of the most well-known techniques, typically employs Gaussian processes as surrogate mode…
View article: A Bidirectional Long Short Term Memory Approach for Infrastructure Health Monitoring Using On-board Vibration Response
A Bidirectional Long Short Term Memory Approach for Infrastructure Health Monitoring Using On-board Vibration Response Open
The growing volume of available infrastructural monitoring data enables the development of powerful datadriven approaches to estimate infrastructure health conditions using direct measurements. This paper proposes a deep learning methodolo…
View article: Learning-Based Model Predictive Control for Piecewise Affine Systems with Feasibility Guarantees
Learning-Based Model Predictive Control for Piecewise Affine Systems with Feasibility Guarantees Open
Online model predictive control (MPC) for piecewise affine (PWA) systems requires the online solution to an optimization problem that implicitly optimizes over the switching sequence of PWA regions, for which the computational burden can b…
View article: Iterative Cut-Based PWA Approximation of Multi-Dimensional Nonlinear Systems
Iterative Cut-Based PWA Approximation of Multi-Dimensional Nonlinear Systems Open
PieceWise Affine (PWA) approximations for nonlinear functions have been extensively used for tractable, computationally efficient control of nonlinear systems. However, reaching a desired approximation accuracy without prior information ab…
View article: Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control
Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control Open
Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inp…
View article: Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids
Integrating Reinforcement Learning and Model Predictive Control with Applications to Microgrids Open
This work proposes an approach that integrates reinforcement learning and model predictive control (MPC) to solve finite-horizon optimal control problems in mixed-logical dynamical systems efficiently. Optimization-based control of such sy…