Papers Model Predictive Control
“Model Predictive Control” 태그가 달린 논문 1,582편 · 필터 해제
A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification
Solving chance-constrained optimal control problems for systems subject to non-stationary uncertainties is a significant challenge.Conventional robust model predictive control (MPC) often yields excessive conservatism by…
Active LearningModel Predictive ControlUncertainty QuantificationContinual Reinforcement Learning by Planning with Online World Models
Continual reinforcement learning (CRL) refers to a naturalistic setting where an agent needs to endlessly evolve, by trial and error, to solve multiple tasks that are presented sequentially. One of the largest obstacles …
Continual LearningModel Predictive Controlreinforcement-learningReinforcement LearningCoordinated Control of Autonomous Vehicles for Traffic Density Reduction at a Signalized Junction: An MPC Approach
The effective and safe management of traffic is a key issue due to the rapid advancement of the urban transportation system. Connected autonomous vehicles (CAVs) possess the capability to connect with each other and adja…
Autonomous VehiclesDecision MakingManagementModel Predictive ControlLearning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control
We propose a novel approach to solving input- and state-constrained parametric mixed-integer optimal control problems using Differentiable Predictive Control (DPC). Our approach follows the differentiable programming par…
Model Predictive ControlSelf-Supervised LearningAgenticControl: An Automated Control Design Framework Using Large Language Models
Traditional control system design, reliant on expert knowledge and precise models, struggles with complex, nonlinear, or uncertain dynamics. This paper introduces AgenticControl, a novel multi-agent framework that automa…
In-Context LearningLarge Language ModelModel Predictive ControlOptimal Design of Experiment for Electrochemical Parameter Identification of Li-ion Battery via Deep Reinforcement Learning
Accurate parameter estimation in electrochemical battery models is essential for monitoring and assessing the performance of lithium-ion batteries (LiBs). This paper presents a novel approach that combines deep reinforce…
Deep Reinforcement LearningExperimental DesignModel Predictive Controlparameter estimationDynamic Hybrid Modeling: Incremental Identification and Model Predictive Control
Mathematical models are crucial for optimizing and controlling chemical processes, yet they often face significant limitations in terms of computational time, algorithm complexity, and development costs. Hybrid models, w…
Model Predictive Controlparameter estimationA Scenario-based Model Predictive Control Scheme for Pandemic Response through Non-pharmaceutical Interventions
This paper presents a scenario-based model predictive control (MPC) scheme designed to control an evolving pandemic via non-pharmaceutical intervention (NPIs). The proposed approach combines predictions of possible pande…
Model Predictive ControlA tutorial overview of model predictive control for continuous crystallization: current possibilities and future perspectives
This paper presents a systematic approach to the advanced control of continuous crystallization processes using model predictive control. We provide a tutorial introduction to controlling complex particle size distributi…
Model Predictive ControlEvaluating the Impact of Model Accuracy for Optimizing Battery Energy Storage Systems
This study investigates two models of varying complexity for optimizing intraday arbitrage energy trading of a battery energy storage system using a model predictive control approach. Scenarios reflecting different stage…
energy tradingModel Predictive ControlReimagination with Test-time Observation Interventions: Distractor-Robust World Model Predictions for Visual Model Predictive Control
World models enable robots to "imagine" future observations given current observations and planned actions, and have been increasingly adopted as generalized dynamics models to facilitate robot learning. Despite their pr…
modelModel Predictive ControlModel Predictive Path-Following Control for a Quadrotor
Automating drone-assisted processes is a complex task. Many solutions rely on trajectory generation and tracking, whereas in contrast, path-following control is a particularly promising approach, offering an intuitive an…
Model Predictive ControlContext-Aware Deep Lagrangian Networks for Model Predictive Control
Controlling a robot based on physics-informed dynamic models, such as deep Lagrangian networks (DeLaN), can improve the generalizability and interpretability of the resulting behavior. However, in complex environments, t…
Model Predictive ControlMicrogrid Operation Control with Adaptable Droop Gains
Modern low-carbon power systems come with many challenges, such as increased inverter penetration and increased uncertainty from renewable sources and loads. In this context, the microgrid concept is a promising approach…
Model Predictive ControlParallel Branch Model Predictive Control on GPUs
We present a parallel GPU-accelerated solver for branch Model Predictive Control problems. Based on iterative LQR methods, our solver exploits the tree-sparse structure and implements temporal parallelism using the paral…
CPUGPUmodelModel Predictive ControlRL-Guided MPC for Autonomous Greenhouse Control
The efficient operation of greenhouses is essential for enhancing crop yield while minimizing energy costs. This paper investigates a control strategy that integrates Reinforcement Learning (RL) and Model Predictive Cont…
Model Predictive ControlReinforcement Learning (RL)Transient performance of MPC for tracking without terminal constraints
Model predictive control (MPC) for tracking is a recently introduced approach, which extends standard MPC formulations by incorporating an artificial reference as an additional optimization variable, in order to track ex…
Model Predictive ControlModel Predictive Control-Based Optimal Energy Management of Autonomous Electric Vehicles Under Cold Temperatures
In autonomous electric vehicles (AEVs), battery energy must be judiciously allocated to satisfy primary propulsion demands and secondary auxiliary demands, particularly the Heating, Ventilation, and Air Conditioning (HVA…
energy managementManagementModel Predictive ControlRe4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
Traditional motion planning methods for robots with many degrees-of-freedom, such as mobile manipulators, are often computationally prohibitive for real-world settings. In this paper, we propose a novel multi-model motio…
Decision MakingDeep Reinforcement LearningModel Predictive ControlMotion PlanningOnline Learning Control Strategies for Industrial Processes with Application for Loosening and Conditioning
This paper proposes a novel adaptive Koopman Model Predictive Control (MPC) framework, termed HPC-AK-MPC, designed to address the dual challenges of time-varying dynamics and safe operation in complex industrial processe…
Model Predictive Control