Papers Model Predictive Control
“Model Predictive Control” 태그가 달린 논문 1,582편 · 필터 해제
An Automatic Tuning MPC with Application to Ecological Cruise Control
Model predictive control (MPC) is a powerful tool for planning and controlling dynamical systems due to its capacity for handling constraints and taking advantage of preview information. Nevertheless, MPC performance is …
Model Predictive ControlClosing the Loop on Runtime Monitors with Fallback-Safe MPC
When we rely on deep-learned models for robotic perception, we must recognize that these models may behave unreliably on inputs dissimilar from the training data, compromising the closed-loop system's safety. This raises…
Conformal PredictionModel Predictive ControlAutomated Multi-Drugs Administration During Total Intravenous Anesthesia Using Multi-Model Predictive Control
In this paper, a multi-model predictive control approach is used to automate the co-administration of propofol and remifentanil from bispectral index measurement during general anesthesia. To handle the parameter uncerta…
Model Predictive ControlEfficient online update of model predictive control in embedded systems using first-order methods
Model Predictive Control (MPC) is typically characterized for being computationally demanding, as it requires solving optimization problems online; a particularly relevant point when considering its implementation in emb…
Model Predictive ControlUngar $\unicode{x2013}$ A C++ Framework for Real-Time Optimal Control Using Template Metaprogramming
We present Ungar, an open-source library to aid the implementation of high-dimensional optimal control problems (OCPs). We adopt modern template metaprogramming techniques to enable the compile-time modeling of complex s…
Code GenerationModel Predictive ControlA Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning
Online reinforcement learning (RL) methods are often data-inefficient or unreliable, making them difficult to train on real robotic hardware, especially quadruped robots. Learning robotic tasks from pre-collected data is…
Model Predictive Controlreinforcement-learningReinforcement LearningReinforcement Learning (RL)Data-Driven Model Reduction and Nonlinear Model Predictive Control of an Air Separation Unit by Applied Koopman Theory
Achieving real-time capability is an essential prerequisite for the industrial implementation of nonlinear model predictive control (NMPC). Data-driven model reduction offers a way to obtain low-order control models from…
CPUmodelModel Predictive ControlNonlinear Model Predictive Control for Uranium Extraction-Scrubbing Operation in Spent Nuclear Fuel Treatment Process
This paper addresses the particularities of the uranium extraction-scrubbing operation in a spent nuclear fuel treatment process (PUREX-Plutonium Uranium Refining by Extraction) through the use of set-point tracking MPC …
Model Predictive ControlPoint TrackingA Minimum-propellant Pontryagin-based Nonlinear MPC for Spacecraft Rendezvous in Lunar Orbit: the Extended Version
We propose a Nonlinear Model Predictive Control approach to spacecraft rendezvous in non-Keplerian Lunar orbits. The approach is based on the Pontryagin Minimum Principle and allows the accomplishment of minimum-propella…
Model Predictive ControlRobust Nonlinear Reduced-Order Model Predictive Control
Real-world systems are often characterized by high-dimensional nonlinear dynamics, making them challenging to control in real time. While reduced-order models (ROMs) are frequently employed in model-based control schemes…
Dimensionality ReductionmodelModel Predictive ControlSignal Temporal Logic Neural Predictive Control
Ensuring safety and meeting temporal specifications are critical challenges for long-term robotic tasks. Signal temporal logic (STL) has been widely used to systematically and rigorously specify these requirements. Howev…
Model Predictive ControlReinforcement Learning (RL)A Generalized Stopping Criterion for Real-Time MPC with Guaranteed Stability
Most of the real-time implementations of the stabilizing optimal control actions suffer from the necessity to provide high computational effort. This paper presents a cutting-edge approach for real-time evaluation of lin…
Model Predictive ControlManipulation of Neuronal Network Firing Patterns using Temporal Deep Unfolding-based MPC
Because neuronal networks are intricate systems composed of interconnected neurons, their control poses challenges owing to their nonlinearity and complexity. In this paper, we propose a method to design control input to…
Model Predictive ControlNeural Networks for Fast Optimisation in Model Predictive Control: A Review
Model Predictive Control (MPC) is an optimal control algorithm with strong stability and robustness guarantees. Despite its popularity in robotics and industrial applications, the main challenge in deploying MPC is its h…
BenchmarkingModel Predictive ControlStochastic Model Predictive Control for Networked Systems with Random Delays and Packet Losses in All Channels
A stochastic Model Predictive Control strategy for control systems with communication networks between the sensor node and the controller and between the controller and the actuator node is proposed. Data packets are sub…
AllModel Predictive ControlScenario-based model predictive control of water reservoir systems
The optimal operation of water reservoir systems is a challenging task involving multiple conflicting objectives. The main source of complexity is the presence of the water inflow, which acts as an exogenous, highly unce…
Model Predictive ControlLearning Robust Model Predictive Control for Voltage Control of Islanded Microgrid
This paper proposes a novel control design for voltage tracking of an islanded AC microgrid in the presence of {nonlinear} loads and parametric uncertainties at the primary level of control. The proposed method is based …
Model Predictive ControlExperimental validation of an explicit flatness-based MPC design for quadcopter position tracking
Due to the nonlinearities and operational constraints typical to quadcopter missions, Model Predictive Control (MPC) encounters the major challenge of high computational power necessary for the online implementation. Thi…
Model Predictive ControlPositionOn the improvement of model-predictive controllers
This article investigates synthetic model-predictive control (MPC) problems to demonstrate that an increased precision of the internal prediction model (PM) automatially entails an improvement of the controller as a whol…
modelModel Predictive ControlReinforcement Learning (RL)Robust Machine Learning Modeling for Predictive Control Using Lipschitz-Constrained Neural Networks
Neural networks (NNs) have emerged as a state-of-the-art method for modeling nonlinear systems in model predictive control (MPC). However, the robustness of NNs, in terms of sensitivity to small input perturbations, rema…
Chemical ProcessModel Predictive ControlSensitivity