Papers Model Predictive Control
“Model Predictive Control” 태그가 달린 논문 1,582편 · 필터 해제
What is the Best Way to Optimally Parameterize the MPC Cost Function for Vehicle Guidance?
Model predictive control (MPC) is a promising approach for the lateral and longitudinal control of autonomous vehicles. However, the parameterization of the MPC with respect to high-level requirements such as passenger c…
Autonomous VehiclesBayesian OptimizationModel Predictive ControlModel-based control algorithms for the quadruple tank system: An experimental comparison
We compare the performance of proportional-integral-derivative (PID) control, linear model predictive control (LMPC), and nonlinear model predictive control (NMPC) for a physical setup of the quadruple tank system (QTS).…
Model Predictive ControlMulti-Objective Model-Predictive Control for Dielectric Elastomer Wave Harvesters
This contribution deals with multi-objective model-predictive control (MPC) of a wave energy converter (WEC) device concept, which can harvest energy from sea waves using a dielectric elastomer generator (DEG) power take…
Model Predictive ControlSafe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots
Flexible robots may overcome some of the industry's major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher payload-to-mass ratio. However, controlling flexible robots is co…
Deformable Object ManipulationImitation LearningModel Predictive ControlBi-Level Optimization Augmented with Conditional Variational Autoencoder for Autonomous Driving in Dense Traffic
Autonomous driving has a natural bi-level structure. The goal of the upper behavioural layer is to provide appropriate lane change, speeding up, and braking decisions to optimize a given driving task. However, this layer…
Autonomous DrivingGPUModel Predictive ControlReinforcement Learning (RL)Performance Quantification of a Nonlinear Model Predictive Controller by Parallel Monte Carlo Simulations of a Closed-loop System
This paper presents a parallel Monte Carlo simulation based performance quantification method for nonlinear model predictive control (NMPC) in closed-loop. The method provides distributions for the controller performance…
CPUModel Predictive ControlModel Predictive Control Tuning by Monte Carlo Simulation and Controller Matching
This paper presents a systematic method for the selection of the Model Predictive Control (MPC) stage cost. We match the MPC feedback law to a proportional-integral (PI) controller, which we efficiently tune by high-perf…
Model Predictive ControlLearning to Optimize in Model Predictive Control
Sampling-based Model Predictive Control (MPC) is a flexible control framework that can reason about non-smooth dynamics and cost functions. Recently, significant work has focused on the use of machine learning to improve…
Computational EfficiencyImitation LearningmodelModel Predictive ControlLearning Sampling Distributions for Model Predictive Control
Sampling-based methods have become a cornerstone of contemporary approaches to Model Predictive Control (MPC), as they make no restrictions on the differentiability of the dynamics or cost function and are straightforwar…
modelModel Predictive ControlAdaptive Robust Model Predictive Control via Uncertainty Cancellation
We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear compo…
Meta-LearningModel Predictive ControlPredict-and-Critic: Accelerated End-to-End Predictive Control for Cloud Computing through Reinforcement Learning
Cloud computing holds the promise of reduced costs through economies of scale. To realize this promise, cloud computing vendors typically solve sequential resource allocation problems, where customer workloads are packed…
Cloud ComputingModel Predictive ControlLinear Data-Driven Economic MPC with Generalized Terminal Constraint
In this paper, we propose a data-driven economic model predictive control (EMPC) scheme with generalized terminal constraint to control an unknown linear time-invariant system. Our scheme is based on the Fundamental Lemm…
LEMMAModel Predictive ControlPredictive Control with Learning-Based Terminal Costs Using Approximate Value Iteration
Stability under model predictive control (MPC) schemes is frequently ensured by terminal ingredients. Employing a (control) Lyapunov function as the terminal cost constitutes a common choice. Learning-based methods may b…
Model Predictive ControlLQG for Constrained Linear Systems: Indirect Feedback Stochastic MPC with Kalman Filtering
We present an output feedback stochastic model predictive control (SMPC) approach for linear systems subject to Gaussian disturbances and measurement noise and probabilistic constraints on system states and inputs. The p…
Model Predictive ControlState EstimationInvestigation of Proper Orthogonal Decomposition for Echo State Networks
Echo State Networks (ESN) are a type of Recurrent Neural Network that yields promising results in representing time series and nonlinear dynamic systems. Although they are equipped with a very efficient training procedur…
Model Predictive ControlTime Series AnalysisDeep Learning-Based Vehicle Speed Prediction for Ecological Adaptive Cruise Control in Urban and Highway Scenarios
In a typical car-following scenario, target vehicle speed fluctuations act as an external disturbance to the host vehicle and in turn affect its energy consumption. To control a host vehicle in an energy-efficient manner…
Model Predictive ControlPredictionTime Series AnalysisDual adaptive MPC using an exact set-membership reformulation
Adaptive model predictive control (MPC) methods using set-membership identification to reduce parameter uncertainty are considered in this work. Strong duality is used to reformulate the set-membership equations exactly …
Model Predictive ControlCollaborative learning model predictive control for repetitive tasks
This paper presents a cloud-based learning model predictive controller that integrates three interacting components: a set of agents, which must learn to perform a finite set of tasks with the minimum possible local cost…
modelModel Predictive ControlBEAR: Physics-Principled Building Environment for Control and Reinforcement Learning
Recent advancements in reinforcement learning algorithms have opened doors for researchers to operate and optimize building energy management systems autonomously. However, the lack of an easily configurable building dyn…
energy managementManagementModel Predictive Controlreinforcement-learning+2A Critical Review of Traffic Signal Control and A Novel Unified View of Reinforcement Learning and Model Predictive Control Approaches for Adaptive Traffic Signal Control
Recent years have witnessed substantial growth in adaptive traffic signal control (ATSC) methodologies that improve transportation network efficiency, especially in branches leveraging artificial intelligence based optim…
Model Predictive ControlTraffic Signal Control