Papers Model Predictive Control
“Model Predictive Control” 태그가 달린 논문 1,582편 · 필터 해제
Recursively Feasible Shrinking-Horizon MPC in Dynamic Environments with Conformal Prediction Guarantees
In this paper, we focus on the problem of shrinking-horizon Model Predictive Control (MPC) in uncertain dynamic environments. We consider controlling a deterministic autonomous system that interacts with uncontrollable s…
Conformal PredictionModel Predictive ControlPredictionSelf-Triggered Distributed Model Predictive Control with Synchronization Parameters Interaction
This paper investigates an aperiodic distributed model predictive control approach for multi-agent systems (MASs) in which parameterized synchronization constraints is considered and an innovative self-triggered criterio…
Model Predictive ControlDistributed Model Predictive Control for Asynchronous Multi-agent Systems with Self-Triggered Coordinator
This paper investigates the distributed model predictive control for an asynchronous nonlinear multi-agent system with external interference via a self-triggered generator and a prediction horizon regulator. First, a shr…
Model Predictive ControlIncorporating ESO into Deep Koopman Operator Modelling for Control of Autonomous Vehicles
Koopman operator theory is a kind of data-driven modelling approach that accurately captures the nonlinearities of mechatronic systems such as vehicles against physics-based methods. However, the infinite-dimensional Koo…
Autonomous VehiclesModel Predictive ControlEfficient model predictive control for nonlinear systems modelled by deep neural networks
This paper presents a model predictive control (MPC) for dynamic systems whose nonlinearity and uncertainty are modelled by deep neural networks (NNs), under input and state constraints. Since the NN output contains a hi…
Model Predictive ControlAdaptive tracking MPC for nonlinear systems via online linear system identification
This paper presents an adaptive tracking model predictive control (MPC) scheme to control unknown nonlinear systems based on an adaptively estimated linear model. The model is determined based on linear system identifica…
LEMMAModel Predictive ControlSOMTP: Self-Supervised Learning-Based Optimizer for MPC-Based Safe Trajectory Planning Problems in Robotics
Model Predictive Control (MPC)-based trajectory planning has been widely used in robotics, and incorporating Control Barrier Function (CBF) constraints into MPC can greatly improve its obstacle avoidance efficiency. Unfo…
Model Predictive ControlSelf-Supervised LearningTrajectory PlanningAccelerating the Evolution of Personalized Automated Lane Change through Lesson Learning
Personalization is crucial for the widespread adoption of advanced driver assistance system. To match up with each user's preference, the online evolution capability is a must. However, conventional evolution methods lea…
Computational EfficiencyModel Predictive ControlTrajectory PlanningNonparametric Control Koopman Operators
This paper presents a novel Koopman (composition) operator representation framework for control systems in reproducing kernel Hilbert spaces (RKHSs) that is free of explicit dictionary or input parametrizations. By estab…
Learning TheoryModel Predictive ControlOperator learningCooperative Route Guidance and Flow Control for Mixed Road Networks Comprising Expressway and Arterial Network
Facing the congestion challenges of mixed road networks comprising expressways and arterial road networks, traditional control solutions fall short. To effectively alleviate traffic congestion in mixed road networks, it …
Model Predictive ControlConfiguration-Constrained Tube MPC for Tracking
This paper proposes a novel tube-based Model Predictive Control (MPC) framework for tracking varying setpoint references with linear systems subject to additive and multiplicative uncertainties. The MPC controllers desig…
Model Predictive ControlOn the constrained feedback linearization control based on the MILP representation of a ReLU-ANN
In this work, we explore the efficacy of rectified linear unit artificial neural networks in addressing the intricate challenges of convoluted constraints arising from feedback linearization mapping. Our approach involve…
Model Predictive ControlModel Predictive Control for Joint Ramping and Regulation-Type Service from Distributed Energy Resource Aggregations
Distributed energy resources (DERs) such as grid-responsive loads and batteries can be harnessed to provide ramping and regulation services across the grid. This paper concerns the problem of optimal allocation of differ…
Model Predictive ControlUDUC: An Uncertainty-driven Approach for Learning-based Robust Control
Learning-based techniques have become popular in both model predictive control (MPC) and reinforcement learning (RL). Probabilistic ensemble (PE) models offer a promising approach for modelling system dynamics, showcasin…
Contrastive LearningModel Predictive Controlreinforcement-learningReinforcement Learning+1Hybrid Lyapunov-based feedback stabilization of bipedal locomotion based on reference spreading
We propose a hybrid formulation of the linear inverted pendulum model for bipedal locomotion, where the foot switches are triggered based on the center of mass position, removing the need for pre-defined footstep timings…
Model Predictive ControlPositionObstacle Avoidance of Autonomous Vehicles: An LPVMPC with Scheduling Trust Region
Reference tracking and obstacle avoidance rank among the foremost challenging aspects of autonomous driving. This paper proposes control designs for solving reference tracking problems in autonomous driving tasks while c…
Autonomous DrivingAutonomous VehiclesModel Predictive ControlSchedulingA Feedback Linearized Model Predictive Control Strategy for Input-Constrained Self-Driving Cars
This paper proposes a novel real-time affordable solution to the trajectory tracking control problem for self-driving cars subject to longitudinal and steering angular velocity constraints. To this end, we develop a dual…
Model Predictive ControlSelf-Driving CarsGAD-Generative Learning for HD Map-Free Autonomous Driving
Deep-learning-based techniques have been widely adopted for autonomous driving software stacks for mass production in recent years, focusing primarily on perception modules, with some work extending this method to predic…
Autonomous DrivingModel Predictive ControlMotion PlanningData-Driven Min-Max MPC for Linear Systems: Robustness and Adaptation
Data-driven controllers design is an important research problem, in particular when data is corrupted by the noise. In this paper, we propose a data-driven min-max model predictive control (MPC) scheme using noisy input-…
Model Predictive ControlAdaptive Learning-based Model Predictive Control for Uncertain Interconnected Systems: A Set Membership Identification Approach
We propose a novel adaptive learning-based model predictive control (MPC) scheme for interconnected systems which can be decomposed into several smaller dynamically coupled subsystems with uncertain coupling. The propose…
Model Predictive Control