Automated turning and merging for autonomous vehicles using a Nonlinear Model Predictive Control approach

2017 ◽  
Author(s):  
Lixing Huang ◽  
Dimitra Panagou
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Vehicles ◽  
Nonlinear Model Predictive Control ◽  
Control Approach

scholarly journals LVD-NMPC: A learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110195
Author(s):  
Sorin Grigorescu ◽  
Cosmin Ginerica ◽  
Mihai Zaha ◽  
Gigel Macesanu ◽  
Bogdan Trasnea
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Vehicles ◽  
Process Model ◽  
Network Performance ◽  
Vision System ◽  
Nonlinear Model Predictive Control ◽  
Scaled Model

In this article, we introduce a learning-based vision dynamics approach to nonlinear model predictive control (NMPC) for autonomous vehicles, coined learning-based vision dynamics (LVD) NMPC. LVD-NMPC uses an a-priori process model and a learned vision dynamics model used to calculate the dynamics of the driving scene, the controlled system’s desired state trajectory, and the weighting gains of the quadratic cost function optimized by a constrained predictive controller. The vision system is defined as a deep neural network designed to estimate the dynamics of the image scene. The input is based on historic sequences of sensory observations and vehicle states, integrated by an augmented memory component. Deep Q-learning is used to train the deep network, which once trained can also be used to calculate the desired trajectory of the vehicle. We evaluate LVD-NMPC against a baseline dynamic window approach (DWA) path planning executed using standard NMPC and against the PilotNet neural network. Performance is measured in our simulation environment GridSim, on a real-world 1:8 scaled model car as well as on a real size autonomous test vehicle and the nuScenes computer vision dataset.

scholarly journals Balancing-Aware Charging Strategy for Series-Connected Lithium-Ion Cells: A Nonlinear Model Predictive Control Approach

2020 ◽  
Vol 28 (5) ◽  
pp. 1862-1877
Author(s):  
Andrea Pozzi ◽  
Massimo Zambelli ◽  
Antonella Ferrara ◽  
Davide M. Raimondo
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Lithium Ion ◽  
Nonlinear Model Predictive Control ◽  
Control Approach ◽  
Lithium Ion Cells ◽  
Charging Strategy

Explicit nonlinear model predictive control tracking control based on a sliding mode observer for a quadrotor subject to disturbances

2019 ◽  
Vol 42 (2) ◽  
pp. 214-227 ◽  
Author(s):  
Nadia Miladi ◽  
Habib Dimassi ◽  
Salim Hadj Said ◽  
Faouzi M’Sahli
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Sliding Mode ◽  
High Gain ◽  
Sliding Mode Observer ◽  
Nonlinear Model Predictive Control ◽  
Control Approach

In this paper, we propose an explicit nonlinear model predictive control (ENMPC) method based on a robust observer to solve the trajectory tracking problem for outdoor quadrotors. We take into consideration the external aerodynamic disturbances present in the dynamics of the Newton-Euler quadrotor model. To overcome the effects of these disturbances, a high gain observer combined with a first order sliding mode observer are proposed to estimate both the states and the unknown disturbances using the only positions and angular measurements of the quadrotor. The estimated signals are then used by the predictive controller in order to ensure the trajectory tracking objective. Despite the presence of bounded disturbances, the convergence of the composite controller (ENMPC technique with the latter observers) is guaranteed through a stability analysis. Theoretical results are validated with some numerical simulations showing the good performances of the proposed tracking control approach.

Path Tracking for Autonomous Vehicles Based on Nonlinear Model: Predictive Control Method

2019 ◽  
Author(s):  
Shaosong Li ◽  
Zheng Li ◽  
Bangcheng Zhang ◽  
Shunhang Zheng ◽  
Xiaohui Lu ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Vehicles ◽  
Control Method ◽  
Path Tracking ◽  
Nonlinear Model Predictive Control
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