scholarly journals From multi‐physics models to neural network for predictive control synthesis

2021 ◽  
Author(s):  
Pierre Clément Blaud ◽  
Philippe Chevrel ◽  
Fabien Claveau ◽  
Pierrick Haurant ◽  
Anthony Mouraud
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Control Synthesis

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.

ChemInform Abstract: Thermal Stress Management of a Solid Oxide Fuel Cell Using Neural Network Predictive Control

ChemInform ◽  
2014 ◽  
Vol 45 (30) ◽  
pp. no-no
Author(s):  
S. A. Hajimolana ◽  
S. M. Tonekabonimoghadam ◽  
M. A. Hussain ◽  
M. H. Chakrabarti ◽  
N. S. Jayakumar ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Stress ◽  
Fuel Cell ◽  
Solid Oxide Fuel Cell ◽  
Stress Management ◽  
Predictive Control ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Neural Network Predictive Control
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