Neural Network Autoregressive With Exogenous Input Assisted Multi-Constraint Nonlinear Predictive Control of Autonomous Vehicles

2019 ◽  
Vol 68 (7) ◽  
pp. 6293-6304 ◽  
Author(s):  
Hamid Taghavifar
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Nonlinear Predictive Control

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.

The use of Neural Network for Nonlinear Predictive Control design for and Overhead Crane

2021 ◽  
Author(s):  
Jakub Nemcik ◽  
Filip Krupa ◽  
Stepan Ozana ◽  
Zdenek Slanina ◽  
Ivan Zelinka
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Control Design ◽  
Overhead Crane ◽  
Nonlinear Predictive Control

Constant Cutting-Power Control of Shearer Based on Neural Network Model Predictive Control

2013 ◽  
Vol 823 ◽  
pp. 340-344
Author(s):  
Yuan Hua Zhou ◽  
Hong Wei Ma ◽  
Hai Yan Wu ◽  
You Jun Zhao
Keyword(s):  
Neural Network ◽  
Power Control ◽  
Network Model ◽  
Predictive Control ◽  
Neural Network Model ◽  
Control Method ◽  
Cutting Power ◽  
Nonlinear Predictive Control ◽  
The Neural Network ◽  
And Control

To solve the problem of constant power control of shearer cutting machine, the nonlinear predictive control method based on Neural Network was proposed in this thesis. In the method, the cutting current was used to identify the cutting load, and the Neural Network was used to predict and control the traction speed. A Neural Network model was built by the current and speed to control the cutting power of shearer. In MATLAB, the field data was used to simulate and the simulation verify the proposed scheme is better than PID method.

Application of Neural Network for Nonlinear Predictive Control

2012 ◽  
Vol 562-564 ◽  
pp. 1964-1967 ◽  
Author(s):  
Zhi Cheng Xu ◽  
Bin Zhu ◽  
Qing Bin Jiang
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Linear Model ◽  
Predictive Control ◽  
Control Method ◽  
Nonlinear Modeling ◽  
Nonlinear Process ◽  
Effective Control ◽  
Nonlinear Predictive Control ◽  
The Stability

A novel model predictive control method was proposed for a class of dynamic processes with modest nonlinearities in this paper. In this method, a diagonal recurrent neural network (DRNN) is used to compensate nonlinear modeling error that is caused because linear model is regarded as prediction model of nonlinear process. It is aimed at offsetting the effect of model mismatch on the control performance, strengthening the robustness of predictive control and the stability of control system. Under a certain assumption condition, linear model predictive control method is extended to nonlinear process, which doesn’t need solve nonlinear optimization problem. Consequently, the computational efforts are reduced drastically. The simulation example shows that the proposed method is an effective control strategy with excellent tracing characteristics and strong robustness.

scholarly journals Constrained Nonlinear Predictive Control Based on Input-Output Linearization Using a Neural Network

1996 ◽  
Vol 29 (1) ◽  
pp. 2574-2579
Author(s):  
Miguel Ayala Botto ◽  
Ton J.J. van den Boom ◽  
Ardjan Krijgsman ◽  
José Sá da Costa
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Input Output ◽  
Nonlinear Predictive Control ◽  
Input Output Linearization
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