Adaptive model predictive control algorithm for incremental forming processes

2021 ◽  
Author(s):  
◽  
An He
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Incremental Forming ◽  
Adaptive Model ◽  
Model Predictive Control Algorithm ◽  
Adaptive Model Predictive Control

scholarly journals Part accuracy improvement in two point incremental forming with a partial die using a model predictive control algorithm

2017 ◽  
Vol 49 ◽  
pp. 179-188 ◽  
Author(s):  
Haibo Lu ◽  
Michael Kearney ◽  
Chenhao Wang ◽  
Sheng Liu ◽  
Paul A. Meehan
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Incremental Forming ◽  
Accuracy Improvement ◽  
Model Predictive Control Algorithm

scholarly journals Robust Path Tracking Control for Autonomous Vehicle Based on a Novel Fault Tolerant Adaptive Model Predictive Control Algorithm

2020 ◽  
Vol 10 (18) ◽  
pp. 6249
Author(s):  
Keke Geng ◽  
Shuaipeng Liu
Keyword(s):  
Predictive Control ◽  
Autonomous Vehicles ◽  
Tracking Control ◽  
Control Algorithm ◽  
Fault Tolerant ◽  
Path Tracking ◽  
Adaptive Model ◽  
Chi Square ◽  
Model Predictive Control Algorithm ◽  
Adaptive Model Predictive Control

Autonomous vehicles are expected to completely change the development model of the transportation industry and bring great convenience to our lives. Autonomous vehicles need to constantly obtain the motion status information with on-board sensors in order to formulate reasonable motion control strategies. Therefore, abnormal sensor readings or vehicle sensor failures can cause devastating consequences and can lead to fatal vehicle accidents. Hence, research on the fault tolerant control method is critical for autonomous vehicles. In this paper, we develop a robust fault tolerant path tracking control algorithm through combining the adaptive model predictive control algorithm for lateral path tracking control, improved weight assignment method for multi-sensor data fusion and fault isolation, and novel federal Kalman filtering approach with two states chi-square detector and residual chi-square detector for detection and identification of sensor fault in autonomous vehicles. Our numerical simulation and experiment demonstrate that the developed approach can detect fault signals and identify their sources with high accuracy and sensitivity. In the double line change path tracking control experiment, when the sensors failure occurs, the proposed method shows better robustness and effectiveness than the traditional methods. It is foreseeable that this research will contribute to the development of safer and more intelligent autonomous driving system, which in turn will promote the industrial development of intelligent transportation system.

scholarly journals Reference Tracking using a Non-Cooperative Distributed Model Predictive Control Algorithm

2016 ◽  
Vol 49 (7) ◽  
pp. 1079-1084 ◽  
Author(s):  
Anca Maxim ◽  
Clara M. Ionescu ◽  
Constantin F. Caruntu ◽  
Corneliu Lazar ◽  
Robin De Keyser
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Algorithm ◽  
Distributed Model ◽  
Reference Tracking ◽  
Distributed Model Predictive Control ◽  
Model Predictive Control Algorithm

A NEW DEVELOPMENT OF ADAPTIVE MODEL PREDICTIVE CONTROL

2005 ◽  
Vol 38 (1) ◽  
pp. 348-353
Author(s):  
D.L. Yu ◽  
D.W. Yu ◽  
J.B. Gomm ◽  
G.F. Page
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Adaptive Model ◽  
New Development ◽  
Adaptive Model Predictive Control
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