Parameters Turning of ADRC based on Neural Network
- https://doi.org/10.2991/emcs-16.2016.187How to use a DOI?
- Tracking differentiator; Gradient information; ADRC controller; Parameters turning; Self-learning
Objective:Though ADRC controller shows strong robust character and adaptability, it still exist a lot of shortages, such as the frequency characteristie, stability... ete. Those were not solved in the theories. And the most diffieulty of ADRC's application in the industry is that the parameters are too more and the parameters’adjusting is diffieulty. The engineers go short of the experience of the parameters’ adjusting for the eontroller. To solve the problems that the active disturbance rejection controller has too many parameters and it is very difficult to calculate a set of optimal parameters without determinate turning algorithms.Methods: Neural network can get the gradient information of the controlled object, then use gradient descent method to modify nonlinear combined parameters online, so that the ADRC controller has the ability of self-learning, enhanced adaptive ability of ADRC. Results :The simulation results show that the ADRC controller which has good dynamic and static features, improves the design efficiency. The feasibility and effectiveness of this method is further verified
- © 2016, the Authors. Published by Atlantis Press.
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Cite this article
TY - CONF AU - Hongliang Kan AU - Fenglong Kan AU - Nan Chen AU - Qiaoshi Ma AU - Xin Wang AU - Dongwei Zhang AU - Ning Qi AU - Bin Wang PY - 2016/01 DA - 2016/01 TI - Parameters Turning of ADRC based on Neural Network BT - Proceedings of the 2016 International Conference on Education, Management, Computer and Society PB - Atlantis Press SP - 767 EP - 769 SN - 2352-538X UR - https://doi.org/10.2991/emcs-16.2016.187 DO - https://doi.org/10.2991/emcs-16.2016.187 ID - Kan2016/01 ER -