Application of PID Controller Based on BP Neural Network Using Automatic Differentiation Method

  • Authors:

  • Weiwei Yang;Yong Zhao;Li Yan;Xiaoqian Chen

  • Affiliations:

  • Multidisciplinary Aerospace Design Optimization Research Center, College of Aerospace and Materials Engineering, National University of Defense Technology, Changsha, China 410073;Multidisciplinary Aerospace Design Optimization Research Center, College of Aerospace and Materials Engineering, National University of Defense Technology, Changsha, China 410073;Multidisciplinary Aerospace Design Optimization Research Center, College of Aerospace and Materials Engineering, National University of Defense Technology, Changsha, China 410073;Multidisciplinary Aerospace Design Optimization Research Center, College of Aerospace and Materials Engineering, National University of Defense Technology, Changsha, China 410073

  • Venue:
  • ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
  • Year:
  • 2008

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Abstract

A simulation analysis of PID controller based on Back-Propagation Neural Network (BPNN) using Automatic Differentiation Method (ADM) is presented. As accurate partial differentiation can be acquired using ADM, the original meaning of learning rate is regained. By comparing with conventional PID controller, the simulation results of a simple tracking problem show that the new controller has a good adaptability for the nonlinear system, which benefits from on-line self-learning. Furthermore, experimental results are presented for an autonomous docking of the chaser simulator to the target, which validates the effectiveness and good robustness of the proposed controller.