A Dynamic Fuzzy Neural Networks Controller for Dynamic Load Simulator

Author

Guo, Ben ; Wang, Ming-Yan ; Zhang, Jian

Author_Institution

Dept. of Electr. Eng., Harbin Inst. of Technol.

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

375

Lastpage

379

Abstract

This paper presents the design, development of dynamic load simulator based on dynamic fuzzy neural networks (D-FNNs) controller. Dynamic load simulator (DLS) can reproduce desired load torque acting on loaded object to test its performance and stability. In DLS, the redundancy torque caused by the motion of loaded object has a very poor effect on the loading accuracy. So a simplified dynamic model is derived to clarify the causation of redundancy torque, and an inverse model controller based on D-FNNs is implemented to compensate redundancy torque and improve the accuracy of load torque despite the nonlinearity and uncertainties in the DLS system. The effectiveness of D-FNNs controller for DLS is verified by numerical simulation and experiment

Keywords

control system synthesis; electric actuators; fuzzy control; fuzzy neural nets; neurocontrollers; simulation; torque control; dynamic fuzzy neural network controller; dynamic load simulator design; electric DLS; inverse model controller; load torque; numerical simulation; redundancy torque; stability; Fuzzy control; Fuzzy neural networks; Inverse problems; Nonlinear control systems; Nonlinear dynamical systems; Numerical simulation; Stability; Testing; Torque control; Uncertainty; Dynamic load simulator; Fuzzy system; Inverse model control; neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.259042

Filename

4028092