Title :
Response of a feedback system with a neural network controller in the presence of disturbances
Author :
Li, Qing ; Teo, C.L. ; Poo, A.N. ; Hong, G.S.
Author_Institution :
Dept. of Mech. & Production Eng., Nat. Univ. of Singapore, Singapore
Abstract :
A neural network controller is presented as an approach to the reduction of the effect of disturbances on the performance of a feedback control system with a nonlinear plant. A backpropagation neural network was trained and subsequently used as a model-based controller for a one-dimensional robot arm. The simulation results obtained show that the neural network controller can perform quite well on a highly nonlinear system even in the presence of high levels of disturbances. A neural network controller trained with noisy data can adapt to the presence of disturbances better than a neural network controller trained with clean data. The neural network controller trained with noisy data was also found to perform better than a conventional model-based controller in the presence of disturbances. In the absence of disturbances, the former also matches the performance of the latter
Keywords :
adaptive control; controllers; feedback; neural nets; nonlinear control systems; adaptive control; backpropagation neural network; disturbances; feedback system; highly nonlinear system; model-based controller; neural network controller; nonlinear control systems; one-dimensional robot arm; Acceleration; Control systems; Degradation; Intelligent networks; Measurement errors; Neural networks; Neurofeedback; Nonlinear control systems; Robots; System performance;
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
DOI :
10.1109/IJCNN.1991.170627