Title :
Neural Network Saturation Compensation for DC Motor Systems
Author_Institution :
Dept. of Comput. Control Eng., Uiduk Univ., Kyongju
fDate :
6/1/2007 12:00:00 AM
Abstract :
A neural network (NN) saturation compensation scheme for dc motor systems is presented. The scheme, which leads to stability, command following, and disturbance rejection, is rigorously proven. The online weight tuning law, overall closed-loop performance, and boundness of the NN weights are derived and guaranteed based on the Lyapunov approach. Simulation and experimental results show that the proposed scheme effectively compensates for saturation nonlinearity in the presence of system uncertainty
Keywords :
DC motors; Lyapunov methods; neural nets; stability; DC motor systems; Lyapunov approach; closed-loop performance; neural network saturation compensation; online weight tuning law; stability; Actuators; Control systems; DC motors; Hysteresis; Neural networks; Neurons; Nonlinear control systems; Nonlinear systems; Stability; Windup; Actuator nonlinearity; dc motor system; neural networks (NNs); saturation compensation; stability;
Journal_Title :
Industrial Electronics, IEEE Transactions on
DOI :
10.1109/TIE.2007.894706
