Title :
Neural network control of a class of nonlinear systems with actuator saturation
Author :
Gao, Wenzhi ; Selmic, Rastko R.
Author_Institution :
Rockwell Corp., Pleasanton, CA, USA
Abstract :
A neural net (NN)-based actuator saturation compensation scheme for the nonlinear systems in Brunovsky canonical form is presented. The scheme that leads to stability, command following, and disturbance rejection is rigorously proved and verified using a general "pendulum type" and a robot manipulator dynamical systems. Online weights tuning law, the overall closed-loop system performance, and the boundedness of the NN weights are derived and guaranteed based on Lyapunov approach. The actuator saturation is assumed to be unknown and the saturation compensator is inserted into a feedforward path. Simulation results indicate that the proposed scheme can effectively compensate for the saturation nonlinearity in the presence of system uncertainty.
Keywords :
Lyapunov methods; closed loop systems; control nonlinearities; feedforward; manipulator dynamics; neurocontrollers; nonlinear control systems; pendulums; stability; uncertain systems; Brunovsky canonical form; Lyapunov method; actuator saturation; closed loop system; command following; disturbance rejection; feedforward path; neural network control; nonlinear systems; pendulum type; robot manipulator dynamical systems; saturation nonlinearity; stability; system uncertainty; Actuators; Control systems; Manipulator dynamics; Neural networks; Nonlinear control systems; Nonlinear systems; Robots; Stability; System performance; Uncertainty; Actuator nonlinearities; Brunovsky canonical form; neural network (NN); saturation compensation; stability;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2005.863416
