On Neural Network Switched Stabilization of SISO Switched Nonlinear Systems with Actuator Saturation
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
International Journal of Intelligent Systems Technologies and Applications
Brief paper: Novel adaptive neural control design for nonlinear MIMO time-delay systems
Automatica (Journal of IFAC)
Motion control with deadzone estimation and compensation using GRNN for TWUSM drive system
Expert Systems with Applications: An International Journal
Fast learning algorithm for controlling logistic chaotic system based on Chebyshev neural network
ICNC'09 Proceedings of the 5th international conference on Natural computation
Radial basis function neural network-based adaptive critic control of induction motors
Applied Soft Computing
Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints
Automatica (Journal of IFAC)
Adaptive fuzzy control of an active vibration isolator
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Information Sciences: an International Journal
Information Sciences: an International Journal
Hi-index | 0.01 |
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.