Intelligent Proportional Differential Neural Network Control for Unknown Nonlinear System

Haoping WANG¹, Shanzhi LI¹, Yang TIAN^1,*, Abdel AITOUCHE²

Abstract: This paper presents an intelligent proportion-differential neural network (iPDNN) controller for unknown nonlinear systems. This controller is based on the intelligent proportion integration differentiation (iPID) controller. In an iPID controller system, a unknown nonlinear SISO system is regarded as an ultra-local two-order or one-order model and a lumped unknown dynamics (LUD) disturbance which contains the high-term and parametric uncertainties by the differential algebra and estimation method online. However, its performance of an iPID control depends on the precision and rapidity for estimating the LUD disturbance. Besides, it also influences the parameter in the ultra-local model. In order to compensate the estimation error of LUD disturbance, we put forward an extra radial basis function (RBF) neural network observer to estimate it. This extra observer cannot only ensure to acquire the estimation error rapidly, but also has an ability of self-learning. In addition, this iPDNN method can ensure the closed-loop system stability under the Lyapunov stability theory. Finally, in order to demonstrate its performance, an inverted pendulum plant has been applied and the results indicate this method is of efficiency.

Keywords: adaptive control; mode- free control; PID.

>Full text
CITE THIS PAPER AS:
Haoping WANG, Shanzhi LI, Yang TIAN*, Abdel AITOUCHE, Intelligent Proportional Differential Neural Network Control for Unknown Nonlinear System, Studies in Informatics and Control, ISSN 1220-1766, vol. 25(4), pp. 445-452, 2016. https://doi.org/10.24846/v25i4y201605