Title :
An approximate internal model-based neural control for unknown nonlinear discrete processes
Author :
Han-Xiong Li ; Hua Deng
Author_Institution :
Dept. of Manuf. Eng. & Eng. Manage., City Univ. of Hong Kong
fDate :
5/1/2006 12:00:00 AM
Abstract :
An approximate internal model-based neural control (AIMNC) strategy is proposed for unknown nonaffine nonlinear discrete processes under disturbed environment. The proposed control strategy has some clear advantages in respect to existing neural internal model control methods. It can be used for open-loop unstable nonlinear processes or a class of systems with unstable zero dynamics. Based on a novel input-output approximation, the proposed neural control law can be derived directly and implemented straightforward for an unknown process. Only one neural network needs to be trained and control algorithm can be directly obtained from model identification without further training. The stability and robustness of a closed-loop system can be derived analytically. Extensive simulations demonstrate the superior performance of the proposed AIMNC strategy
Keywords :
closed loop systems; discrete time systems; neurocontrollers; nonlinear control systems; open loop systems; stability; approximate internal model-based neural control; closed-loop system; input-output approximation; neural network; open-loop unstable nonlinear processes; unknown nonaffine nonlinear discrete processes; unstable zero dynamics; Gradient methods; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Open loop systems; Process control; Robust stability; Signal generators; Stability analysis; Uncertainty; Input–output approximation; neural networks; nonaffine nonlinear discrete-time systems; nonlinear internal model control; unstable zero dynamics; Algorithms; Artificial Intelligence; Computer Simulation; Feedback; Information Storage and Retrieval; Models, Statistical; Neural Networks (Computer); Nonlinear Dynamics; Pattern Recognition, Automated; Systems Theory;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2006.873277