Title :
Adaptive neural-network predictive control for nonminimum-phase systems
Author :
Wu, Wei ; Hsu, Wei-Ching
Abstract :
An adaptive neural-network predictive control strategy for a class of nonlinear processes, which exhibit input multiplicities and change in the sign of steady-state gains, is presented. According to the graphic-based determination for neural network architecture associated with prescribed input/output patterns, the feedforward neural network (FNN) is used to capture dynamic and steady-state characteristics of minimum-phase modes over a specified operating range. A one-step-ahead neural prediction algorithm with respect to physical constraints can carry out the offset free performance. Closed-loop simulations demonstrate the effectiveness of the proposed approaches
Keywords :
MIMO systems; adaptive control; closed loop systems; feedforward neural nets; neurocontrollers; nonlinear control systems; predictive control; adaptive control; closed-loop simulations; feedforward neural network; input multiplicities; input/output patterns; neural network architecture; neural-network control; nonlinear processes; nonminimum-phase systems; one-step-ahead neural prediction; predictive control; Adaptive control; Autoregressive processes; Fuzzy control; Neural networks; Nonlinear control systems; Nonlinear systems; Predictive control; Predictive models; Programmable control; Steady-state;
Conference_Titel :
American Control Conference, 2006
Conference_Location :
Minneapolis, MN
Print_ISBN :
1-4244-0209-3
Electronic_ISBN :
1-4244-0209-3
DOI :
10.1109/ACC.2006.1657173
