Title :
A minimum variance predictive controller for nonlinear systems
Author :
Qingbo, Shen ; Jin, Wang ; Chang, LI
Author_Institution :
Dept. of Autom., Fushun Petrol. Inst., China
Abstract :
The paper presents a minimum variance predictive controller (MVPC) using a modified neural network (MNN) in order to learn the characteristics of a dynamic system. The MVPC can adapt parameter variation and uncertainty in the controlled plant through online learning. The learning algorithm is considerably faster because of the introduction of a recursive least squares (RLS) algorithm. Simulation results show that the proposed approach is effective for adaptive control of nonlinear systems
Keywords :
adaptive control; learning (artificial intelligence); least squares approximations; neurocontrollers; nonlinear control systems; predictive control; MVPC; adaptive control; dynamic system; learning algorithm; minimum variance predictive controller; modified neural network; nonlinear systems; online learning; parameter variation; recursive least squares; uncertainty; Adaptive control; Control systems; Least squares methods; Multi-layer neural network; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Resonance light scattering; Uncertainty;
Conference_Titel :
Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-4253-4
DOI :
10.1109/ICIPS.1997.672886
