Title :
Data driven- Adaptive single neuron predictive controller based on Lyapunov approach
Author :
Jia, Li ; Cao, Luming ; Chiu, Minsen
Author_Institution :
Dept. of Autom., Shanghai Univ., Shanghai, China
Abstract :
In this paper, a novel data driven-adaptive single neuron predictive controller is proposed. The self-tuning algorithm for the single neuron predictive controller is derived by a rigorous analysis based on the Lyapunov method such that the predicted tracking error convergences asymptotically. Simulation results are presented to illustrate the proposed adaptive predictive controller and a comparison with its conventional counterparts is made.
Keywords :
Lyapunov methods; adaptive control; asymptotic stability; convergence; neurocontrollers; predictive control; self-adjusting systems; Lyapunov approach; Lyapunov method; adaptive predictive controller; asymptotic convergence; conventional counterparts; data driven-adaptive single neuron predictive controller; predicted tracking error; rigorous analysis; self-tuning algorithm; Adaptation models; Automation; Chemical reactors; Neurons; Periodic structures; Prediction algorithms; Process control; Lyapunov approach; PID controller; neuron predictive controller;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2011 9th World Congress on
Conference_Location :
Taipei
Print_ISBN :
978-1-61284-698-9
DOI :
10.1109/WCICA.2011.5970724
