Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems

1999 ◽  
Vol 146 (4) ◽  
pp. 319-326 ◽  
Author(s):  
Q. M. Zhu ◽  
K. Warwick ◽  
Z. Ma
Keyword(s):  
Neural Network ◽  
Discrete Time ◽  
Minimum Variance ◽  
Discrete Time Systems ◽  
Self Tuning ◽  
Time Systems

On Self-Tuning Control of Nonminimum Phase Discrete-Time Systems Using Approximate Inverse Systems

1993 ◽  
Vol 115 (1) ◽  
pp. 12-18 ◽  
Author(s):  
Takashi Yahagi ◽  
Jianming Lu
Keyword(s):  
Discrete Time ◽  
Stochastic Systems ◽  
Output Data ◽  
Approximate Inverse ◽  
Nonminimum Phase ◽  
Discrete Time Systems ◽  
Inverse Systems ◽  
Self Tuning ◽  
The Stability ◽  
Time Systems

This paper presents a new method for self-tuning control of nonminimum phase discrete-time stochastic systems using approximate inverse systems obtained from the least-squares approximation. We show how unstable pole-zero cancellations can be avoided, and that this method has the advantage of being able to determine an approximate inverse system independently of the plant zeros. The proposed scheme uses only the available input and output data and the stability using approximate inverse systems is analyzed. Finally, the results of computer simulation are presented to show the effectiveness of the proposed method.

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