Self-tuning minimum-variance control of nonlinear systems of the Hammerstein model

1981 ◽  
Vol 26 (4) ◽  
pp. 959-961 ◽  
Author(s):  
K. Anbumani ◽  
L. Patnaik ◽  
I. Sarma
Keyword(s):  
Nonlinear Systems ◽  
Minimum Variance ◽  
Hammerstein Model ◽  
Minimum Variance Control ◽  
Self Tuning

scholarly journals Design of Self-Tuning Regulator for Large-Scale Interconnected Hammerstein Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Mourad Elloumi ◽  
Samira Kamoun
Keyword(s):  
Large Scale ◽  
Recursive Algorithm ◽  
Minimum Variance ◽  
Parametric Estimation ◽  
Hammerstein Model ◽  
Stochastic Nonlinear Systems ◽  
Hydraulic Systems ◽  
Self Tuning ◽  
Variance Approach ◽  
Error Method

This paper deals with the self-tuning regulator for large-scale stochastic nonlinear systems, which are composed of several interconnected nonlinear monovariable subsystems. Each interconnected subsystem is described by discrete Hammerstein model with unknown and time-varying parameters. This self-tuning control is developed on the basis of the minimum variance approach and is combined by a recursive algorithm in the estimation step. The parametric estimation step is performed on the basis of the prediction error method and the least-squares techniques. Simulation results of the proposed self-tuning regulator for two interconnected nonlinear hydraulic systems show the reliability and effectiveness of the developed method.

Self-tuning strategy for a minimum variance control system of a highly disturbed process

2019 ◽  
Vol 46 ◽  
pp. 49-62 ◽  
Author(s):  
Ioan Filip ◽  
Cristian Vasar ◽  
Iosif Szeidert ◽  
Octavian Prostean
Keyword(s):  
Control System ◽  
Minimum Variance ◽  
Minimum Variance Control ◽  
Self Tuning ◽  
Tuning Strategy

Neuro-Fuzzy Generalized Minimum Variance Control of Nonlinear Systems

2004 ◽  
Vol 37 (21) ◽  
pp. 741-746 ◽  
Author(s):  
Sergio E. Pinto Castillo ◽  
Mike J. Grimble ◽  
Reza Katebi
Keyword(s):  
Nonlinear Systems ◽  
Minimum Variance ◽  
Minimum Variance Control ◽  
Neuro Fuzzy

scholarly journals Direct and indirect self-tuning generalized minimum variance control

2019 ◽  
Vol 16 (2) ◽  
pp. 149-160
Author(s):  
Hayder Kareem ◽  
Ali Abdulrazzak Jasim ◽  
Mohammed Yousif ◽  
Thamer Abdullah
Keyword(s):  
Minimum Variance ◽  
Minimum Variance Control ◽  
Self Tuning

Self-Tuning Generalized Minimum Variance Control Based on On-Demand Type Feedback Controller

2016 ◽  
Vol 28 (5) ◽  
pp. 674-680 ◽  
Author(s):  
Akira Yanou ◽  
◽  
Mamoru Minami ◽  
Takayuki Matsuno
Keyword(s):  
Design Method ◽  
Strong Stability ◽  
Minimum Variance ◽  
Feedback Controller ◽  
Feedback Signal ◽  
Minimum Variance Control ◽  
On Demand ◽  
Control Objective ◽  
Self Tuning ◽  
Type Feedback

[abstFig src='/00280005/08.jpg' width='300' text='Feedback signal is generated on demand' ] This paper proposes a design method of self-tuning generalized minimum variance control based on on-demand type feedback controller. A controller, such as generalized minimum variance control (GMVC), generalized predictive control (GPC) and so on, can be extended by using coprime factorization. Then new design parameter is introduced into the extended controller, and the parameter can re-design the characteristic of the extended controller, keeping the closed-loop characteristic that way. Although strong stability systems can be obtained by the extended controller in order to design safe systems, focusing on feedback signal, the extended controller can adjust the magnitude of the feedback signal. That is, the proposed controller can drive the magnitude of the feedback signal to zero if the control objective was achieved. In other words the feedback signal by the proposed method can appear on demand of achieving the control objective. Therefore this paper proposes on-demand type feedback controller using self-tuning GMVC for plant with uncertainty. A numerical example is shown in order to check the characteristic of the proposed method.

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