Multiple model reduction approach using gap metric and stability margin for control nonlinear systems

2016 ◽  
Vol 15 (1) ◽  
pp. 267-273 ◽  
Author(s):  
Ali Zribi ◽  
Mohamed Chtourou ◽  
Mohamed Djemel
Keyword(s):  
Nonlinear Systems ◽  
Model Reduction ◽  
Stability Margin ◽  
Multiple Model ◽  
Gap Metric ◽  
Reduction Approach

Multimodel Control of Nonlinear Systems: An Improved Gap Metric and Stability Margin-Based Method

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Mahdi Ahmadi ◽  
Mohammad Haeri
Keyword(s):  
Nonlinear Systems ◽  
Controller Design ◽  
A Priori ◽  
Stability Margin ◽  
Threshold Value ◽  
Performance Criteria ◽  
Local Models ◽  
Gap Metric ◽  
Maximum Stability ◽  
And Performance

This paper presents a new multimodel controller design approach incorporating stability and performance criteria. The gap metric is employed to measure the distance between local models. An efficient method based on state feedback strategy is introduced to improve the maximum stability margin of the local models. The proposed method avoids local model redundancy, simplifies the multimodel controller structure, and supports employing of many linear control techniques, while does not rely on a priori experience to choose the gridding threshold value. To evaluate the proposed method, three benchmark nonlinear systems are studied. Simulation results demonstrate that the method provides the closed-loop stability and performance via a simple multimodel structure in comparison with the opponents.

An integrated best–worst decomposition approach of nonlinear systems using gap metric and stability margin

2020 ◽  
pp. 095965182094965
Author(s):  
Mahdi Ahmadi ◽  
Mohammad Haeri
Keyword(s):  
Nonlinear Systems ◽  
Robust Stability ◽  
Stability Margin ◽  
Distance Matrix ◽  
Local Model ◽  
Local Models ◽  
Continuous Stirred Tank Reactor ◽  
Decomposition Approach ◽  
Gap Metric ◽  
Highly Nonlinear

This article uses gap metric method to design a multi-model controller for nonlinear systems. In order to decompose the nonlinear system into a reduced nominal local models bank as much as possible, and assure the closed-loop robust stability and performance, the decomposition and designing of local controllers are integrated. To this end, robust stability, performance, and gap metric are incorporated to build a binary distance matrix that supports defining the driving and dependence powers for each local model. Then a best–worst analysis is employed considering the driving and dependence powers to find out the nominal local models. The proposed approach screens the value of all local models to choose each nominal local model. As a result, the global multi-model controller has a simple structure and avoids the computational complexity issues. To evaluate the effectiveness of the proposed method, two highly nonlinear systems, pH neutralization and continuous stirred tank reactor process, are simulated.

Adaptive Filter Design for FDI in Nonlinear Systems Based on Multiple Model Approach

2003 ◽  
Vol 36 (5) ◽  
pp. 645-650 ◽  
Author(s):  
D. Theilliol ◽  
M. Rodrigues ◽  
M. Adam-Medina ◽  
D. Sauter
Keyword(s):  
Nonlinear Systems ◽  
Adaptive Filter ◽  
Filter Design ◽  
Multiple Model ◽  
Model Approach
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