Real coded Integer Genetic Algorithm for parameter identification of non linear system

2011 ◽  
Author(s):  
A.C. Megherbi ◽  
H. Megherbi ◽  
A. Dendouga ◽  
K Benmahammed ◽  
A. Aissaoui
Keyword(s):  
Genetic Algorithm ◽  
Linear System ◽  
Parameter Identification ◽  
Non Linear ◽  
Non Linear System

Optimization Design of Wind/Photovoltaic Hybrid Power Systems Based on Genetic Algorithms

2013 ◽  
Vol 278-280 ◽  
pp. 1692-1695
Author(s):  
Lu Li ◽  
Zhong Fu Tan
Keyword(s):  
Genetic Algorithm ◽  
Linear System ◽  
Power Systems ◽  
Wind Power ◽  
Hybrid System ◽  
Alternative Energy ◽  
Optimization Design ◽  
Non Linear ◽  
Non Linear System ◽  
And Control

Wind power and solar energy power are clean, abundant and renewable. Wind power and photovoltaic power are important alternative energy in the world, which will contribute to adjusting energy structures and protecting environments. The genetic algorithm has the characteristics of automatic optimization and approaches the simulate stuff illimitably. Also, there has no use for accurate model on questions, which is very suitable in the non-linear system. The wind/photovoltaic hybrid systems consist with wind power generation units, photovoltaic matrix, storage battery, diesel engine and data collection and control. This paper optimized the wind/photovoltaic hybrid system using genetic algorithm. The result showed the efficiency of this algorithm in the design of this kind of non-linear system. On the other hand, this hybrid system is strongly non-linear when is running. Finally, abundant operating expenses and maintains expenses will be saved by using genetic algorithm in its dynamic management according to the change of load, wind power and irradiation.

A novel LSSVM-L Hammerstein model structure for system identification and nonlinear model predictive control of CSTR servo and regulatory control

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Akshaykumar Naregalkar ◽  
Subbulekshmi Durairaj
Keyword(s):  
Linear System ◽  
Regulatory Control ◽  
Hammerstein Model ◽  
Support Vector ◽  
Model Structure ◽  
Linear Dynamics ◽  
Vector Machines ◽  
Non Linear ◽  
Laguerre Filters ◽  
Non Linear System

Abstract A continuous stirred tank reactor (CSTR) servo and the regulatory control problem are challenging because of their highly non-linear nature, frequent changes in operating points, and frequent disturbances. System identification is one of the important steps in the CSTR model-based control design. In earlier work, a non-linear system model comprises a linear subsystem followed by static nonlinearities and represented with Laguerre filters followed by the LSSVM (least squares support vector machines). This model structure solves linear dynamics first and then associated nonlinearities. Unlike earlier works, the proposed LSSVM-L (least squares support vector machines and Laguerre filters) Hammerstein model structure solves the nonlinearities associated with the non-linear system first and then linear dynamics. Thus, the proposed Hammerstein’s model structure deals with the nonlinearities before affecting the entire system, decreasing the model complexity and providing a simple model structure. This new Hammerstein model is stable, precise, and simple to implement and provides the CSTR model with a good model fit%. Simulation studies illustrate the benefit and effectiveness of the proposed LSSVM-L Hammerstein model and its efficacy as a non-linear model predictive controller for the servo and regulatory control problem.

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