Nonlinear Hammerstein model identification using genetic algorithm

Akramizadeh, Ali; Farjami, Ali Akbar; Khaloozadeh, Hamid

doi:10.1109/icais.2002.1048126

Cited by 25 publications

(6 citation statements)

References 6 publications

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“…Akramizadeh et al [36] initially proposed the GA for the identification of the Hammerstein system. They used GA for determining the correct nonlinear function structure and parameters, as well as the number [37], the piezoceramic actuator is identified by the Hammerstein-based model and the parameters are identified using GA.…”

Section: Related Workmentioning

confidence: 99%

Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

2022

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Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics algorithms are being introduced on an annual basis and applications that are more new are gradually being discovered. This paper presents a survey for the years 2011-2021 on multiple metaheuristics algorithms, particularly swarm and evolutionary algorithms, to identify a nonlinear block-oriented model called the Hammerstein model, mainly because such model has garnered much interest amidst researchers to identify nonlinear systems. Besides introducing a complete survey on the various population-based algorithms to identify the Hammerstein model, this paper also investigated some empirically verified actual process plants results. As such, this article serves as a guideline on the fundamentals of identifying nonlinear block-oriented models for new practitioners, apart from presenting a comprehensive summary of cutting-edge trends within the context of this topic area.

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Section: Related Workmentioning

confidence: 99%

Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

2022

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“…AGA takes advantage of adaptive crossover probability and mutation probability [3]- [6], [9]. The structure of AGA is shown as follows.…”

Section: E Adaptive Genetic Algorithm (Aga)mentioning

confidence: 99%

“…Most of methods used in the system identification are based on the assumption that the searching space of identified system model is continuous and differentiable. If the searching space is undifferentiable or parameter span is nonlinear, traditional recurrent methods can not gain the global optimization [2][3][4][5][6][7][8][9]. However, Genetic Algorithm (hereafter referred to as GA) does not need to assume that the searching space is differentiable or continuous.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear System Identification Based on Genetic Algorithm and Grey Function

Wang

2007

2007 IEEE International Conference on Automation and Logistics

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The paper presents a method for the identification of nonlinear system parameters by using an improved Genetic Algorithm and Grey Function. The paper firstly outlines several commonly used nonlinear identification methods such as RLS, RIV and COR and also their drawbacks. Then, a method based on the Genetic Algorithm and Grey Function is proposed and given in detail in the paper. Finally, a simulation experiment to TV set production data of an electronic factory was carried out. The simulations show that the method can gain good results and is also simple and effective.

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“…In order to directly apply controller by simulation at the real experiment, system identification is required. In such case, genetic algorithm is one of the best identification methods, because it is good searching algorithm without solving any differential equation and can obtain optimal data within boundary [10][11][12].…”

Section: System Configurationmentioning

confidence: 99%

Nonlinear motor control using dual feedback controller

Kong

Jung

Lee

et al. 2005

31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005.

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This paper is concerned with the control of multiple nonlinearities included in a humanoid robot system. A humanoid robot has some problems of the structural instability basically, which leads to consider the control of multiple nonlinearities caused by driver parts as well as gear reducer. Saturation and backlash are typical examples of nonlinearities in the system. The conventional algorithms of backlash control are based on fuzzy algorithm, disturbance observer and neural network, etc. However, it is not easy to control the system that is employed by only single algorithm because the system includes multiple nonlinearities. In this paper, a switching PID is considered for a control of saturation, and a dual feedback algorithm is proposed for a backlash control. To implement the above algorithms, the system identification is firstly performed for the minimization of the difference between simulation and experiment. After that, the switching PID gains are determined using genetic algorithm for removing limit cycle by saturation. The control algorithm is applied by dual feedback concept based on disturbance observer. All the processes are investigated through simulations and are verified experimentally in a real humanoid system.

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Nonlinear Hammerstein model identification using genetic algorithm

Cited by 25 publications

References 6 publications

Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

Nonlinear System Identification Based on Genetic Algorithm and Grey Function

Nonlinear motor control using dual feedback controller

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