A hybrid metaheuristic algorithm for identification of continuous-time Hammerstein systems

Jui, Julakha Jahan; Ahmad, Mohd Ashraf

doi:10.1016/j.apm.2021.01.023

Cited by 44 publications

(30 citation statements)

References 24 publications

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“…A Hammerstein structure is used to represent the isometric response in the paper [31], which considers modelling of an electrically activated muscle, where they proposed the ARLS algorithm for identifying the parameters. Jui and Ahmad [72]…”

Section: Experimental Work On Identifying Hammerstein Modelmentioning

confidence: 99%

“…The experimental verification of such plants and processes is displayed in Table 1. These process plants can be classified into several groups, such as chemical plants, mechanical plants, industrial plants and engineering plants such as amplified piezoelectric tube actuators [20], intelligent pneumatic actuator (IPA) [25], electrically stimulated muscle [31], ultrasonic motor [27], proton exchange membrane fuel cell (PEMFC) [28], turntable servo system [29], hydraulic mini excavator [26], twin rotor system (TRS) and flexible manipulator system (FMS) [72], and valve stiction [30].…”

Section: Experimental Work On Identifying Hammerstein Modelmentioning

confidence: 99%

“…They claim that the GSA algorithm is outperformed by the PSO and DE by avoiding critical flaws such as premature convergence to sub-optimal solutions. In another study, novel hybrid metaheuristics called average multi-verse optimizer and sine cosine algorithm (AMVO-SCA) is proposed for the identification of  ISSN: 2302-9285 the continuous-time Hammerstein model [72]. They showed that their proposed method provides good identification accuracy than several well-known metaheuristics such as PSO, GWO, MVO and SCA.…”

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confidence: 99%

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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: Experimental Work On Identifying Hammerstein Modelmentioning

confidence: 99%

Section: Experimental Work On Identifying Hammerstein Modelmentioning

confidence: 99%

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confidence: 99%

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Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

2022

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“…Hammerstein systems in a continuous mode can be modelled using the hybrid metaheuristic algorithm. The parameters of the linear and nonlinear subsystems within such systems can be identified using the hybrid approach [7]. The cloth washing real systems can be modelled against such Hammerstein model with continuous-time transfer function, based on using the Grey Wolf Optimizer method to tune both the coefficients of the nonlinear and transfer functions of the model.…”

Section: Introductionmentioning

confidence: 99%

A multipart distributed modelling approach for an automated protection system in open-air cloth drying

Onibonoje

Ojo

2022

IJECE

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<span>There are different methods of drying wet clothes, but drying with direct sunlight is considered the best suited for the preservation of the quality and usability of the cloths. However, sudden rainfall during the drying period constitutes a major drawback of the method. This returns the cloths to a drenched state as it is practically impossible to watch the clothes dry off after washing. This paper has proposed a model for an automated system for controlled open-air fabric drying by detecting the rain and moisture status of the cloths in real-time, and capable of shielding them to safety from the rainfall and excess sun. The modelled part considers the sensing model, drying model, control model, and their validation. The implementation and evaluation stage relates the result of the validated results to the developed prototypes. The simulated results in the sensing unit indicate above 87.5% agreements with the analytical results, and the controller simulated result provides a relatively small overshoot and faster dynamic response. Manufacturers of hanger systems for cloth drying have a basis for the design and implementation of their products in the paper.</span>

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“…On the other hand, the block-oriented models for the identification of nonlinear system have become a prominent strategy to model such type of complex plants [11]. There are mainly three types of block-oriented nonlinear models present, which are the Hammerstein model, Wiener model, and Hammerstein-Wiener model.…”

Section: Introductionmentioning

confidence: 99%

Identification of the Thermoelectric Cooler Using Hybrid Multi-Verse Optimizer and Sine Cosine Algorithm Based Continuous-Time Hammerstein Model

Jui

Ahmad

Ali

et al. 2021

Cybernetics and Information Technologies

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This paper presents the identification of the ThermoElectric Cooler (TEC) plant using a hybrid method of Multi-Verse Optimizer with Sine Cosine Algorithm (hMVOSCA) based on continuous-time Hammerstein model. These modifications are mainly for escaping from local minima and for making the balance between exploration and exploitation. In the Hammerstein model identification a continuous-time linear system is used and the hMVOSCA based method is used to tune the coefficients of both the Hammerstein model subsystems (linear and nonlinear) such that the error between the estimated output and the actual output is reduced. The efficiency of the proposed method is evaluated based on the convergence curve, parameter estimation error, bode plot, function plot, and Wilcoxon’s rank test. The experimental findings show that the hMVOSCA can produce a Hammerstein system that generates an estimated output like the actual TEC output. Moreover, the identified outputs also show that the hMVOSCA outperforms other popular metaheuristic algorithms.

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A hybrid metaheuristic algorithm for identification of continuous-time Hammerstein systems

Cited by 44 publications

References 24 publications

Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

Metaheuristics algorithms to identify nonlinear Hammerstein model: a decade survey

A multipart distributed modelling approach for an automated protection system in open-air cloth drying

Identification of the Thermoelectric Cooler Using Hybrid Multi-Verse Optimizer and Sine Cosine Algorithm Based Continuous-Time Hammerstein Model

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