Hammerstein model for hysteresis characteristics of pneumatic muscle actuators

Ai, Qingsong; Peng, Yuan; Zuo, Jie; Meng, Wei; Liu, Quan

doi:10.1007/s41315-019-00084-5

Cited by 17 publications

(4 citation statements)

References 26 publications

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“…A least-squares like estimator was presented on the basis of the periodic input signals by Brouri et al [27] to achieve the parameter estimation for the Hammerstein-Wiener-like system. In [28], Ai et al used a Hammerstein-like system with hysteresis to model a pneumatic muscle actuator based on a back propagation (BP) neural network, in which model parameters were estimated using a nonlinear least squares estimator. Li et al [29] proposed an adaptive estimation method to recover the information of the Wiener-Hammerstein-like system, in which the convergence rate can be lifted by using attenuation coefficient.…”

Section: Introductionmentioning

confidence: 99%

A New Robust Identification Algorithm for Hammerstein-Like System Using Identification Error Structure

Wang

2022

IEEE Access

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A new robust identification algorithm is introduced in this study for a Hammersteinlike system based on identification error information. With the help of the half-substitution idea, the identification model is converted into a compact model where the coupling parameters are avoided. To reduce the effect of noise signals, a filter gain is proposed to obtain helpful system data. Then, on the basis of the filtered variables and developed forcing variables, the identification error information is extracted from the helpful system data. By using the identification error data, a new parameter estimation adaptive law that differs from the classic prediction error method is derived. Therefore, a new identification scheme framework is proposed and the weakness of the prediction error method is improved. Simulations and a real-life plant are presented to test the validity and practicality of the presented identification approach.The results of parameter estimation and estimation error qualitatively demonstrated the advantages of the proposed algorithm. The computational complexity and performance evaluation indicators results quantitatively indicated that the proposed algorithm produces higher estimation performance compared with the other algorithms.

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Section: Introductionmentioning

confidence: 99%

A New Robust Identification Algorithm for Hammerstein-Like System Using Identification Error Structure

Wang

2022

IEEE Access

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“…Amini Tehrani et al [31] proposed a standard deviation minimum estimator for the nonlinear system with hysteresis, in which the hysteresis curves are used to obtain the final parameters optimization. Ai et al [32] used least squares based on the special input signal to estimate the Hammerstein-like model with hysteresis. In reference [33], an adaptive identification method using gradient law is reported to nonlinear hysteresis system and used parameter projection scheme to increase the convergence performance.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification with a New Recursive Framework for Wiener–Hammerstein-Like System and Its Application

Liang

Zhang

et al. 2022

Mathematical Problems in Engineering

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In the process of actual system modeling, many systems exhibit nonlinear characteristics with memory. Thus, the parameter identification problem of the nonlinear system with memory usually appears in the system modeling. This report focuses on the nonlinear system identification of Wiener–Hammerstein-like model with memory hysteresis, in which a new recursive estimation way is introduced. In this algorithm, the estimation bias problem can be improved by introducing a data filtering technique. On the basis of the filtered data, some auxiliary matrices and vectors are proposed. Following this, the identification error variable is introduced by using auxiliary matrices and vectors with an adaptive forgetting factor. Afterward, the identification error variable is integrated into the design of parameter estimation adaptive law with recursive gain structure. By comparison with the classic estimation methods, the proposed algorithm shows an alternative identification algorithm design angle. In addition, it is strictly proved that the parameter estimation error converges to zero under a general excitation condition. Based on the results of indices M S E , compared with the existing methods, the performance improvements of the proposed method are 33.9 %, 41.26%, and 53.5%, respectively. In terms of indices P E M , the augmented performances of the developed scheme are 50%, 56.2%, and 68.4%, respectively, in comparison to the available schemes.

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“…Among all these nonlinear system identification models, the Hammerstein model is the popular one because of its easy model structure and common usage to identify nonlinear systems [12]. Hammerstein model has also been applied to model many actual plants and processes including Solid Oxide fuel cell [13], turntable servo system [14], amplified piezoelectric actuators [15] multi-axis piezoelectric micro-positioning stages [16], and pneumatic muscle actuators [17]. Moreover, there are many traditional identification methods that have been used for the identification of the Hammerstein models such as the iterative method [18], the subspace method [19], the least square method [20] and the blind approach [21].…”

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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Hammerstein model for hysteresis characteristics of pneumatic muscle actuators

Cited by 17 publications

References 26 publications

A New Robust Identification Algorithm for Hammerstein-Like System Using Identification Error Structure

A New Robust Identification Algorithm for Hammerstein-Like System Using Identification Error Structure

Parameter Identification with a New Recursive Framework for Wiener–Hammerstein-Like System and Its Application

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

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