System Identification of Micro Piezoelectric Actuators via Rate-Dependent Prandtl-Ishlinskii Hysteresis Model Based on a Modified PSO Algorithm

Feng, Ying; Li, Ying

doi:10.1109/tnano.2020.3034965

Cited by 29 publications

(10 citation statements)

References 34 publications

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“…PSO and its improved version (Feng and Li, 2020) are relatively mature algorithms in the identification of piezoelectric hysteresis problems. However, in some cases (Wang and Chen, 2017), the advantages of PSO cannot be fully used.…”

Section: Further Discussionmentioning

confidence: 99%

A novel identification approach of Bouc–Wen model parameter for piezoelectric hysteresis characteristic based on a modified whale optimization algorithm

Wang

Zhou

2022

COMPEL

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Purpose Due to the non-linear nature of the hysteresis behavior, the accurate identification of the parameters of the Bouc–Wen hysteresis model is still a challenging problem. The purpose of this paper is to explore the potential of a heuristic improved whale optimization algorithm (IWOA) to accurately identify the model parameters, which has never been applied to the field of piezoelectric hysteresis identification. Design/methodology/approach Based on the analysis of the Bouc–Wen model structure and WOA optimization process, an approach that can fully exploit the potential of WOA is proposed. In this work, the position updating formula is improved by introducing non-linear weights, and the convergence factor formula is modified. And thus, the iteration speed, accuracy and stability of the classical WOA can be improved. Findings The experimental results show that the model output is in good agreement with the response of the real piezoelectric platform. Compared with the standard WOA and particle swarm optimization algorithms, the search performance of the proposed IWOA is better than those two competitors in terms of convergence speed and identification accuracy. Originality/value An IWOA is proposed according to the properties of the Bouc–Wen model and piezoelectric hysteresis. It has been approved that the algorithm has a good prospect in the identification of piezoelectric hysteresis systems. Furthermore, this method is easy to implement and is a good candidate algorithm to identify Bouc–Wen model parameters.

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Section: Further Discussionmentioning

confidence: 99%

A novel identification approach of Bouc–Wen model parameter for piezoelectric hysteresis characteristic based on a modified whale optimization algorithm

Wang

Zhou

2022

COMPEL

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“…The nonlinear hysteresis model is expressed by the Prandtl-Ishlinskii (PI) model. [38][39][40] The PI model is comprises multiple backlash operators, and its expression is…”

Section: Drive System Modelingmentioning

confidence: 99%

Integration modeling and control of a 12-degree-of-freedom macro–micro dual parallel manipulator

Lin

Gao

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Parallel manipulator systems with large workspace and high-accuracy positioning are attractive for a variety of accuracy engineering applications. In this article, a new 12-DOF macro–micro dual parallel manipulator is presented, which comprises a 6-PSU macro symmetrical parallel manipulator and a 6-SPS micro orthogonal parallel manipulator. This novel mechanism can not only realize a large range of movement but also accurate positioning. To achieve high-accuracy control, a macro–micro integrated model of the macro–micro dual parallel mechanism is proposed, including a mechanical system model and drive system model. Aiming at the trajectory planning for a multi-degree-of-freedom (DOF) manipulator, a smooth optimization trajectory planner for the 12-DOF macro–micro dual parallel manipulator is presented to realize smooth trajectory generation. A high-accuracy motion controller combining feedback and feedforward is developed based on the drive system model. Finally, the experiment well validates the correctness of the integrated model and motion control algorithm and reveals that the proposed mechanism is expected to be adopted into micro scale manipulations.

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“…Using the golden section rate to divide the solution space, different inertia weights and normal distribution improve the global search ability and convergence speed of the algorithm to obtain high-quality solutions. Feng et al [22] proposed a modified PSO algorithm to identify micro piezoelectric actuators. This method improves the ability of global optimization and ensures the accuracy of parameter identification.…”

mentioning

confidence: 99%

“…Simulation results demonstrate the effectiveness of the algorithm. To highlight the advantages of the proposed algorithm, a comparative study is also implemented, and it is proved that the convergence speed, global search ability, and robustness of SNPSO are superior to classical PSO [19] , PSO with Vol.8, No.1, 2023 PBM • Basis Weight Control System time-varying acceleration coefficients (PSO-TVAC) [29] , and modified particle swarm optimization (MPSO) [22] .…”

mentioning

confidence: 99%

Novel Parameter Identification Method for Basis Weight Control Loop of Papermaking Process

Shen¹,

Tang²,

Liu³

2023

Paper and Biomaterials

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The basis weight control loop of the papermaking process is a non-linear system with time-delay and time-varying. It is impractical to identify a model that can restore the model of real papermaking process. Determining a more accurate identification model is very important for designing the controller of the control system and maintaining the stable operation of the papermaking process. In this study, a strange nonchaotic particle swarm optimization (SNPSO) algorithm is proposed to identify the models of real papermaking processes, and this identification ability is significantly enhanced compared with particle swarm optimization (PSO). First, random particles are initialized by strange nonchaotic sequences to obtain high-quality solutions. Furthermore, the weight of linear attenuation is replaced by strange nonchaotic sequence and the time-varying acceleration coefficients and a mutation rule with strange nonchaotic characteristics are utilized in SNPSO. The above strategies effectively improve the global and local search ability of particles and the ability to escape from local optimization. To illustrate the effectiveness of SNPSO, step response data are used to identify the models of real industrial processes. Compared with classical PSO, PSO with timevarying acceleration coefficients (PSO-TVAC) and modified particle swarm optimization (MPSO), the simulation results demonstrate that SNPSO has stronger identification ability, faster convergence speed, and better robustness.

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System Identification of Micro Piezoelectric Actuators via Rate-Dependent Prandtl-Ishlinskii Hysteresis Model Based on a Modified PSO Algorithm

Cited by 29 publications

References 34 publications

A novel identification approach of Bouc–Wen model parameter for piezoelectric hysteresis characteristic based on a modified whale optimization algorithm

A novel identification approach of Bouc–Wen model parameter for piezoelectric hysteresis characteristic based on a modified whale optimization algorithm

Integration modeling and control of a 12-degree-of-freedom macro–micro dual parallel manipulator

Novel Parameter Identification Method for Basis Weight Control Loop of Papermaking Process

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