Parameters identification of Bouc–Wen hysteresis model for piezoelectric actuators using hybrid adaptive differential evolution and Jaya algorithm

Sơn, Nguyễn Ngọc; Kien, Cao Van; Ánh, Hồ Phạm Huy

doi:10.1016/j.engappai.2019.103317

Cited by 72 publications

(30 citation statements)

References 47 publications

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“…The feedforward control of an inverse multiplicative structure with the multivariable Bouc-Wen model can avoid additional computation [5]. Hybrid adaptive differential evolution and the Jaya algorithm are utilized to identify the parameters of the Bouc-Wen model of the PA [6].…”

Section: Introductionmentioning

confidence: 99%

A Compound Control Based on the Piezo-Actuated Stage with Bouc–Wen Model

Fang

Jia

et al. 2019

Micromachines

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The piezoelectric actuator (PA) is one of the most commonly used actuators in a micro-positioning stage. But its hysteresis non-linearity can cause error in the piezo-actuated stage. A modified Bouc–Wen model is presented in this paper to describe the hysteresis non-linearity of the piezo-actuated stage. This model can be divided into two categories according to the input frequency: rate-independent type and rate-dependent type. A particle swarm optimization method (PSO) is employed to identify these parameters of the Bouc–Wen hysteresis model. An inverse model feedforward compensator is established based on the modified Bouc–Wen model. The fuzzy proportional-integral-derivative (PID) controller combined with the feedforward compensator is implemented to the piezo-actuated stage. The experimental results indicate that the proposed control strategy can compensate for the hysteresis phenomenon.

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Section: Introductionmentioning

confidence: 99%

A Compound Control Based on the Piezo-Actuated Stage with Bouc–Wen Model

Fang

Jia

et al. 2019

Micromachines

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“…Several control structures have been suggested for free positioning as open as well as closed-loop approaches. Impedance control schemes, sliding mode as well as robust control which have been coupled with adaptive strategies have been used in such cases [5][6][7][8][9]. The significant drawback of the above presented control schemes is the elimination of any contact force.…”

Section: Literature Reviewmentioning

confidence: 99%

Sliding Mode Contact Force Control of n-Dof Robotics by Force Estimation

Namnabat

Zaeri

Vahedi

2020

mjee

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Control of the force exerted on an object is important for boosting system performance in robotics manipulators. Any undesired applied force may leave remarkable effects on the system, with the potential to damage the object. In addition, measuring external force is another challenge associated with such cases. Proposing an appropriate force estimation algorithm is a solution to overcome this deficiency. In this research, a control strategy is proposed to control the external force applied on the n-dof robotics. To eliminate force measurement in the controller, a force estimation strategy based on a disturbance observer is employed. Subsequently, a sliding-mode based control is implemented to cope with the force estimation error. The closed-loop stability of the system in the presence of estimated force is analytically considered. The proposed algorithm was implemented on piezoelectric actuators as the experimental setup. The experimental results confirm that by employing the proposed control scheme, precise force control is achievable. The force estimation algorithm can also suitably estimate external force.

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“…With PZT, the hysteresis characteristics containing highly nonlinear features can be illustrated based on Bouc-Wen scheme. [50][51][52] The PZT dynamic presentation regarding the Bouc-Wen scheme and disturbances is presented as (1):…”

Section: Introductionmentioning

confidence: 99%

Hysteresis compensation and adaptive control based evolutionary neural networks for piezoelectric actuator

2021

Self Cite

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This manuscript introduces a new adaptive inverse neural (AIN) control method applied to precisely track the piezoelectric (PZT) actuator displacement.First, a 3-layer neural network optimized by the enhanced differential evolution technique which modifies a mutation scheme and provides suggestions for selecting mutant coefficient F, crossover coefficient CR, and population size NP, is used to identify the inverse nonlinearity hysteresis structure of the PZT actuator. Second, a feed-forward control based on the identified model is proposed to compensate for the PZT hysteresis effect. Third, the Lyapunov stability principle is used to design and implement an adaptive law-based neural sliding mode model plus the feed-forward compensator to ensure that the whole PZT plant is operated in asymptotical stability. The experiment results demonstrate the proposed AIN controller proves superiority in comparison with other advanced control methods. K E Y W O R D S adaptive inverse neural controller, back-propagation, enhanced differential evolution, hybrid adaptive inverse neural control, Lyapunov stability concept

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Parameters identification of Bouc–Wen hysteresis model for piezoelectric actuators using hybrid adaptive differential evolution and Jaya algorithm

Cited by 72 publications

References 47 publications

A Compound Control Based on the Piezo-Actuated Stage with Bouc–Wen Model

A Compound Control Based on the Piezo-Actuated Stage with Bouc–Wen Model

Sliding Mode Contact Force Control of n-Dof Robotics by Force Estimation

Hysteresis compensation and adaptive control based evolutionary neural networks for piezoelectric actuator

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