Hybrid Control Method of Magnetically Controlled Shape Memory Alloy Actuator Based on Inverse Prandtl-Ishlinskii Model

Zhou, Miaolei; Xu, Rui; Zhang, Qi; Wang, Ziying; Zhang, Yu

doi:10.5370/jeet.2016.11.5.1457

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…The feedforward control approach is most widely used for compensating the hysteresis nonlinearity, structuring a controller by placing in a feedforward loop as a compensator with various models. These models include the Preisach model [ 15 ], the Prandtl–Ishlinskii model [ 16 , 17 , 18 ], the Krasnoselskii–Pokrovskii model [ 19 ], the Bouc–Wen model [ 20 , 21 ], the Duhem model [ 22 , 23 ], the Dahl model [ 24 ], and the neural network model [ 25 , 26 ] to suppress the undesirable behaviors. It is pointed out that the precision of open-loop control is affected by modeling errors concerning these hysteresis models; furthermore, open-loop control cannot suppress the influence of external interference.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Self-Tuning Control for a Piezoelectric Actuator

Zhang

Gao

et al. 2020

Sensors

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Piezoelectric actuators (PEA) have been widely used in the ultra-precision manufacturing fields. However, the hysteresis nonlinearity between the input voltage and the output displacement, which possesses the properties of rate dependency and multivalued mapping, seriously impedes the positioning accuracy of the PEA. This paper investigates a control methodology without the hysteresis model for PEA actuated nanopositioning systems, in which the inherent drawback generated by the hysteresis nonlinearity aggregates the control accuracy of the PEA. To address this problem, a neural network self-tuning control approach is proposed to realize the high accuracy tracking with respect to the system uncertainties and hysteresis nonlinearity of the PEA. First, the PEA is described as a nonlinear equation with two variables, which are unknown. Then, using the capabilities of super approximation and adaptive parameter adjustment, the neural network identifiers are used to approximate the two unknown variables automatically updated without any off-line identification, respectively. To verify the validity and effectiveness of the proposed control methodology, a series of experiments is executed on a commercial PEA product. The experimental results illustrate that the established neural network self-tuning control method is efficient in damping the hysteresis nonlinearity and enhancing the trajectory tracking property.

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Section: Introductionmentioning

confidence: 99%

Neural Network Self-Tuning Control for a Piezoelectric Actuator

Zhang

Gao

et al. 2020

Sensors

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“…Such models typically have a rate-independent hysteretic nature, which is their output variable does not depend on the first derivative of the input one [ 6 , 7 , 8 , 9 ]. Such as Duhem model [ 10 ], Bouc–Wen model [ 11 , 12 ], Prandtl–Ishlinskii (PI) model [ 13 , 14 ], Preisach model [ 15 ], and Krasnoselskii–Pokrovskii (KP) model [ 16 , 17 , 18 ]. The Duhem model and Bouc–Wen model are differential equation-based hysteresis models.…”

Section: Introductionmentioning

confidence: 99%

Rate Dependent Krasnoselskii-Pokrovskii Modeling and Inverse Compensation Control of Piezoceramic Actuated Stages

Nie

Liu

et al. 2020

Sensors

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The piezoceramic actuated stages have rate-dependent hysteresis nonlinearity, which is not simply related to the current and historical input, but also related to the frequency of the input signal, seriously affects its positioning accuracy. Consider the influence of frequency on hysteresis modeling, a rate-dependent hysteresis nonlinearity model that is based on Krasnoselskii–Pokrovskii (KP) operator is proposed in this paper. A hybrid optimization algorithm of improved particle swarm optimization and cuckoo search is employed in order to identify the density function of rate-dependent KP model, avoiding the blind search process caused by the high randomness of Levy’s flight in the cuckoo search algorithm, and improving the parameter identification performance. For the sake of eliminating the hysteresis characteristics, an inverse feed-forward compensation control that is based on recursive method is proposed without any additional conditions, and a feed-forward compensation controller is designed accordingly. The experimental results show that, under different frequency input signals, as compared with the classic KP model, the proposed rate-dependent KP model can accurately describe the rate-dependent hysteresis characteristics of the piezoceramic actuated stages, and the recursive inverse feed-forward compensation control method can effectively mitigate the hysteresis behaviors.

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Elman neural network-based identification of Krasnosel'skii-Pokrovskii model for magnetic shape memory alloys actuator

2017

2017 IEEE International Magnetics Conference (INTERMAG)

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Hybrid Control Method of Magnetically Controlled Shape Memory Alloy Actuator Based on Inverse Prandtl-Ishlinskii Model

Cited by 4 publications

References 25 publications

Neural Network Self-Tuning Control for a Piezoelectric Actuator

Neural Network Self-Tuning Control for a Piezoelectric Actuator

Rate Dependent Krasnoselskii-Pokrovskii Modeling and Inverse Compensation Control of Piezoceramic Actuated Stages

Elman neural network-based identification of Krasnosel'skii-Pokrovskii model for magnetic shape memory alloys actuator

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