PSO based parameter identification of Colman-Hodgdon hysteresis model of a piezoelectric actuator and PID feedback controller

Ounissi, Amor; Landry, M. D.; Kaddouri, Azeddine; Abdessemed, Rachid

doi:10.12988/ces.2014.3953

Cited by 3 publications

(3 citation statements)

References 27 publications

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“…Ounissi et al [7] used Coleman Hodgdon model which has two differential equations with different exponential decay to describe the rising and declining hysteresis curves.…”

Section: A Differential Equations-based Modelsmentioning

confidence: 99%

“…Lin and Lee [32] developed an intelligent integral backstepping sliding-mode controller using a recurrent neural network to eliminate the hysteresis nonlinearity of piezo-driven nano-positioning stage. Ounissi et al [7] used the PID controller to eliminate the hysteresis nonlinearity in piezoelectric actuator and the maximum relative error was 5.3%. Ge and Jouaneh [33] proposed a hybrid controller comprised of inverse Preisach model as feedforward compensation and PID control to improve the tracking accuracy for a piezoelectric system, when the amplitude of reference displacement is 12µm, the maximum error is 0.25µm.…”

Section: B Operator-based Modelsmentioning

confidence: 99%

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Iterative Learning and Fractional Order PID Hybrid Control for a Piezoelectric Micro-Positioning Platform

Zhou

Zhang

et al. 2020

IEEE Access

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The piezoelectric micro-positioning platform(PMP) has advantages in high displacement resolution and fast response, however, the serious hysteresis nonlinearity in the PMP restricts its positioning accuracy. This paper presents a discretization of Krasnosel'skii-Pokrovskii model to describe the hysteresis nonlinearity of the PMP. Then, the density function is identified online by the adaptive linear neural network. To compensate the intrinsic hysteresis nonlinearity in the PMP, a feedforward compensation control method is implemented by an innovative iterative learning control algorithm. Moreover, a hybrid control method based on iterative learning and fractional order PID control is proposed to further improve the control accuracy. A series of comparative experiments are carried out to validate the feasibility and effectiveness of the proposed control method.INDEX TERMS Piezoelectric micro-positioning platform, hysteresis nonlinearity, iterative learning control, fractional order PID.

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“…Ounissi et al [7] used Coleman Hodgdon model which has two differential equations with different exponential decay to describe the rising and declining hysteresis curves.…”

Section: A Differential Equations-based Modelsmentioning

confidence: 99%

Section: B Operator-based Modelsmentioning

confidence: 99%

Iterative Learning and Fractional Order PID Hybrid Control for a Piezoelectric Micro-Positioning Platform

Zhou

Zhang

et al. 2020

IEEE Access

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“…So far, the Duhem model, Bouc-Wen model, and Coleman-Hodgdon model are the common differential equations-based models [4]. For example, the Coleman-Hodgdon model, which had two differential equations with different exponential, was used to describe the rising and declining hysteresis curves [5]. Lin et al [6] conducted a depth research for a class of generalized Duhem models.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Tracking Control for the Piezoelectric Actuated Stage Using the Krasnosel’skii-Pokrovskii Operator

Tian

Wang

2020

Micromachines

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In this paper, a discrete second order linear equation with the Krasnosel’skii-Pokrovskii (KP) operator is used to describe the piezoelectric actuated stage. The weights of the KP operators are identified by the gradient descent algorithm. To suppress the hysteresis nonlinearity of the piezoelectric actuated stage, this paper proposes an adaptive tracking control with the hysteresis decomposition on the designed error surface. The proposed adaptive tracking controller dispenses with any form of the feed-forward hysteresis compensation and the unknown parameters of the discrete second order linear equation are adaptively adjusted. Some simulations are implemented to verify the effectiveness of the KP operators, then a series of modeling and control experiments are carried out on the piezoelectric actuated stages experimental systems. The comparative experimental results verify the feasibility of the KP operators modeling method and the adaptive tracking control method.

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

Zhou¹,

Xu²,

Zhang³

et al. 2016

Journal of Electrical Engineering and Technology

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-The hysteresis nonlinearity of magnetically controlled shape memory alloy actuator is an obstacle in the achievement of high positioning accuracy. To eliminate the influence of hysteresis nonlinearity, a PID hybrid control method which uses the inverse Prandtl-Ishlinskii model as a feedforward controller is proposed in this paper. The PID parameters are searched by particle swarm optimization with cross and heredity function. The simulation results show that when the influence of external disturbance is not considered, the positioning accuracy of open loop control method is 0.67%, and the positioning accuracy of PID hybrid control based on particle swarm optimization is 0.38%; when the influence of external disturbance is considered, the positioning accuracy of open loop control method is 3.29%, and the positioning accuracy of PID hybrid control based on particle swarm optimization is 1.65%. This indicates that the proposed control method can effectively eliminate the impact of hysteresis nonlinearity and external disturbances on the positioning accuracy.

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PSO based parameter identification of Colman-Hodgdon hysteresis model of a piezoelectric actuator and PID feedback controller

Cited by 3 publications

References 27 publications

Iterative Learning and Fractional Order PID Hybrid Control for a Piezoelectric Micro-Positioning Platform

Iterative Learning and Fractional Order PID Hybrid Control for a Piezoelectric Micro-Positioning Platform

Adaptive Tracking Control for the Piezoelectric Actuated Stage Using the Krasnosel’skii-Pokrovskii Operator

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

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