Hysteresis Compensation and Sliding Mode Control with Perturbation Estimation for Piezoelectric Actuators

Ding, Bingxiao; Li, Yangmin

doi:10.3390/mi9050241

Cited by 34 publications

(22 citation statements)

References 23 publications

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“…The I-M compensator using iterative structure has superior performance in accuracy and response speed, but it may appear that the parameter identified by PMPI model does not satisfy Equation (16). Once this happens, the proportional gain k e u d (k) addressed in Equation (20) can be introduced to adjust the ratio between the MPI and memoryless polynomial part.…”

Section: Stability Analysismentioning

confidence: 99%

“…Because of the high accuracy and flexibility, the phenomenological model is more popular in hysteresis modeling. The phenomenological models include Preisach model [11,12], polynomial model [13,14], Bouc-Wen model [15,16], Duhem model [17,18], neural network model [19,20], Prandtl-Ishlinskii (PI) model [21][22][23], and etc. Among them, because of simple expression and analytical inverse model, the PI model is the most widely used in hysteresis modeling and compensation.…”

Section: Introductionmentioning

confidence: 99%

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Research on Asymmetric Hysteresis Modeling and Compensation of Piezoelectric Actuators with PMPI Model

Wang

Chen

et al. 2020

Micromachines

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Because of fast frequency response, high stiffness, and displacement resolution, the piezoelectric actuators (PEAs) are widely used in micro/nano driving field. However, the hysteresis nonlinearity behavior of the PEAs affects seriously the further improvement of manufacturing accuracy. In this paper, we focus on the modeling of asymmetric hysteresis behavior and compensation of PEAs. First, a polynomial-modified Prandtl–Ishlinskii (PMPI) model is proposed for the asymmetric hysteresis behavior. Compared with classical Prandtl–Ishlinskii (PI) model, the PMPI model can be used to describe both symmetric and asymmetric hysteresis. Then, the congruency property of PMPI model is analyzed and verified. Next, based on the PMPI model, the inverse model (I-M) compensator is designed for hysteresis compensation. The stability of the I-M compensator is analyzed. Finally, the simulation and experiment are carried out to verify the accuracy of the PMPI model and the I-M compensator. The results implied that the PMPI model can effectively describe the asymmetric hysteresis, and the I-M compensator can well suppress the hysteresis characteristics of PEAs.

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Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Research on Asymmetric Hysteresis Modeling and Compensation of Piezoelectric Actuators with PMPI Model

Wang

Chen

et al. 2020

Micromachines

View full text Add to dashboard Cite

show abstract

“…Therefore, many intelligent control algorithms have been proposed to achieve higher robustness and adaptability. For instance, sliding mode control has been proposed to improve the accuracy and the robustness against noise and disturbances [14,21]. A linearization control method with feedforward hysteresis compensation and proportional-integral-derivative (PID) feedback has also been proposed [22].…”

Section: Introductionmentioning

confidence: 99%

Single-Neuron Adaptive Hysteresis Compensation of Piezoelectric Actuator Based on Hebb Learning Rules

Qin

Duan

2020

Micromachines

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This paper presents an adaptive hysteresis compensation approach for a piezoelectric actuator (PEA) using single-neuron adaptive control. For a given desired trajectory, the control input to the PEA is dynamically adjusted by the error between the actual and desired trajectories using Hebb learning rules. A single neuron with self-learning and self-adaptive capabilities is a non-linear processing unit, which is ideal for time-variant systems. Based on the single-neuron control, the compensation of the PEA’s hysteresis can be regarded as a process of transmitting biological neuron information. Through the error information between the actual and desired trajectories, the control input is adjusted via the weight adjustment method of neuron learning. In addition, this paper also integrates the combination of Hebb learning rules and supervised learning as teacher signals, which can quickly respond to control signals. The weights of the single-neuron controller can be constantly adjusted online to improve the control performance of the system. Experimental results show that the proposed single-neuron adaptive hysteresis compensation method can track continuous and discontinuous trajectories well. The single-neuron adaptive controller has better adaptive and self-learning performance against the rate-dependence of the PEA’s hysteresis.

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“…Sun and Li proposed H ∞ control compensation, based on BW model [22]. And a sliding model control for the hysteresis compensation of piezo actuator with a inverse BW model was proposed by Ding and Li [23]. However, few studies has been focused on the parameters and optimization of BW model.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Rate-Dependent Hysteresis Control Compensator Design With Bouc-Wen Model Based on RMSO for Piezoelectric Actuator

2020

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Piezoelectric actuators (PAs) require high precision positioning for the applications of micro electrical mechanical systems, but it exhibits hysteresis nonlinearity which deteriorates positioning accuracy if no proper compensation is given. Hysteresis nonlinear modeling of PAs is a prime choice for hysteresis compensation. This paper proposes a novel intelligent positioning control algorithm based on Bouc-Wen (BW) model for the compensation of a bi-morph type piezoelectric actuator (PA) suffering ratedependent hysteresis. A region based mixed-species swarm optimization (RMSO) algorithm is proposed for BW modeling to capture the dynamic nonlinearity of a piezoelectric actuator which exhibits rate-dependent hysteresis. Results of numerical simulations have been disclosed to illustrate the performance enhancement of RMSO over classical algorithm while they are applied to the parameter fitting problem of BW model for experimentally acquired datasets. An model based adaptive Fuzzy neural network (Fuzzy-NN) controller of PA is utilized to compensate the hysteresis for the positioning tracking control. Experimental results also illustrate the good performance of the proposed RMSO-BW based control scheme for the hysteresis compensation control of the PA.

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Hysteresis Compensation and Sliding Mode Control with Perturbation Estimation for Piezoelectric Actuators

Cited by 34 publications

References 23 publications

Research on Asymmetric Hysteresis Modeling and Compensation of Piezoelectric Actuators with PMPI Model

Research on Asymmetric Hysteresis Modeling and Compensation of Piezoelectric Actuators with PMPI Model

Single-Neuron Adaptive Hysteresis Compensation of Piezoelectric Actuator Based on Hebb Learning Rules

Intelligent Rate-Dependent Hysteresis Control Compensator Design With Bouc-Wen Model Based on RMSO for Piezoelectric Actuator

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