Modeling and control of hysteresis in magnetostrictive actuators

Tan, Xiaobo; Baras, John S.

doi:10.1016/j.automatica.2004.04.006

Cited by 387 publications

(293 citation statements)

References 26 publications

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“…Refer to [18] for a description of the experimental setup. The displacement output y of the actuator is controlled by the magnetic field generated through the current I in a coil.…”

Section: Resultsmentioning

confidence: 99%

Control of hysteresis in smart actuators with application to micro-positioning

Tan

Baras

Krishnaprasad

2005

Systems & Control Letters

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Hysteresis in smart material actuators makes the effective use of these actuators quite challenging. The Preisach operator has been widely used to model smart material hysteresis. Motivated by positioning applications of smart actuators, this paper addresses the value inversion problem for a class of discretized Preisach operators, i.e., to find an optimal input trajectory given a desired output value. This problem is solved through optimal state transition of a finite state machine (FSM) that corresponds to the discretized Preisach operator. A state-space reduction scheme for the FSM is developed, which significantly saves the memory and the computation time. Experimental results on micro-positioning control of a magnetostrictive actuator are presented to demonstrate the effectiveness of the proposed approach.

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“…Refer to [18] for a description of the experimental setup. The displacement output y of the actuator is controlled by the magnetic field generated through the current I in a coil.…”

Section: Resultsmentioning

confidence: 99%

Control of hysteresis in smart actuators with application to micro-positioning

Tan

Baras

Krishnaprasad

2005

Systems & Control Letters

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“…Theorem 1: Consider the closed-loop system consisting of the plant (1) with the unknown hysteresis nonlinearities (2), and the control and adaptation laws (35)-(40). Under Assumptions 1-6, given some initial conditions 1, 2, ..., n), belong in Ω 0 , we can conclude that the overall closed-loop neural control system is semi-globally uniformly ultimately bounded (SGUUB) in the sense that all of the signals in the closed-loop system are bounded i.e., the states and weights in the closed-loop system will remain in the compact set defined by…”

Section: Consider the Following Lyapunov Function Candidatementioning

confidence: 99%

“…The objective is to design adaptive neural control v(t) for system (1) (2) such that all signals in the closed-loop system are bounded, while the tracking error converges to a small neighborhood of zero.…”

Section: A Problem Formulationmentioning

confidence: 99%

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Adaptive neural control for uncertain nonlinear systems in pure-feedback form with hysteresis input

Ren

Lee

et al. 2008

2008 47th IEEE Conference on Decision and Control

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Abstract-In this paper, adaptive neural control is investigated for a class of unknown nonlinear systems in purefeedback form with the generalized Prandtl-Ishlinskii hysteresis input. The non-affine problem both in the pure-feedback form and in the generalized Prandtl-Ishlinskii hysteresis input function is solved by adopting the Mean Value Theorem. By utilizing Lyapunov synthesis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small neighborhood of zero. Simulation results are provided to illustrate the performance of the proposed approach.

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“…Among other mathematical hysteresis models, Preisach model [6][7][8] is the more commonly used model. Maxwell slip model [9] and hysteron model [10] are some other options available.…”

Section: Introductionmentioning

confidence: 99%

Adaptive rate-dependent feedforward controller for hysteretic piezoelectric actuator

Tan

Widjaja

Latt

et al. 2008

2008 IEEE International Conference on Robotics and Automation

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Abstract-With the increasing popularity of actuators involving smart materials like piezoelectric, control of such materials becomes important. The existence of the inherent hysteretic behavior hinders the tracking accuracy of the actuators. To make matters worse, the hysteretic behavior changes with rate. One of the suggested ways is to have a feedforward controller to linearize the relationship between the input and output. Thus, the hysteretic behavior of the actuator must be first modeled by sensing the relationship between the input voltage and output displacement. Unfortunately, the hysteretic behavior is dependent on individual actuator and also environmental conditions like temperature. In this fast moving world, time is money and it is very costly to model the hysteresis regularly. In addition, the hysteretic behavior of the actuators also changes with age. Base on the studies done on the phenomena hysteretic behavior with rate, this paper proposes an adaptive rate-dependent feedforward controller with Prandtl-Ishlinskii (PI) hysteresis operators for piezoelectric actuators. This adaptive controller is achieved by adapting the coefficients to manipulate the weights of the play operators. Actual experiments are conducted to demonstrate the effectiveness of the adaptive controller.

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Modeling and control of hysteresis in magnetostrictive actuators

Cited by 387 publications

References 26 publications

Control of hysteresis in smart actuators with application to micro-positioning

Control of hysteresis in smart actuators with application to micro-positioning

Adaptive neural control for uncertain nonlinear systems in pure-feedback form with hysteresis input

Adaptive rate-dependent feedforward controller for hysteretic piezoelectric actuator

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