• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.2
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• pp.324-330
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• 2012

In this paper, we presents a robust control system design for compensating hysteresis of a piezoelectric actuator-based actuation unit. First, the dynamics between the input voltage and the output displacement of the actuation unit are unravelled via a non-parametric system identification method. From the dynamic characteristics of those experimental transfer functions, a parametric model is then derived, whose dynamics match those of the non-parametric ones under various conditions on input voltages. A robust controller is constructed on the basis of this parametric model in order not only to effectively compensate the hysteresis of the actuation unit but also to guarantee the robust stability. Extensive experiments show that the proposed robust control system successfully mitigate the effect of the hysteresis and improve the tracking capability of the actuation unit.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.7 s.172
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• pp.94-101
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• 2005

This work proposes a new method for describing the hysteresis non-linearity of a piezoelectric actuator. The hysteresis behaviour of piezoelectric actuators, including the minor loop trajectory, are modeled by geometrical relationship between a reference major loop and its minor loops. This hysteresis model is transformed into inverse hysteresis model in order to output compensated voltage with regard to the given input displacement. A feedforward neural network, which is trained by a feedback PID control module, is incorporated to the inverse hysteresis model to compensate unknown dynamics of the piezoelectric system. To show the feasibility of the proposed feedforward-feedback controller, some experiments have been carried out and the tracking performance was compared to that of simple PTD controller.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.11a
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• pp.267-268
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• 2007

• The Transactions of the Korean Institute of Power Electronics
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• v.2 no.2
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• pp.35-40
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• 1997

The hysteresis control method has been frequently used for current control of power conversion equipments or motor drive systems. This method makes the measured signal follow the reference signal by changing the control signal whenever the error signal exceeds the preset band width. In this paper, hysteresis control method with fast response characteristics is applied for active noise control to suppress acoustic noise. Both Pentium processor and sound blaster 16 are used for experimental implementation, which executes A/D, D/A conversion and also is used as operating source of loudspeaker for audible noise cancellation.

• The Transactions of the Korean Institute of Power Electronics
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• v.14 no.6
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• pp.466-471
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• 2009

The vector control algorithm has come to be used as induction motor for high performance torque and response improvement. This paper deals with new control algorithm which improves the performance of the rotor flux control by combining the conventional current controller with the flux controller and the hysteresis controller. In voltage shortage condition, proposed algorithm shows that the response and the stability can be improved both in the simulation results and in the experimental results.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.11
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• pp.9-15
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• 1999

A piezoelectric actuator is widely used in precision positioning applications due to its excellent positioning resolution. However, the piezoelectric actuator lacks in repeatability because of its inherently high hysteresis characteristic between voltage and displacement. In this paper, a controller is proposed to compensate the hysteresis nonlinearity. The controller is composed of a PID and a neural network part in parallel manner. The output of the PID controller is used to teach the neural network controller by the unsupervised learning method. In addition, the PID controller stabilizes the piezoelectric actuator in the beginning of the learning process, when the neural network controller is not learned. However, after the learning process the piezoelectric actuator is mainly controlled by the neural netwok controller. In this paper, the excellent tracking performance of the proposed controller was verified by experiments and was compared with the classical PID controller.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.219-224
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• 1996

Piezoelectric actuators exhibit limited accuracy in tracking control due to their hysteresis nonlinearity. In this study a digital tracking control approach for a piezoelectric actuator based on incorporating a feedback linearization loop with a PID feedback controller is presented. The hysteresis nonlinearity of the piezoelectric actuator is modeled in the feedback compensation loop using the Maxwell slip model. Experiments were performed on a piezoelectric 2-axis linear positioner for tracking linearly decaying sinusoidal waveforms and circles. The experimental results show that the tracking control performance is noticeably improved by augmenting the feedback loop with a model of hysteresis in the feedback compensation loop.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.630-637
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• 2003

This paper proposes a new modeling scheme to describe the hysteresis and the dynamic characteristics of piezoelectric actuators in the inchworm and develops a control algorithm for the precision motion control. From the analysis of piezoelectric actuator behaviors, the hysteresis can be described by the functions of a maximum input voltage. The dynamic characteristics are also identified by the frequency domain modeling technique based on the experimental data. For the motion control, the hysteresis behavior is compensated by the inverse hysteresis model. The dynamic stiffness of an inchworm is generally low compared to its driving condition, so mechanical vibration may degenerate the motion accuracy of the inchworm. Therefore, the sliding mode control and the Kalman filter are developed for the precision motion control of the inch-warm. To demonstrate the effectiveness of the proposed modeling schemes and control algorithm, experiment validations are performed.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.5
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• pp.541-546
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• 2010

When a flexible arm is rotated by a motor about an joint axis, transverse vibration may occur. The motor torque should be controlled in such a way that the moter rotates by a specified angle, while simultaneously stabilizing vibration of the flexible arm so that it is arrested at the end of rotation. In this paper, the dynamic model of flexible robot arm is modeled by using Bernoulli-Euler beam theory and Lagrange equation. Nonlinear control with hysteresis deadzone using the sliding sector theory with continued input function in the sector is proposed.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.576-583
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• 2000

A robust position control using a sliding mode controller is adopted for the stable dynamic walking of the biped. For the biped robot that is modeled with 14 degrees of freedom rigid bodies using the method of the multibody dynamics, the joint angles for simulation are obtained by the velocity transformation matrix using the given Cartesian foot and trunk trajectories. Hertz force model and Hysteresis damping element which is used in explanation of the energy dissipation during contact with ground are used for modeling of the ground reactions during the simulation. By the obtained that forces which contains highly confused noise elements and the system modeling uncertainties of various kinds such as unmodeled dynamics and parameter inaccuracies, the biped system will be unstable. For that problems, we are adopting a nonlinear robust control using a sliding mode controller. Under the assumption that the esimation error on the unknown parameters is bounded by a given function, that controller provides a successful way to preserve stability and achieve good performance, despite the presence of strong modeling imprecisions or uncertainties.