• 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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• 2000.11a
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• pp.85-88
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• 2000

In this paper, the ultra precision positioning system for piezoelectric actuator using hysteresis compensation has been developed. Piezoelectric actuators exhibit limited accuracy in tracking control due to their hysteresis nonlinearity. The main purpose of the proposed controller is to compensate the hysteresis nonlinearity of the piezoelectric actuator. The controller is composed of a PD, hysteresis compensation and neural network part in parallel manner, at first, the excellent tracking performance of the neural network controller was verified by experiments and was compared with the classical PD controller.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.1
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• pp.22-29
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• 2003

A piezoelectric actuator yields hysteresis effect due to its composed ferroelectric. Hysteresis nonlinearty is neglected when a piezoelectric actuator moves with short stroke. However when it moves with long stroke and high frequency, the hysteresis nonlinearty can not be neglected. The hysteresis nonlinearty of piezoelectric actuator degrades the control performance in precision position control. In this paper, in order to improve the control performance of piezoelectric actuator, an inverse modeling scheme is proposed to compensate the hysteresis nonlinearty. And feedforward - feedback controller is proposed to give a good tracking performance. The Feedforward controller is an inverse hysteresis model, base on neural network and the feedback control is implemented with PID control. To show the feasibility of the proposed controller and hysteresis modeling, some experiments have been carried out. It is concluded that the proposed control scheme gives good tracking performance.

• 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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• 2004.10a
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• pp.383-388
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• 2004

A piezoelectric actuator yields hysteresis effect due to its composed ferroelectric. Hysteresis nonlinearty is neglected when a piezoelectric actuator moves with short stroke. However when it moves with long stroke and high frequency, the hysteresis nonlinearty can not be neglected. The hysteresis nonlinearty of piezoelectric actuator degrades the control performance in precision position control. In this paper, in order to improve the control performance of piezoelectric actuator, an inverse modeling scheme is proposed to compensate the hysteresis nonlinearty problem. And feedforward - feedback controller is proposed to give a good tracking performance. The Feedforward controller is inverse hysteresis model, Nueral network and PID control is used as a feedback controller. To show the feasibility of the proposed controller and hysteresis modeling, some experiments have been carried out. It is concluded that the proposed control scheme gives good tracking performance

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.505-507
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• 2004

This paper attempts to compensate the nonlinearity between the input voltage and the output displacement of the piezoelectric stack in dynamic actuation by the following two ways. Firstly, the charge steering by circuit configuration reduces the hysteresis of piezoelectric actuator remarkably. However, it makes the ripple in positioning due to the phase lag and noise induced from the elements of the long closed loop. Secondly, the feedforward control by neural network compensates the hysteresis of the piezoelectric actuators effectively with the appropriate selection of the input variables for the training. The improvement of the dynamic performance of the piezoelectric actuators by the developed linearization technique is verified by experiments.

• Smart Structures and Systems
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• v.5 no.6
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• pp.613-632
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• 2009

The present paper is concerned with the design of a proper patch actuator network in order to track a desired displacement of the sidewalls of a one-storey frame structure; both, for the static and the dynamic case. Weights for each patch of the actuator network found in our previous work were based on beam theory; in the present paper a refinement of these weights by modeling the sidewalls of the frame structure as thin plates is presented. For the sake of calculating the refined weights approximate solutions of the plate equations are calculated by an extended Galerkin method. The solutions based on the analytical plate model are compared with three-dimensional Finite Element results computed in the commercially available code ANSYS. The patch actuator network is put into practice by means of four piezoelectric patches attached to each of the two sidewalls of the frame structures, to which electric voltages proportional to the analytically refined patch weights are applied. Analytical and numerical results coincide very well over a broad frequency range.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.382-385
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• 2003

In this paper, in order to improve the control performance of piezoelectric actuator, an integrated control structure is proposed. The control structure consists of inverse hysteresis model , to compensate the hysteresis nonlinearty problem, and feedforward - feedback controller to give a good tracking performance. The inverse hysteresis model and neural network are used as feed-forward controller, and PID controller is used as a feedback controller. From diverse experiments it is concluded that the proposed control scheme gives good tracking performance than the classical control does.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.05a
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• pp.135-138
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• 2002

Experimental studies on vibration control of a composite beam with a piezoelectric actuator and an extrinsic Fabry-Perot interferometer (EFPI) have been performed using a neural network controller and an LQG controller. Vibration control performance was investigated in the nonlinear sensing range according to the vibration amplitudes. Using a neuro-controller, adaptive vibration control experiment has been performed for the structure with frequency variations, and its performance is compared with that of an LQG controller. The vibration control results show that the neuro-controller has good performance and robustness with respect to the system parameter variations.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.353-354
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• 2014

An active vibration control technique of a time-varying cantilever beam is proposed in this study. A simple in-house coil sensor instead of expensive commercial sensors was used to measure the vibrational displacement of the beam. Active band pass filters and artificial neutral net works detect the frequencies, amplitudes, and phases of the main vibration mode. The time constants of the low pass filter representing the positive position feedback controller are updated in real-time, which generates the control voltage input to actuate the piezoelectric actuator and suppress the vibration. An experiment was successfully performed to verify the algorithm for a cantilever beam, which fundamental natural frequency arbitrarily varies between 9 Hz ~ 18 Hz. The present active vibration suppression technique can be applied to variety of structures which undergoes large variation of dynamic characteristics while operating.