Speed control of ultrasonic motors using neural network

Senjyu, Tomonobu; Miyazato, Hiroshi; Yokoda, S.; Uezato, K.

doi:10.1109/63.668092

Cited by 63 publications

(14 citation statements)

References 8 publications

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“…A USM (PUMR 40E, Piezoelectric Technology Co., Ltd., Seoul, Korea) shown in Figure 2 generated torque is also affected by the temperature increase because the motor operates on a frictionbased mechanism [23]. In addition, the speed is temperature sensitive; a temperature increase results in a decrease in speed [24].…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, the temperature increase seriously degrades the motor output characteristics and therefore the motor performance [22]. The generated torque is also affected by the temperature increase because the motor operates on a friction-based mechanism [23]. In addition, the speed is temperature sensitive; a temperature increase results in a decrease in speed [24].…”

Section: Introductionmentioning

confidence: 99%

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Measuring the Temperature Increase of an Ultrasonic Motor in a 3-Tesla Magnetic Resonance Imaging System

2017

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This paper aims to evaluate the temperature increase caused by a 3.0-T magnetic resonance imaging (MRI) system on an ultrasonic motor (USM) used to actuate surgical robots in the MRI environment. Four fiber-optic temperature sensors were attached to the USM. Temperature was monitored outside the five-Gauss boundary and then inside the bore for 20 min while the USM was powered on. The USM temperature was tested for two states of the scanner, "off" and "on", by employing common clinical imaging sequences and echo planar imaging sequences. The USM showed a slight temperature increase while operating in the static field of the MRI. A considerable temperature increase (~10 • C) was observed when the scanner was on. The temperature increased to 60 • C, which is beyond the acceptable safe temperature and can result in thermal burns. Most of the temperature increase (80%) was due to effects of the static field on the motion of the rotating parts of the motor, while the remainder (20%) derived from heat deposited in the conductive components of the USM due to radiofrequency pulses and gradient field changes. To solve the temperature increase, the metal components of the USM's case can be replaced by silicon carbide.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measuring the Temperature Increase of an Ultrasonic Motor in a 3-Tesla Magnetic Resonance Imaging System

2017

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“…An ultrasonic motor (USM) is a newly developed motor, which has some excellent performances and useful features such as high torque at low speeds, compactness in size, no electromagnetic interference, short start-stop times, and many others. Owing to the advantages mentioned above, the USM has been used in many practical applications [1][2][3][4][5][6][7], such as microelectromechanical systems (MEMS), robots, medical instruments, cameras and aeronautics.…”

Section: Introductionmentioning

confidence: 99%

“…The USM is a peculiar motor whose driving principle is different from that of other electromagnetic-type motors and its characteristics have not been elucidated in detail. Furthermore, the USM has strongly nonlinear speed characteristics that vary with the driving conditions [7]. The operational characteristics of the USM depend on many factors, including the mode shape, the resonant frequency, the contact stiffness, the frictional characteristics, the working temperature and many others.…”

Section: Introductionmentioning

confidence: 99%

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Identification and speed control of ultrasonic motors based on neural networks

Xiao-li¹,

Liang²,

Lee³

et al. 2002

J. Micromech. Microeng.

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An ultrasonic motor (USM) is a newly developed motor that has many excellent performances, useful features and extensive applications. The operational characteristics of the USM are affected by many factors. Strongly nonlinear characteristics could be caused by the increase of temperature, the changes of load, driving frequency and voltage and many other factors. Therefore, it is difficult to perform effective control on USMs using traditional control methods based on mathematical models of systems. Recently, artificial intelligent methods based on neural networks have become the main approaches to perform USM control. However, the existing neural-network-based methods for USM control have some shortcomings, such as complex network structures, slower convergent speeds and lower convergent precision, as well as no theoretical guarantee on the convergence of control. Furthermore, it is difficult to obtain accurate control input for the USM by using a speed controller with a single control variable. In this paper, a bimodal controller is designed where both the driving frequency and amplitude of the applied voltage are used as control inputs. A novel input–output recurrent neural network (IORNN) identifier is constructed to dynamically identify the input–output relation of the ultrasonic motors. To guarantee convergence and for faster learning, the adaptive learning rates are derived using discrete-type Lyapunov stability analysis. Numerical results show that the proposed IORNN identifier can approximate the nonlinear input–output mapping of ultrasonic motors quite well. Compared with the existing method, the control precision can be increased by about three times and the convergence time can be decreased by about two times when the proposed method is employed. Good effectiveness of the proposed control scheme is also obtained for various reference speeds.

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Identification and Speed Control of Ultrasonic Motors Based on Modified Immune Algorithm and Elman Neural Networks

Zhang

2006

Rough Sets and Current Trends in Computing

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Speed control of ultrasonic motors using neural network

Cited by 63 publications

References 8 publications

Measuring the Temperature Increase of an Ultrasonic Motor in a 3-Tesla Magnetic Resonance Imaging System

Measuring the Temperature Increase of an Ultrasonic Motor in a 3-Tesla Magnetic Resonance Imaging System

Identification and speed control of ultrasonic motors based on neural networks

Identification and Speed Control of Ultrasonic Motors Based on Modified Immune Algorithm and Elman Neural Networks

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