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Fuzzy Neural Network Control Method of Ultrasonic Motor Based on Basis Function Network

A technology of fuzzy neural network and ultrasonic motor, applied in the direction of adaptive control, general control system, control/regulation system, etc., can solve problems such as inability to eliminate cross-coupling

Inactive Publication Date: 2017-11-24
MINJIANG UNIV
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Problems solved by technology

While a single network cannot eliminate the influence of cross-coupling disturbances, we use a recursive radioactive basis function network-based fuzzy neural network control system for effective control

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  • Fuzzy Neural Network Control Method of Ultrasonic Motor Based on Basis Function Network
  • Fuzzy Neural Network Control Method of Ultrasonic Motor Based on Basis Function Network
  • Fuzzy Neural Network Control Method of Ultrasonic Motor Based on Basis Function Network

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Embodiment Construction

[0074] The technical solution of the present invention will be described in detail below in conjunction with the drawings.

[0075] The invention provides a fuzzy neural network control method of an ultrasonic motor based on a basis function network, such as figure 1 As shown, it includes a base 12 and an ultrasonic motor 4 arranged on the base 12. The output shaft 3 of the ultrasonic motor 4 is connected to the photoelectric encoder 1 on one side, and the output shaft 6 is connected to the flywheel inertial load 7 on the other side. The output shaft 8 of the flywheel inertial load 7 is connected to the torque sensor 10 via an elastic coupling 9, and the signal output end of the photoelectric encoder 1 and the signal output end of the torque sensor 10 are respectively connected to the control system.

[0076] The ultrasonic motor 4, the photoelectric encoder 1, and the torque sensor 10 are respectively fixed on the base 12 via the ultrasonic motor fixing bracket 5, the photoelectri...

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Abstract

The invention relates to an ultrasonic motor fuzzy neural network control method based on a base function network, comprising a base and an ultrasonic motor arranged on the base. The output shaft at one side of the ultrasonic motor is connected with a photoelectric encoder, and the output shaft at the other side of the ultrasonic motor is connected with a flywheel inertia load. The output shaft of the flywheel inertia load is connected with a torque sensor through a coupling. The signal output ends of the photoelectric encoder and the torque sensor are connected to a control system. The control system is composed of a fuzzy neural network controller based on a recursive radioactive base function network and a motor. The system of the whole controller is established on the basis of the recursive radioactive base function network, a fuzzy neural network is taken as the adjustment function, and therefore, better control performance is achieved. The control accuracy is high, the structure is simple and compact, and the using effect is good.

Description

Technical field [0001] The invention relates to the field of motor controllers, in particular to a fuzzy neural network control method of an ultrasonic motor based on a basis function network. Background technique [0002] The design of the existing ultrasonic motor recursive neural network control system considers the total set uncertainty, and the total set uncertainty includes the cross-coupled disturbances that appear in the drive system. However, a single network cannot eliminate the impact of cross-coupled disturbances. We use a recursive radioactive basis function network based fuzzy neural network control system for effective control. The system has strong robustness and can effectively control the number of fuzzy logic rules when external force interference occurs, so the fuzzy neural network control system based on the recursive radioactive basis function network can effectively improve the control efficiency of the system and further reduce The degree of influence of ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05B13/02
Inventor 傅平程敏
Owner MINJIANG UNIV
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