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LQR optimized brushless DC motor speed regulation neural network PID controller

A brushed DC motor, DC motor technology, applied in electric controllers, controllers with specific characteristics, motors, etc., can solve problems such as robustness to be improved, achieve ideal control effects, suppress nonlinear conditions, and improve Effects of Dynamics and Robustness

Active Publication Date: 2019-11-22
CHANGCHUN UNIV OF TECH
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  • Abstract
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Problems solved by technology

Although the LQR optimized PID controller has good tracking performance and stable performance, its robustness needs to be improved

Method used

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  • LQR optimized brushless DC motor speed regulation neural network PID controller
  • LQR optimized brushless DC motor speed regulation neural network PID controller
  • LQR optimized brushless DC motor speed regulation neural network PID controller

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Embodiment Construction

[0020] The embodiments of the present invention will be further described in detail below in conjunction with the accompanying drawings. It should be noted that the technical features and combinations of technical features described in the following embodiments should not be considered isolated, and they can be combined with each other to achieve more Nice technical effect.

[0021] Such as figure 1 As shown, the concrete structure of a kind of LQR optimization type brushless DC motor speed regulation neural network PID controller proposed by the present invention includes LQR optimization type BP neural network module, PID controller, logic switch, position and speed sensor, pulse width modulation inverter Inverter, brushless DC motor, the specific control method is as follows.

[0022] First, compare and calculate the actual output speed y(k) of the motor with the input speed r(k), and obtain the final speed error e(k)=r(k)-y(k). According to the speed error e(k), the e(k...

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Abstract

The invention relates to an LQR optimized brushless DC motor speed regulation neutral network PID controller, which is used for improving the control performance of the traditional neutral network PIDcontroller and the traditional LQR optimized PID controller. The designed LQR optimized neural network PID controller (LNPID) adjusts the KP, KI, KD gains of the controller by using a BP neural network, thereby improving the dynamic characteristic and robustness of the controller. The three-layer BP neural network adopted by the invention has strong nonlinear mapping capability, and can effectively inhibit the nonlinear condition of the controlled object. However, the traditional BP neural network is an optimization method of local search, so that the optimal output of the BP neural network is optimized by introducing an LQR control algorithm, and the output data is enabled to be closer to the target PID gain; and finally, the control output value of the controller is inputted into the brushless DC motor to achieve the rotation speed control of the motor. Meanwhile, the LNPID is adopted to continuously monitor the change of the parameters and the real-time feedback of the parameters,so that the control effect is enabled to be ideal.

Description

technical field [0001] The invention belongs to the technical field of brushless direct current motor speed regulation, and in particular relates to an LQR optimized brushless direct current motor speed regulation neural network PID controller. Background technique [0002] Due to its simple structure, high efficiency, low maintenance cost, and high dynamic response, brushless DC motors have been widely used in aerospace, robotics, and electric vehicles. As we all know, speed control is an important aspect in the field of brushless DC motor drive. With the continuous and rapid development of modern power electronics technology, sensor technology, automatic control technology and manufacturing technology, it is of great practical significance and application prospect to study brushless DC motor speed controllers with fast response speed, strong adjustment ability and high control precision. . However, the complex strongly coupled nonlinear characteristics make it difficult ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B11/42
CPCG05B11/42Y02T10/64
Inventor 胡黄水王婷婷杨兴旺韩优佳韩博
Owner CHANGCHUN UNIV OF TECH
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