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Servo system inertia identification method adopting genetic algorithm for optimization

A genetic algorithm and servo system technology, applied in control systems, electronic commutation motor control, electrical components, etc., can solve problems such as the contradiction between convergence speed and identification accuracy, and the identification results have a greater impact

Active Publication Date: 2016-08-31
无锡超通智能制造技术研究院有限公司
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Problems solved by technology

However, when using the model reference adaptive algorithm to identify the moment of inertia, the adaptive gain in the algorithm has a great influence on the identification results, and there is a contradiction between the convergence speed and the identification accuracy.

Method used

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  • Servo system inertia identification method adopting genetic algorithm for optimization
  • Servo system inertia identification method adopting genetic algorithm for optimization
  • Servo system inertia identification method adopting genetic algorithm for optimization

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specific Embodiment approach

[0052] Such as figure 1 As shown, a servo system inertia identification method optimized by a genetic algorithm of the present invention is based on the model reference adaptive theory, and the adaptive law of inertia identification is designed according to the Landau discrete-time recursive algorithm. Using the global search ability of genetic algorithm, the model reference adaptive system is used as the control object, the output deviation between the actual angular velocity and the estimated angular velocity of the motor is used as the control error, and the time multiplied by the absolute value of the error is used as the optimization target to dynamically adjust the inertia identification The adaptive gain β in , realizes the online optimization of the control parameters. The specific implementation is as follows:

[0053] Based on the model reference adaptive theory, an inertia identification system is established. The equation containing parameters to be estimated is u...

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Abstract

The invention discloses a servo system inertia identification method adopting a genetic algorithm for optimization. Based on a model reference self-adaption theory, the self-adaption rule of inertia identification is designed according to a Landau discrete time recursive algorithm. The global searching capability of the genetic algorithm is utilized, a model reference self-adaption system is used as a control object, an output difference between a motor practical angular speed and an estimated angular speed is used as a control error, the integration of the product of time and an error absolute value is used as an optimization target, the self-adaption gain [beta] in the inertia identification is dynamically adjusted, and the online optimization of control parameters is realized. According to the invention, both the higher convergence speed and the higher identification precision are realized in inertia identification, and the relatively high self-adaption capability to the change of rotary inertia is achieved.

Description

technical field [0001] The invention relates to a servo system inertia identification method, in particular to a servo system inertia identification method optimized by genetic algorithm. Background technique [0002] Permanent magnet synchronous motors have been widely used in high-speed and high-precision control systems such as CNC machine tools, aerospace, and industrial robots due to their small size, high efficiency, large electromagnetic torque, easy maintenance, and convenient control. High-performance applications have strict requirements on the control performance of the servo system. During the actual operation of the motor, the change of the moment of inertia of the load will have a bad influence on the dynamic and static characteristics of the servo system. In order to improve the control performance of the servo system, it is necessary to identify the moment of inertia to obtain an accurate value of the moment of inertia. Therefore, the accurate identificatio...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H02P6/00
CPCH02P6/00H02P2207/05
Inventor 梅雪松宋哲许睦旬林英行齐太安孙书川
Owner 无锡超通智能制造技术研究院有限公司
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