Multi-layer neural network motor system control method based on robust integral

A multi-layer neural network and control method technology, applied in the field of motor servo control, can solve problems such as affecting the tracking performance of the system, and achieve the effect of solving gain feedback and improving tracking performance

Active Publication Date: 2019-02-12
NANJING UNIV OF SCI & TECH
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Problems solved by technology

However, problems with high-frequency dynamics and measurement noise

Method used

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  • Multi-layer neural network motor system control method based on robust integral
  • Multi-layer neural network motor system control method based on robust integral
  • Multi-layer neural network motor system control method based on robust integral

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0104] Motor position servo system parameters are inertial load parameters: m = 0.02kg; viscous friction coefficient B = 10N m s / °; torque amplification factor k i =6N / V; time-varying external interference

[0105] The position command that the system expects to track is as follows Figure 4 For the sinusoidal command shown, the curves of command speed and acceleration changing with time are also given together.

[0106] Comparison of simulation results: Robust integral-based multilayer neural network controller (NNRISE) parameter selection: k 1 =300;k 2 =100; β=60; PID controller parameter selection: k P =1699;k I = 13097; k D =0.

[0107] The selection steps of the PID controller parameters are as follows: First, under the condition of ignoring the nonlinear dynamics of the motor servo system, a set of controller parameters are obtained through the PID parameter self-tuning function in Matlab, and then after adding the nonlinear dynamics of the system Fine-tuning th...

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Abstract

The invention provides a multi-layer neural network motor system control method based on robust integral. The multi-layer neural network motor system control method comprises the steps that step 1, amathematical model of a motor system is established; step 2, a multi-layer neural network controller for robust integral is designed; and step 3, the Lyapunov stability theory is used for proving thestability, and the mean value theorem is used for obtaining the result of the semi-global asymptotic stability of the system.

Description

technical field [0001] The invention relates to a motor servo control technology, in particular to a multilayer neural network motor system control method based on robust integrals. Background technique [0002] The motor servo system has outstanding advantages such as fast response, convenient maintenance, high transmission efficiency, and convenient energy acquisition. It is widely used in various important fields, such as robots, machine tools, and electric vehicles. With the rapid development of the modern control engineering field, the requirements for the tracking performance of the motor servo system are getting higher and higher, but how to design the controller to ensure the high performance of the motor servo system is still a difficult problem. This is because the motor servo system is a typical nonlinear system, and there will be many modeling uncertainties (such as unmodeled disturbances, nonlinear friction, etc.) in the process of designing the controller. Thes...

Claims

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Application Information

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IPC IPC(8): G05B13/02G05B13/04G06N3/04
CPCG05B13/027G05B13/042G06N3/045
Inventor 姚志凯姚建勇
Owner NANJING UNIV OF SCI & TECH
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