Rotary-table servo system neural network control method

A technology of neural network control and servo system, which is applied in the field of adaptive robust finite-time neural network control of turntable servo system, which can solve the problems of low control accuracy, difficulty in obtaining the inverse model of the dead zone accurately, complex calculations, etc.

Active Publication Date: 2013-07-10
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0004] The present invention overcomes the problems in the existing turntable servo system control method that the dead zone inverse model is difficult to obtain accurately, the control speed is slow, and the control precision is not high, and provides a method that avoids the complicated calculation of the dead zone inverse model and improves the control speed and control precision. Adaptive Robust Finite-Time Neural Control (ARFTNC) for turntable servo system

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  • Rotary-table servo system neural network control method
  • Rotary-table servo system neural network control method
  • Rotary-table servo system neural network control method

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Embodiment Construction

[0074] Refer to attached Figure 1-4 , the design of an adaptive robust finite-time neural network control method for a turntable servo system with an unknown dead zone, including the following steps:

[0075] 1. Establish the mechanical dynamic model of the permanent magnet synchronous motor turntable servo system:

[0076] m x · · + f * ( x ‾ , t ) + d * ( x ‾ , t ) = k 0 * u ( t ) (1)

[0077] y=x(t)

[0078] in, y(t)∈R represent the system state, control input voltage and motor output, res...

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Abstract

A rotary-table servo system neural network control method comprises (1) building a mechanical dynamic model of a permanent magnet synchronous motor rotary-table servo system, and initializing the system state, sampling time and relative control parameters; (2) according to the differential mean value theorem, enabling a non-linear input dead zone in the system to linearly approximate to a simple time-varying system, avoiding complex calculation of dead-zone inverse compensation, and finally inferring a rotary-table servo system model provided with an unknown dead zone; (3) at each sampling moment, calculating and controlling a system tracking error, a fast terminal sliding mode surface and a first-order derivative of the system; (4) based on the rotary-table servo system model provided with the unknown dead zone, selecting a neural network approaching unknown trend, designing an adaptive robust finite-time neural network controller according to the system tracking error, the fast terminal sliding mode surface and the first-order derivative of the system, and updating a neural network weight matrix; and (5) entering the next sampling moment, and repetitively executing the steps from (3) to (5).

Description

technical field [0001] The invention relates to a control method of a turntable servo system, in particular to an adaptive robust finite time neural network control method of a turntable servo system with an unknown nonlinear input dead zone. Background technique [0002] Dead zone non-linear link widely exists in hydraulic servo system, servo motor system and other industrial engineering fields. The existence of the dead zone often leads to the reduction of the efficiency of the control system or even failure. Therefore, in order to improve the control performance, compensation and control methods for the dead zone are essential. The traditional dead zone compensation method is generally to establish the inverse model or approximate inverse model of the dead zone, and design an adaptive controller by estimating the upper and lower bound parameters of the dead zone to compensate the influence of the dead zone. However, in nonlinear systems such as turntable servo systems, ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
Inventor 陈强王晶金燕
Owner ZHEJIANG UNIV OF TECH
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