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首页> 外文会议>International Conference on Fluid Power Transmission and Control(ICFP' 2005); 20050405-08; Hangzhou(CN) >Research on the Adaptive Fuzzy-Neural Network Control of the Large Power Controlled Start Transmission System
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Research on the Adaptive Fuzzy-Neural Network Control of the Large Power Controlled Start Transmission System

机译：大功率启动控制系统的自适应模糊神经网络控制研究

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摘要

The soft start/stop control devices are vital to the large power machines such as a large conveyer belt system, which can reduce the peak load in the system and the strain in the belt. There exist several techniques such as variable frequency control of the AC motor and the controlled start transmission system for this purpose. The large power controlled start transmission systems are widely used for the drive of the large power machines such as large conveyer belt systems, by which the process of the start and stop of the conveyer belt can be controlled smoothly following a set curve. The system includes a planetary gearbox in which there are frictional disks to control the rotating speed of the ring gear so that the output speed of the gearbox can be controlled, a cooling system to take away the heat from the gearbox , and a control unit which is composed of a micro-controller, an output rotating speed sensor and an electro-hydraulic servo valve to control the pressure on the frictional disks. The traditional control techniques such as PID algorithms do not work properly for the control of the large power controlled start transmission systems because of the nonlinear properties of the load variation. To achieve the effective soft starting/braking control of the system following the specified curves, the dynamic model of the system is built up and the simulation is realized with the MATLAB/SIMULINK software. To adapt the nonlinear load variation, an adaptive fuzzy-neural network algorithm is designed for the control of the output rotating speed of the gearbox by adjusting hydraulic pressure on the frictional disks via the electro-hydraulic servo valve in this paper. The simulation results showed that the control algorithm can modify the fuzzy control parameters to adapt the load variation by training the BP neural network. The experimental study on a test rig validates the simulation results.
机译：软启动/停止控制设备对于大型动力机械（例如大型传送带系统）至关重要，它可以减少系统中的峰值负荷和皮带中的应变。为此，存在几种技术，例如交流电动机的变频控制和受控起动传动系统。大功率控制的起动传动系统被广泛地用于诸如大输送带系统之类的大功率机器的驱动，由此可以按照设定曲线平稳地控制输送带的起动和停止的过程。该系统包括一个行星齿轮箱，在该行星齿轮箱中有摩擦片来控制环形齿轮的转速，以便可以控制齿轮箱的输出速度；一个冷却系统，用于从齿轮箱中带走热量，以及一个控制单元，该齿轮箱它由一个微控制器，一个输出转速传感器和一个电动液压伺服阀组成，以控制摩擦盘上的压力。由于负载变化的非线性特性，传统的控制技术（例如PID算法）不适用于控制大功率受控起动变速器系统。为了按照指定的曲线实现对系统的有效软启动/制动控制，建立了系统的动态模型，并使用MATLAB / SIMULINK软件进行了仿真。为了适应非线性负载的变化，本文设计了一种自适应模糊神经网络算法，通过电动液压伺服阀调节摩擦片上的液压，从而控制变速箱的输出转速。仿真结果表明，该控制算法可以通过训练BP神经网络来修改模糊控制参数，以适应负荷变化。在试验台上进行的实验研究验证了仿真结果。

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