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Calculation of PID controller parameters by using a fuzzy neural network

机译:用模糊神经网络计算PID控制器参数

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In this paper, we use the fuzzy neural network (FNN) to develop a formula for designing the proportional-integral-derivative (PID) controller. This PID controller satisfies the criteria of minimum integrated absolute error (IAE) and maximum of sensitivity (M_(s)). The FNN system is used to identify the relationship between plant model and controller parameters based on IAE and M_(s). To derive the tuning rule, the dominant pole assignment method is applied to simplify our optimization processes. Therefore, the FNN system is used to automatically tune the PID controller for different system parameters so that neither theoretical methods nor numerical methods need be used. Moreover, the FNN-based formula can modify the controller to meet our specification when the system model changes. A simulation result for applying to the motor position control problem is given to demonstrate the effectiveness of our approach.
机译:在本文中,我们使用模糊神经网络(FNN)来开发用于设计比例积分微分(PID)控制器的公式。该PID控制器满足最小积分绝对误差(IAE)和最大灵敏度(M_(s))的标准。 FNN系统用于基于IAE和M_(s)识别工厂模型和控制器参数之间的关系。为了得出调整规则,采用了主导极点分配方法来简化我们的优化过程。因此,FNN系统用于针对不同的系统参数自动调整PID控制器,因此无需使用理论方法或数值方法。此外,当系统模型更改时,基于FNN的公式可以修改控制器以满足我们的规范。给出了用于电机位置控制问题的仿真结果,以证明该方法的有效性。

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