Implementation of Sliding Surface Moving Anfis Based Sliding Mode Control to Rotary Inverted Pendulum

Muhammet Aydın, O. Yakut
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Abstract

This study covers the control of the pendulum angle by taking into account the dynamic equations and motor dynamics of the rotary inverted pendulum system, with the help of state variables in the Matlab program, by using the sliding mode control method with sliding surface moving and the adaptive neural fuzzy inference system together. The sliding mode control method with a changing sliding surface is a part of the control structure. The slope of the sliding surface was calculated using the adaptive neural fuzzy inference technique. The optimum values of the coefficients in the adaptive neural-fuzzy inference system structure have been calculated by genetic algorithm. The finding of the coefficients, the sum of the squares of the errors chosen as the objective function. The input of the adaptive neural fuzzy inference system structure consists of the error of the pendulum and the derivative of the error of the pendulum. The gradient of the sliding surface of the sliding mode control structure is the output of the adaptive neural fuzzy inference system structure. According to the findings, the pendulum angle achieved the appropriate reference value after 1.5 seconds, with an error of around zero. It obtained that the engine torque value reaches up to 50 Nm. From here, it is seen that the motor torque values used in practical applications and the motor torque values as a result of this study overlap.
基于滑动面移动anfi的旋转式倒立摆滑模控制的实现
本研究考虑旋转倒立摆系统的动力学方程和电机动力学,借助Matlab程序中的状态变量,采用滑动面移动的滑模控制方法和自适应神经模糊推理系统相结合的方法,对摆角进行控制。具有变化滑动面的滑模控制方法是控制结构的一部分。采用自适应神经模糊推理技术计算滑动面的斜率。采用遗传算法计算自适应神经模糊推理系统结构中各系数的最优值。求出系数,选择误差的平方和作为目标函数。自适应神经模糊推理系统结构的输入由摆误差和摆误差的导数组成。滑模控制结构的滑动面梯度是自适应神经模糊推理系统结构的输出。根据研究结果,摆角在1.5秒后达到合适的参考值,误差在零左右。结果表明,该发动机的扭矩值可达50 Nm。从这里可以看出,实际应用中使用的电机转矩值与本研究得出的电机转矩值是重叠的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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