Abstract In this work, the effectiveness of the adaptive neural based fuzzy inference system (ANFIS) in identifying underactuated systems is illustrated. Two case studies of underactuated systems are used to validate the system identification i. e., linear inverted pendulum (LIP) and rotary inverted pendulum (RIP). Both the systems are treated as benchmark systems in modeling and control theory for their inherit nonlinear, unstable, and underactuated behavior. The systems are modeled with ANFIS using the input-output data acquired from the dynamic response of the nonlinear analytical model of the systems. The dynamic response of the ANFIS model is simulated and compared to the nonlinear mathematical model of the inverted pendulum systems. In order to check the effectiveness of the ANFIS model, mean square error is used as the performance index. From the obtained simulation results, it has been perceived that the ANFIS model performed satisfactorily within the trained operating range while a minor deviation is seen outside the trained operating region for both the case studies. Furthermore, the experimental validation of the of the proposed ANFIS model is done by comparing it with the experimental model of the rotary inverted pendulum. The obtained results show that the response of ANFIS model is in close agreement to the experimental model of the rotary inverted pendulum.

中文翻译：

---

基于 ANFIS 的欠驱动系统系统识别

摘要 在这项工作中，说明了自适应神经模糊推理系统（ANFIS）在识别欠驱动系统方面的有效性。欠驱动系统的两个案例研究用于验证系统识别，即线性倒立摆 (LIP) 和旋转倒立摆 (RIP)。这两个系统都被视为建模和控制理论中的基准系统，因为它们继承了非线性、不稳定和欠驱动行为。系统使用 ANFIS 建模，使用从系统非线性分析模型的动态响应中获取的输入-输出数据。模拟了 ANFIS 模型的动态响应，并与倒立摆系统的非线性数学模型进行了比较。为了检验ANFIS模型的有效性，使用均方误差作为性能指标。从获得的模拟结果可以看出，ANFIS 模型在训练操作范围内的表现令人满意，而在两个案例研究中，训练操作区域外都出现了微小的偏差。此外，所提出的 ANFIS 模型的实验验证是通过将其与旋转倒立摆的实验模型进行比较来完成的。得到的结果表明，ANFIS 模型的响应与旋转倒立摆的实验模型非常吻合。所提出的 ANFIS 模型的实验验证是通过将其与旋转倒立摆的实验模型进行比较来完成的。得到的结果表明，ANFIS 模型的响应与旋转倒立摆的实验模型非常吻合。所提出的 ANFIS 模型的实验验证是通过将其与旋转倒立摆的实验模型进行比较来完成的。得到的结果表明，ANFIS 模型的响应与旋转倒立摆的实验模型非常吻合。