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Recurrent Interval Type-2 Fuzzy Control of 2-DOF Helicopter With Finite Time Training Algorithm

机译：有限时间训练算法的2自由度直升机递归区间2型模糊控制

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This study presents the decentralized control of a 2-DOF helicopter by designing a recurrent interval type-2 fuzzy neural network (RIT2FNN). The main aim of the proposed controller is to force the pitch and yaw angles follow a desired trajectory by using a finite time adaptation law. The proposed control signal is composed of two terms: the output of the RIT2FNN and the control signal generated by a conventional proportional-derivative (PD) controller. In the beginning, since the initial conditions of the RIT2FNN are randomly selected and may not be appropriate, the PD controller is responsible for the control of the system. However, the stable adaptation laws, which benefit from sliding mode control theory, train the parameters of the RIT2FNN. Since the adaptation laws are guaranteed to converge in finite time, the parameters of the RIT2FNN converge to their appropriate values. Meanwhile, the PD controller participates less in the control process and the RIT2FNN becomes the dominant controller of the system. The proposed control method is promising when dealing with highly nonlinear real-time systems which have to operate under uncertain working environment.
机译：本研究通过设计递归区间2型模糊神经网络（RIT2FNN）提出了2自由度直升机的分散控制。所提出的控制器的主要目的是通过使用有限的时间适应律来强迫俯仰和偏航角遵循期望的轨迹。建议的控制信号由两部分组成：RIT2FNN的输出和由常规比例微分（PD）控制器生成的控制信号。首先，由于RIT2FNN的初始条件是随机选择的，因此可能不合适，因此PD控制器负责系统的控制。但是，得益于滑模控制理论的稳定自适应律训练了RIT2FNN的参数。由于保证了自适应定律在有限的时间内收敛，因此RIT2FNN的参数收敛到其适当的值。同时，PD控制器较少参与控制过程，而RIT2FNN成为系统的主导控制器。所提出的控制方法在处理必须在不确定的工作环境下运行的高度非线性实时系统时很有希望。

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