Abstract In this paper, adaptive general type-2 fuzzy neural network control for motion balance adjusting of a power-line inspection robot is developed. It is used to enhance the anti-interference ability of the controlled plant. General type-2 fuzzy system is adopted because of its ability to more effectively handle uncertainties which may exist as external disturbances and parameter perturbations. The structure of general type-2 fuzzy system is designed by mimicking the neural network. The adaptive laws can be obtained based on the sliding mode control theory. This provides a kind of dynamic general type-2 fuzzy system whose membership functions and consequent parts are changing adaptively. The proposed controller is used to control an under-actuated non-linear power-line inspection (PLI) robot. Different simulation conditions are considered to test the anti-interference ability of the proposed controller. Simulation results indicate that the proposed method can strengthen the PLI robot’s anti-interference ability in a better way as compared to its interval type-2 fuzzy counterpart and PD controller.

中文翻译：

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基于滑模控制理论的电力线检测机器人自适应通用二类模糊神经网络控制

摘要 本文提出了一种用于电力巡检机器人运动平衡调整的自适应通用2类模糊神经网络控制方法。用于增强被控设备的抗干扰能力。采用通用 2 类模糊系统是因为它能够更有效地处理可能存在的外部干扰和参数扰动的不确定性。模拟神经网络设计了通用2型模糊系统的结构。基于滑模控制理论可以得到自适应规律。这提供了一种动态的通用 2 类模糊系统，其隶属函数和后续部分自适应地变化。所提出的控制器用于控制欠驱动非线性电力线检查（PLI）机器人。考虑不同的仿真条件来测试所提出的控制器的抗干扰能力。仿真结果表明，与间隔类型2模糊对应物和PD控制器相比，所提出的方法可以更好地增强PLI机器人的抗干扰能力。