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Saturated adaptive back-stepping control for robot manipulators with RBF neural network approximation

机译：基于RBF神经网络逼近的机械臂饱和自适应反步控制

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摘要

Aiming at solving the actuator saturation problem for the robot manipulators, a saturated adaptive back-stepping controller with neural network approximation is proposed. Different from the traditional back-stepping controllers, a class of saturation function and projection-type adaptation are applied to make the torque control inputs bounded. In the meantime, a Radial Basis Function (RBF) neural network based approximator is designed to replace some complex expressions in the control law, which facilitates the practical implementation of the proposed controller. In addition, explicit and strict stability analysis is given via Lyapunov's direct method, which shows that all the signals of tracking error of the system are uniformly ultimately bounded. Finally, simulation comparisons indicate that the proposed controller results in a more satisfactory tracking performance, and can suppress the initial sharp oscillation of the torque control inputs for each joint effectively.
机译：为了解决机器人操纵器的执行器饱和问题，提出了一种具有神经网络逼近的饱和自适应反步控制器。与传统的后退控制器不同，一类饱和函数和投影类型自适应被应用来使转矩控制输入有界。同时，设计了一种基于径向基函数（RBF）神经网络的逼近器来代替控制律中的一些复杂表达式，从而为所提出的控制器的实际实现提供了便利。此外，通过李雅普诺夫的直接方法给出了显式和严格的稳定性分析，表明系统的所有跟踪误差信号最终都是一致有界的。最后，仿真比较表明，所提出的控制器具有更令人满意的跟踪性能，并且可以有效地抑制每个关节的转矩控制输入的初始急剧振荡。

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