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Method of Maximum Permitted Learning Rate Calculation for Neural Controller of Balancing Robot

机译：平衡机器人神经控制器的最大允许学习率计算方法

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This research is to solve a problem of sustainability of a balancing robot controlled by an artificial neural network. The mentioned network acts as a regulator and calculates at its output layer a control action for the plant. Online training of such a network is necessary to improve the quality of the robot control since it changes its parameters or a mode of functioning in the course of operation. Implementing such training, the question of the learning rate limitation arises sharply. It is directly related to the assessment of sustainability of the control system under consideration. That is why a method based on the second Lyapunov approach is proposed to calculate the upper allowable limit of the online learning rate for the neural network controller under various conditions at each moment of its functioning. This method does not require the plant mathematical model. The efficiency of the approach is proved by experiments with a real balancing robot based on the EV3 platform.
机译：这项研究是为了解决由人工神经网络控制的平衡机器人的可持续性问题。提到的网络充当调节器，并在其输出层计算对工厂的控制操作。这种网络的在线培训对于提高机器人控制的质量是必要的，因为它会在操作过程中更改其参数或功能模式。实施这样的培训，学习率限制的问题就急剧地出现了。它与所考虑的控制系统的可持续性评估直接相关。因此，提出了一种基于第二种Lyapunov方法的方法，以计算神经网络控制器在每个功能运行的各个时刻在各种条件下的在线学习率的允许上限。此方法不需要工厂数学模型。通过使用基于EV3平台的真实平衡机器人进行的实验证明了该方法的有效性。

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