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Near-optimal neural-network robot control with adaptive gravity compensation

机译：具有自适应重力补偿的近最优神经网络机器人控制

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Adaptive nonlinear optimal control methods, as proposed in the literature, give rise to some questions around practical implementation in robotics, especially how to find a solution in a reasonable time and how to deal with gravity. This paper proposes a method to solve these problems by using a neural network with local basis-function domains, specifically the Cerebellar Model Articulation Controller (CMAC). The algorithm uses the local domains in order to keep track of the value of local cost-functionals. In this way, it can freeze the learning of the network's weights in a feedforward-like component in the CMAC when the bias has been overcome identified by using an error-based cost-functional e.g. automatic gravity compensation in a robot. After the feedforward component has been established, the algorithm then starts to learn another set of weights which contribute to feedback-like terms in the CMAC and these weights get frozen when they no longer reduce a cost-functional that includes additional control effort e.g. in a robot the control effort beyond that needed to compensate for gravity is penalized. Lyapunov methods guarantee uniformly ultimately bounded signals and ensure weight drift and bursting do not occur. One advantage is that the training time for finding a near-optimal control does not increase over previous neural-adaptive methods. Another advantage is that penalizing the control effort in a search for optimization does result in any steady-state error due to gravity. Simulations show that the proposed method significantly outperforms a standard adaptive-CMAC control using e-modification, without increasing control effort or training time. An experimental flexible-joint robot verifies that the adaptive method significantly outperforms a linear quadratic regulator. (C) 2020 Published by Elsevier B.V.
机译：如文学中提出的自适应非线性最佳控制方法引发了机器人中实际实施的一些问题，特别是如何在合理的时间内找到解决方案以及如何处理重力。本文提出了一种通过使用具有局部基函数域的神经网络来解决这些问题的方法，特别是小脑模型铰接控制器（CMAC）。该算法使用本地域以跟踪本地成本函数的值。以这种方式，当通过使用基于误差的成本函数识别时，它可以冻结在CMAC中的前馈状组件中的网络权重的学习。机器人中的自动重力补偿。在已经建立了前馈组件之后，该算法开始学习另一组权重，这些权重包括在CMAC中的反馈术语，并且当它们不再降低包括额外控制工作的成本功能时，这些权重被冻结。在一个机器人中，超越需要补偿重力的控制努力受到惩罚。 Lyapunov方法保证均匀最终有界信号，确保不会发生重量漂移和突发。一个优点是寻找近最优控制的训练时间不会增加先前的神经自适应方法。另一个优点是在寻求优化中惩罚控制工作确实导致由于重力引起的任何稳态误差。模拟表明，该方法使用电子修改显着优于标准的自适应-CMAC控制，而不会增加控制努力或培训时间。实验柔性接头机器人验证自适应方法显着优于线性二次调节器。 （c）2020由elsevier b.v发布。

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