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Neural control of robot manipulators considering motor voltage saturation: performance evaluation and experimental validation
COMPEL ( IF 0.7 ) Pub Date : 2020-11-30 , DOI: 10.1108/compel-03-2020-0127
Alireza Izadbakhsh , Saeed Khorashadizadeh

Purpose

This paper aims to design a neural controller based on radial basis function networks (RBFN) for electrically driven robots subjected to constrained inputs.

Design/methodology/approach

It is assumed that the electrical motors have limitations on the applied voltages from the controller. Due to the universal approximation property of RBFN, uncertainties including un-modeled dynamics and external disturbances are represented with this powerful neural network. Then, the lumped uncertainty including the nonlinearities imposed by actuator saturation is introduced and a mathematical model suitable for model-free control is presented. Based on the closed-loop equation, a Lyapunove function is defined and the stability analysis is performed. It is assumed that the electrical motors have limitations on the applied voltages from the controller.

Findings

A comparison with a similar controller shows the superiority of the proposed controller in reducing the tracking error. Experimental results on a SCARA manipulator actuated by permanent magnet DC motors have been presented to guarantee its successful practical implementation.

Originality/value

The novelty of this paper in comparison with previous related works is improving the stability analysis by involving the actuator saturation in the design procedure. It is assumed that the electrical motors have limitations on the applied voltages from the controller. Thus, a comprehensive approach is adopted to include the saturated and unsaturated areas, while in previous related works these areas are considered separately. Moreover, a performance evaluation has been carried out to verify satisfactory performance of transient response of the controller.



中文翻译:

考虑电机电压饱和的机器人机械手的神经控制:性能评估和实验验证

目的

本文旨在设计一种基于径向基函数网络(RBFN)的神经控制器,用于受约束输入的电动机器人。

设计/方法/方法

假定电动机对来自控制器的施加电压有限制。由于RBFN的通用逼近特性,使用此强大的神经网络可以表示不确定性,包括未建模的动力学和外部干扰。然后,介绍了包括执行器饱和所引起的非线性在内的总不确定性,并提出了适用于无模型控制的数学模型。基于闭环方程,定义Lyapunove函数并进行稳定性分析。假定电动机对来自控制器的施加电压有限制。

发现

与类似控制器的比较显示了所提出的控制器在减少跟踪误差方面的优势。提出了由永磁直流电动机驱动的SCARA机械手的实验结果,以确保其成功地实际实施。

创意/价值

与以前的相关工作相比,本文的新颖之处在于通过在设计过程中包含执行器饱和来改进稳定性分析。假定电动机对来自控制器的施加电压有限制。因此,采用了一种综合方法来包括饱和区和非饱和区,而在先前的相关工作中,这些区域是分开考虑的。而且,已经进行了性能评估以验证控制器的瞬态响应的令人满意的性能。

更新日期:2020-11-30
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