Purpose

Omnidirectional mobile platforms are still plagued by the problem of heading deviation. In four-Mecanum-wheel systems, this problem arises from the phenomena of dynamic imbalance and slip of the Mecanum wheels while driving. The purpose of this paper is to analyze the mechanism of omnidirectional motion using Mecanum wheels, with the aim of enhancing the heading precision. A proportional-integral-derivative (PID) setting control algorithm based on a radial basis function (RBF) neural network model is introduced.

Design/methodology/approach

In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1.

Findings

The network RBF NN1 calculates the deviations ?Kp, ?Ki and ?Kd to regulate the three coefficients Kp, Ki and Kd of the heading angle PID controller. This corrects the driving heading in real time, resolving the problems of low heading precision and unstable driving. The experimental data indicate that, for a externally imposed deviation in the heading angle of between 34º and ∼38°, the correction time for an omnidirectional mobile platform applying the algorithm during longitudinal driving is reduced by 1.4 s compared with the traditional PID control algorithm, while the overshoot angle is reduced by 7.4°; for lateral driving, the correction time is reduced by 1.4 s and the overshoot angle is reduced by 4.2°.

Originality/value

In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. The method is innovative.

中文翻译：

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基于RBF神经网络控制器的全方位移动平台PID控制

目的

全向移动平台仍然受到航向偏差问题的困扰。在四麦克纳姆轮系统中，这个问题是由麦克纳姆轮在行驶时的动态不平衡和打滑现象引起的。本文旨在分析利用麦克纳姆轮进行全向运动的机理，以提高航向精度。介绍了一种基于径向基函数(RBF)神经网络模型的比例积分微分(PID)整定控制算法。

设计/方法/方法

本研究分析了麦克纳姆轮的全向运动机理，以提高航向精度。介绍了一种基于RBF神经网络模型的PID整定控制算法。该算法基于全向移动平台的运动学模型，实时修正行驶航向。在该算法中，神经网络RBF NN2用于识别系统状态，计算系统的雅可比信息，并将信息传递给神经网络RBF NN1。

发现

网络RBF NN1计算偏差ΔKp、ΔKi和ΔKd以调节航向角PID控制器的三个系数Kp、Ki和Kd。实时修正行车航向，解决航向精度低、行车不稳定的问题。实验数据表明，对于外部施加的航向角偏差在 34º 和 ~38° 之间，与传统的 PID 控制算法相比，应用该算法的全向移动平台在纵向行驶时的校正时间减少了 1.4 s，而超调角减少了7.4°；对于横向驱动，校正时间减少1.4 s，超调角减少4.2°。

原创性/价值

本研究分析了麦克纳姆轮的全向运动机理，以提高航向精度。介绍了一种基于RBF神经网络模型的PID整定控制算法。该算法基于全向移动平台的运动学模型，实时修正行驶航向。在该算法中，神经网络RBF NN2用于识别系统状态，计算系统的雅可比信息，并将信息传递给神经网络RBF NN1。方法是创新的。