Fast neural network control of a pseudo-driven wheel on deformable terrain

Bo You; Huanan Qi; Liang Ding; Shu Li; Lan Huang; Linrui Tian; Haibo Gao

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Fast neural network control of a pseudo-driven wheel on deformable terrain

机译：可变形地形上伪驱动轮的快速神经网络控制

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The performance of the wheel-driving control scheme in wheeled mobile robots is a vital aspect in vehicular robotics applications, both for fully exerting the robot tractive capabilities and to save energy. This is especially true in contexts in which the robot must travel through unknown and unpredictable deformable terrains, such as the planetary exploration context considered in this study. To compensate the disturbances resulting from terrain deformations while simultaneously leveraging the control advantages provided by the use of the recently proposed concept of pseudo-driven wheels (PDWs), an artificial-neural-network-based control method is proposed here. This study develops and presents the network algorithms necessary for achieving active following control on velocity tracking for PDWs. To handle the considered complex and highly uncertain wheel-terrain interactions, an online sequential forgetting update method for the neural network is presented, and an improved online sequential extreme learning machine (OS-ELM), combined with a proportional integral derivative (PID) controller is used, thereby leading to an efficient and highly performant hybrid OS-ELM-PID control system. A simulation showed the feasibility of the proposed control method, and subsequent real-life experimental results demonstrate the capability of the control system in maintaining the drawbar force on the PDW within the range F_(DP) = ±2 N.

机译：轮式移动机器人的车轮驱动控制方案的性能是车辆机器人应用中的重要方面，用于完全施加机器人牵引力并节省能量。在机器人必须通过未知和不可预测的可变形地形的背景下，这尤其如此，例如本研究中考虑的行星勘探背景。为了补偿地形变形导致的干扰，同时利用使用最近提出的伪驱动的车轮（PDWS）的概念提供的控制优势，这里提出了一种人工神经网络的控制方法。该研究开发并提出了在PDW的速度跟踪上实现了活动的网络算法所必需的网络算法。为了处理所考虑的复杂和高度不确定的轮式相互作用，提出了用于神经网络的在线顺序遗忘更新方法，以及改进的在线顺序极端学习机（OS-ELM），与比例积分衍生物（PID）控制器组合使用，从而导致高效且高度性能的混合动力OS-ELM-PID控制系统。模拟显示所提出的控制方法的可行性，随后的实际实验结果证明了控制系统在维持范围内PDW上的牵引力力的能力（DP）=±2 N.

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来源
《Mechanical systems and signal processing》 |2021年第5期|107478.1-107478.16|共16页
作者
Bo You; Huanan Qi; Liang Ding; Shu Li; Lan Huang; Linrui Tian; Haibo Gao;
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作者单位

School of Automation Harbin University of Science and Technology No. 52 Xuefu Avenue Nangang District Harbin China;

School of Automation Harbin University of Science and Technology No. 52 Xuefu Avenue Nangang District Harbin China State Key Laboratory of Robotics and Systems Harbin Institute of Technology No. 92 Xidazhi Avenue Nangang District Harbin China;

State Key Laboratory of Robotics and Systems Harbin Institute of Technology No. 92 Xidazhi Avenue Nangang District Harbin China;

State Key Laboratory of Robotics and Systems Harbin Institute of Technology No. 92 Xidazhi Avenue Nangang District Harbin China;

State Key Laboratory of Robotics and Systems Harbin Institute of Technology No. 92 Xidazhi Avenue Nangang District Harbin China;

State Key Laboratory of Robotics and Systems Harbin Institute of Technology No. 92 Xidazhi Avenue Nangang District Harbin China;

State Key Laboratory of Robotics and Systems Harbin Institute of Technology No. 92 Xidazhi Avenue Nangang District Harbin China;

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正文语种 eng
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Wheeled mobile robot; Pseudo-driven wheel; Neural network control; Online sequential extreme learning machine with PID controller (OS-ELM-PID);

机译：轮式移动机器人;伪驾驶轮;神经网络控制;具有PID控制器的在线序贯极限学习机（OS-ELM-PID）;

Fast neural network control of a pseudo-driven wheel on deformable terrain

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