Riding and Speed Governing for Parallel Two-Wheeled Scooter Based on Sequential Online Learning Control by Humanoid Robot
Riding and Speed Governing for Parallel Two-Wheeled Scooter Based on Sequential Online Learning Control by Humanoid Robot
复制标题
基于仿人机器人顺序在线学习控制的并联两轮滑板车骑行与调速
DOI：
10.1109/iros.2018.8593685
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Inaba Masayuki
中科院分区：
文献类型：
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作者：
Kimura Kohei;Nozawa Shunichi;Mizohana Hiroto;Okada Kei;Inaba Masayuki
为了保证仿人机器人进入并联两轮滑板车后的连续动作和骑行后的速度调节，需要对控制器增益进行连续在线整定。在行驶和调速过程中，所采用的控制器是不同的，并且这些调整策略也是不同的。特别是，在短的骑行阶段，骑行需要即时调谐和速度调节需要精确调谐，以调节仿人机器人的速度。针对上述要求，本文提出了基于SGD的开环学习控制（SLC）和基于小批量的闭环学习控制（MLC）级联的顺序在线学习控制（SOLC）方法。SLC在骑行过程中对脚力矩控制进行阻尼增益在线整定，MLC在速度调节控制中对PID增益在线整定。最后，通过仿人机器人HRP 2-JSK在并联两轮滑板车上的连续骑行和调速实验，验证了SOLC的有效性。
The sequential online tuning for controller gains is required for the continuous action of the riding into parallel two-wheeled scooter and the speed governing after riding by humanoid robot. The implemented controllers are different between the riding and the speed governing, and these tuning strategies are also different. In particular, the riding requires the immediate tuning in the short riding phase and the speed governing requires the accurate tuning to regulate the speed of humanoid robot. To the above requirements, this paper proposes the Sequential Online Learning Control (SOLC)method composed of the cascade connection of SGD-based open-loop Learning Control (SLC)and Mini-batch-based closed-loop Learning Control (MLC). SLC contributes the damping gain online tuning for the foot torque control during execution of riding, and MLC contributes the PID gains online tuning for the speed governing control. Finally, we show the validity of SOLC through the sequential experiment of riding and speed governing for parallel two-wheeled scooter by life-sized humanoid robot HRP2-JSK.