Tracking Control of Tangential Velocity of Eel Robot Based on Iterative Learning Control
ZHANG Anfan1,2, MA Shugen1,3, LI Bin1, WANG Minghui1, CHANG Jian1
1. State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. Department of Robotics, Ritsumeikan University, Shiga-ken 525-8577, Japan
张安翻, 马书根, 李斌, 王明辉, 常健. 基于迭代学习控制的鳗鱼机器人切向速度跟踪控制[J]. 机器人, 2018, 40(6): 769-778.DOI: 10.13973/j.cnki.robot.170590.
ZHANG Anfan, MA Shugen, LI Bin, WANG Minghui, CHANG Jian. Tracking Control of Tangential Velocity of Eel Robot Based on Iterative Learning Control. ROBOT, 2018, 40(6): 769-778. DOI: 10.13973/j.cnki.robot.170590.
Abstract:Due to the strong nonlinear and high under-actuated of the dynamic model of the eel robot, the tracking control of tangential velocity of the multi-joint eel robot is very challenging. So, the P-type iterative learning control and the gait generator are combined to achieve the tracking control of tangential velocity of the multi-joint eel robot. Firstly, the dynamic model of the eel robot in non-inertial frame is established by using analytical Newton-Euler method, which can obtain the tangential velocity sub-dynamics model directly. Then, the convergence condition of tangential velocity tracking errors is obtained by adopting the P-type iterative learning controller with the saturated function to control the gait parameters, and using the composite energy function and the tangential velocity sub-dynamics model to analyze the convergence of the controller. Finally, the motion control framework of the eel robot is introduced and the multi-module eel robot is simulated and tested. The experiment results demonstrate that the actual tangential velocity can follow the desired tangential velocity as the iteration number increases, which verifies the effectiveness of the tracking controller of tangential velocity of the eel robot.
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