WANG Wei1, ZHANG Zhi-liang1,2, GAO Ben-wen2, YI Ming2
（1. School of Mechanical Engineering, Southwest Petroleum University, Chengdu 610500, China；
2. Drilling Engineering Technology Institute of CNPC Xibu Drilling Engineering Company Limited, Urumqi 830011, China）
Abstract： This paper presents a two-wheeled differential spherical mobile robot in view of the problems that the motion of spherical robot is difficult to control and the sensor is limited by the spherical shell. The robot is simple in structure, flexible in motion and easy to control. The kinematics and dynamics model of spherical mobile robot is established according to the structure of spherical mobile robot. On the basis of the adaptive neural sliding mode control, the trajectory tracking controller of the system is designed. During the simulation of the s-trajectory and circular trajectory tracking control of the spherical mobile robot, it is concluded that the spherical mobile robot is flexible in motion and easy to control. In addition, the simulation results show that the adaptive neural sliding mode control can effectively track the trajectory of the spherical robot. The adaptive control eliminates the influence of unknown parameters and disturbances, and avoids the jitter of left and right wheels during the torque output.
Key words： mobile robot; adaptive neural sliding mode control; dynamics controller; trajectory tracking
CLD number： TP242doi： 10.3969/j.issn.1674-8042.2020.03.011
References
［1］Jelassi A, Chaker A, Mlika A. 3-RRR spherical parallel robot optimization with minimum of singularities. In: Proceddings of 7th International Workshop on Computational Kinematics, Futuroscope, France, 2017: 299-306.
［2］Liu Y T, Guo J, Sun S, et al. Design of a new type of bionic spherical amphibious robot system. Journal of Intelligent Systems, 2019, 14(3): 582-588.
［3］Huang Y, Zhu G, Wang C, et al. Structure design and dynamical modelling of a spherical robot driven by omnidirectional wheels. In: Proceedings of MATEC Web of Conferences, 2018.
［4］Fan Q M, Lv S M. Adaptive neural network sliding mode control of mobile robots. Control Engineering, 2017, 24(7): 1409-1414.
［5］Muralidharan V, Mahindrakar A D. Geometric controllability and stabilization of spherical robot dynamics. IEEE Transactions on Automatic Control, 2015, 60(10): 2762-2767.
［6］Takateru U, Masanori O, Hisashi T, et al. Development of a spherical rolling robot equipped with a gyro. In: Proceedings of 2012 IEEE International Conference on Mechatronics and Automation, Chengdu, China, 2012: 1602-1607.
［7］Jaimez M, Castillo J J, García F, et al. Design and modelling of omnibola, a spherical mobile robot. Mechanics Based Design of Structures and Machines, 2012, 40(4): 383-399.
［8］Chen W H, Chen C P, Tsai J S, et al. Design and implementation of a ball-driven omnidirectional spherical robot. Mechanism & Machine Theory, 2013, 68: 35-48.
［9］Karavaev Y L, Kilin A A. The dynamics and control of a spherical robot with an internal omniwheel platform. Regular & Chaotic Dynamics, 2015, 20(2): 134-152.
［10］Wu K H, Li W, Liu C A, et al. Dynamic control of two-wheeled mobile robot. Journal of Astronautics, 2006, 27(2): 272-275.
［11］Song L Y, Xing F. Adaptive neural sliding mode trajectory tracking control for mobile robot. Control Engineering, 2018, 25(11): 1965-1970.
［12］Zhao C D. Simulation of vertical and horizontal target path planning for autonomous wheeled mobile robot. Computer Simulation, 2019, 36(6): 306-309.
［13］Guo B H, Hu Y M. Motion planning for nonholonomic robot with dynamic modeling. Control Theory and Applications, 2004, 21(3): 443-446.
［14］Xing F. Uncertain trajectory tracking control of wheeled mobile robots. Liaoning: Liaoning University of Technology, 2017.
［15］Li Z C, Wang Y, Song X G, et al. Neural adaptive tracking control for wheeled mobile robots. In: Proceedings of 2015 International Conference on Fluid Power and Mechatronics, Harbin, 2015: 610-617
［16］Liu Z L, Wang S, Zhang W W, et al. Neural network-based path following control of mobile robots. Control Engineering, 2017, 24(10): 2045-2052.
［17］Shi X P, Liu S R, Liu F, et al. Adaptive neural network sliding mode trajectory tracking control for nonholonomic mobile robots. Journal of East China University of Science and Technology(Natural Science Edition), 2010, 36(5): 695-701.
［18］Wang Z Y, Li Y D, Zhu L. Dual adaptive neural sliding mode control for nonholonomic mobile robots. Journal of Mechanical Engineering, 2010, 46(23): 16-22.
两轮差动球形移动机器人轨迹跟踪控制
王威1， 张智亮1,2， 高本文2， 伊明2
（1. 西南石油大学 机电工程学院， 四川 成都 610500；2. 中国石油西部钻探工程有限公司钻井工程技术研究院， 新疆 乌鲁木齐 830011）
摘要:针对现有球形机器人运动难以控制以及球壳限制传感器应用的问题， 提出了一种双轮差动式球形移动机器人， 其结构简单、运动灵活且易于控制。 首先， 根据球形移动机器人结构， 建立了运动学与动力学模型。 然后， 基于自适应神经滑膜控制方法， 设计了该系统的轨迹跟踪控制器。 最后， 通过对球形机器人进行S轨迹和圆周轨迹的跟踪控制仿真， 得出了球形移动机器人具备灵活地运动特性且易于控制的结论。 仿真结果验证了自适应神经滑模控制能够有效地对球形机器人进行轨迹跟踪， 自适应控制消除了系统未知参数与扰动的影响， 同时也避免了左右轮力矩输出的抖振问题。
关键词:移动机器人； 自适应神经滑模控制； 动力学控制器； 轨迹跟踪
引用格式：WANG Wei, ZHANG Zhi-liang, GAO Ben-wen, et al. Control of trajectory tracking of two-wheeled differential spherical mobile robot. Journal of Measurement Science and Instrumentation, 2020, 11（3）： 276-284. ［doi： 10.3969／j.issn.1674-8042.2020.03.011］
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