移动机器人预设性编队控制算法研究

李海婷; 张鹏超; 呙生富; 刘亚恒; 徐鹏飞

doi:10.13433/j.cnki.1003-8728.20200367

移动机器人预设性编队控制算法研究

doi: 10.13433/j.cnki.1003-8728.20200367

1.
陕西理工大学电气工程学院，陕西汉中　723001
2.
陕西理工大学陕西省工业自动化重点实验室，陕西汉中　723001

基金项目: 陕西省教育厅重点科学研究计划项目（20JS022）

详细信息

作者简介:
李海婷（1994−），硕士研究生，研究方向为多移动机器人编队控制，1032014861@qq.com

通讯作者:
张鹏超，教授，硕士生导师，lht644877@163.com

中图分类号: TP242
计量
- 文章访问数: 86
- HTML全文浏览量: 23
- PDF下载量: 7
- 被引次数: 0
出版历程
- 收稿日期: 2020-08-20
- 刊出日期: 2022-05-11

Research on Prescribed Performance Formation Control Algorithm of Mobile Robots

1.
School of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, Shaanxi, China
2.
Shaanxi Key Laboratory of industrial automation, Shaanxi University of Technology, Hanzhong 723001, Shaanxi, China

摘要

摘要: 针对多移动机器人控制系统在通讯范围约束、外界干扰存在时表现出的自适应差、控制精度低的问题，提出一种基于双曲正切型约束函数的预设性能模糊滑模编队控制算法，以实现多机器人以任意队形高精度稳定的编队，并利用MATLAB进行对比仿真试验研究。结果表明，这种预设性模糊滑模控制算法不仅可以实现多个机器人以任意队形编队，而且保证了机器人编队的暂态和稳态性能。与现有控制法相比，该预设性编队控制算法具有更高的控制精度和稳定性。
- 多移动机器人 /
- 预设性能 /
- 模糊滑模控制算法 /
- 编队
Abstract: Aiming at the problems of poor self-adaptive and low control accuracy of multi mobile robot control system when communication range is restricted and external interference exists, a prescribed performance fuzzy sliding-mode formation control algorithm based on the hyperbolic tangent constraint function is proposed to realize high-precision and stable formation of multiple robots in arbitrary formation. Then, the control system was verified by the simulation experiment in MATLAB. The results show that under the action of the designed prescribed performance fuzzy-sliding controller can not only realize the formation of multiple robots in arbitrary formation, but also ensure the transient and steady performance of the formation. And compared with the existing control method, it is proved that the proposed control method has high control accuracy and stability.
- multiple mobile robots /
- prescribed performance /
- fuzzy-sliding control algorithm /
- formation

HTML全文

图 1 移动机器人运动-动力学模型

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图 2 跟随者与虚拟领航者相对运动位姿模型

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图 3 输入变量隶属函数

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图 4 输出变量隶属函数

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图 5 三角形-曲线轨迹编队

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图 6 直线形-异构圆形轨迹编队

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图 7 同构圆形轨迹编队图

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图 8 8字形编队轨迹

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图 9 A算法跟随者轨迹跟踪误差

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图 10 B算法跟随者轨迹跟踪误差

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图 11 A算法跟随者位置跟踪误差

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图 12 B算法跟随者位置跟踪误差

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图 13 A算法速度跟踪误差

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图 14 B算法跟随者速度跟踪误差

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表 1 各状态变量跟踪误差对比

算法	Avex/ (10⁻³ m)	Avey/ (10⁻⁴ m)	Aveθ/ (10⁻³ rad)	Avev/ [10⁻³ (m·s⁻¹)]	Avew/ [10⁻⁴ (rad·s⁻¹)]
A	5 × 10⁻⁵	5 × 10⁻⁴	3 × 10⁻⁵	1 × 10⁻¹⁰	4 × 10⁻⁴
B	4.1	2.9	3.8	4.7	2.6

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表 2 队形整体评价对比

算法	f₁/10⁻³ m	f₂/10⁻² m	f₃/m
A	9.6 × 10⁻⁵	7.7	0.00899
B	1.112	7.2	10.2

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参考文献(20)

[1]	王伟嘉, 郑雅婷, 林国政, 等. 集群机器人研究综述[J]. 机器人, 2020, 42(2): 232-256 WANG W J, ZHENG Y T, LIN G Z, et al. Swarm robotics: a review[J]. Robot, 2020, 42(2): 232-256 (in Chinese)
[2]	WANG C Y, TNUNAY H, ZUO Z Y, et al. Fixed-time formation control of multirobot systems: design and experiments[J]. IEEE Transactions on Industrial Electronics, 2019, 66(8): 6292-6301 doi: 10.1109/TIE.2018.2870409
[3]	YOSHIDA K, FUKUSHIMA H, KON K, et al. Control of a group of mobile robots based on formation abstraction and decentralized locational optimization[J]. IEEE Transactions on Robotics, 2014, 30(3): 550-565 doi: 10.1109/TRO.2013.2293836
[4]	邢小军, 席奥, 闫建国. 多无人机协同编队最优鲁棒控制方法研究[J]. 西北工业大学学报, 2013, 31(5): 722-726 doi: 10.3969/j.issn.1000-2758.2013.05.009 XING X J, XI A, YAN J G. Study on optimal robust control for multiple UAVs′ collaborative formation flight[J]. Journal of Northwestern Polytechnical University, 2013, 31(5): 722-726 (in Chinese) doi: 10.3969/j.issn.1000-2758.2013.05.009
[5]	WU H, LI J M, CHEN J X. Distributed adaptive iterative learning consensus for uncertain topological multi-agent systems based on T-S fuzzy models[J]. International Journal of Fuzzy Systems, 2018, 20(8): 2605-2619 doi: 10.1007/s40815-018-0518-z
[6]	MATRAJI I, AL-DURRA A, HARYONO A, et al. Trajectory tracking control of skid-steered mobile robot based on adaptive second order sliding mode control[J]. Control Engineering Practice, 2018, 72: 167-176 doi: 10.1016/j.conengprac.2017.11.009
[7]	赖欣, 陆阳, 周乐, 等. 轮式移动机器人的Back-stepping滑模模糊自适应轨迹跟踪控制[J]. 机械科学与技术, 2018, 37(12): 1834-1840 LAI X, LU Y, ZHOU L, et al. Trajectory tracking with Back-stepping sliding mode fuzzy adaptive control for wheeled mobile robot[J]. Mechanical Science and Technology for Aerospace Engineering, 2018, 37(12): 1834-1840 (in Chinese)
[8]	WANG J H, ZHANG X, XU Y, et al. Distributed adaptive formation control for non-identical non-linear multi-agents systems based on sliding mode[J]. IET Control Theory & Applications, 2019, 13(2): 222-229
[9]	WANG P C, ZHANG D F, LU B C. Robust fuzzy sliding mode control based on low pass filter for the welding robot with dynamic uncertainty[J]. Industrial Robot, 2020, 47(1): 111-120
[10]	PHAM H A, SORIANO T, VAN NGO H, et al. Distributed adaptive neural network control applied to a formation tracking of a group of low-cost underwater drones in hazardous environments[J]. Applied Sciences, 2020, 10(5): 1732 doi: 10.3390/app10051732
[11]	刘安东, 秦冬冬. 基于虚拟结构法的多移动机器人分布式预测控制[J]. 控制与决策, 2021, 36(5): 1273-1280 LIU A D, QIN D D. Distributed predictive control of multiple mobile robots based on virtual structure method[J]. Control and Decision, 2021, 36(5): 1273-1280 (in Chinese)
[12]	CHEN S Y, ZHANG T, ZOU Y B, et al. Fuzzy-sliding mode force control research on robotic machining[J]. Journal of Robotics, 2017, 2017: 8128479
[13]	DJELAL N, SAADIA N, RAMDANE-CHERIF A, et al. Adaptive force-vision control of robot manipulator using sliding mode and fuzzy logic[J]. Automatic Control and Computer Sciences, 2019, 53(3): 203-213 doi: 10.3103/S0146411619030027
[14]	NAIR R R, KARKI H, SHUKLA A, et al. Fault-tolerant formation control of nonholonomic robots using fast adaptive gain nonsingular terminal sliding mode control[J]. IEEE Systems Journal, 2019, 13(1): 1006-1017 doi: 10.1109/JSYST.2018.2794418
[15]	BECHLIOULIS C P, ROVITHAKIS G A. Decentralized robust synchronization of unknown high order nonlinear multi-agent systems with prescribed transient and steady state performance[J]. IEEE Transactions on Automatic Control, 2017, 62(1): 123-134 doi: 10.1109/TAC.2016.2535102
[16]	DAI S L, HE S D, LIN H, et al. Platoon formation control with prescribed performance guarantees for USVs[J]. IEEE Transactions on Industrial Electronics, 2018, 65(5): 4237-4246 doi: 10.1109/TIE.2017.2758743
[17]	VERGINIS C K, BECHLIOULIS C P, DIMAROGONAS D V, et al. Robust distributed control protocols for large vehicular platoons with prescribed transient and steady-state performance[J]. IEEE Transactions on Control Systems Technology, 2018, 26(1): 299-304 doi: 10.1109/TCST.2017.2658180
[18]	DAI S L, HE S D, CHEN X, et al. Adaptive leader-follower formation control of nonholonomic mobile robots with prescribed transient and steady-state performance[J]. IEEE Transactions on Industrial Informatics, 2020, 16(6): 3662-3671 doi: 10.1109/TII.2019.2939263
[19]	李海婷, 张鹏超, 李文科, 等. 轮式移动机器人的直接模糊自适应输出反馈动力学控制[J]. 陕西理工大学学报, 2020, 36(2): 18-24 LI H T, ZHANG P C, LI W K, et al. Direct fuzzy adaptive output feedback dynamics control for wheeled mobile robot[J]. Journal of Shaanxi University of Technology, 2020, 36(2): 18-24 (in Chinese)
[20]	刘金琨. 机器人控制系统的设计与MATLAB仿真[M]. 北京: 清华大学出版社, 2017: 15-16 LIU J K. Design and MATLAB simulation of robot control system[M]. Beijing: Tsinghua University Press, 2017: 15-16 (in Chinese)