基于积分滑模的二阶系统任意预设时间编队控制

doi:10.19678/j.issn.1000-3428.0068654

摘要/Abstract

摘要：

在通信过程中, 智能体受各种未知干扰信号的影响, 导致系统性能降低。针对二阶非线性多智能体系统, 研究了在有向通信拓扑结构下实现任意预设时间编队期望的问题。首先, 结合预设时间机制与积分滑模控制策略, 设计一种新的编队控制协议, 该控制协议能够确保二阶多智能体系统在任意预设时间内实现到达段和滑动段2个阶段的收敛, 引入的积分滑模控制项有效降低了系统的稳态误差, 并提高其在预设时间控制下的鲁棒稳定性; 其次, 利用李雅普诺夫方法与代数图论知识, 分析得出二阶系统在该控制方法下达到预设时间编队期望的充分条件, 并证明了包含预设时变函数控制协议的有界性。仿真实验结果验证所设计控制方法的有效性与可行性, 即给定任意初值, 带干扰项的二阶系统均可在任意预设时间内快速准确地达到稳定状态。

关键词: 滑模控制, 任意预设时间控制, 编队控制, 二阶非线性系统, 多智能体

Abstract:

During the communication process, the agent is influenced by various unknown interference signals, resulting in a decrease in system performance. This study investigates the problem of achieving the desired formation in a directed communication topology for nonlinear second-order multi-agent systems within an arbitrary preset time. First, the preset time mechanism is combined with an integral sliding mode control strategy to integrate the design of the new formation control protocol. This control protocol ensures that the second-order multi-agent system achieves convergence in both the arrival and sliding stages within any preset time. The introduced integral sliding mode control term effectively reduces the steady-state error of the system, thus improving the robust stability of the system under preset time control. Through the analysis of the Lyapunov method and algebraic graph theory, sufficient conditions are obtained for the second-order system to achieve the desired formation time under this control method. Moreover, the boundedness of the control protocol containing preset time-varying functions is proven. The simulation experiment results verify the effectiveness and feasibility of the designed control method, that is, given any initial value, a second-order system with interference terms can quickly and accurately reach a stable state within any preset time.

Key words: sliding mode control, arbitrary preset time control, formation control, second-order nonlinear system, multi-agent

程雯, 过榴晓. 基于积分滑模的二阶系统任意预设时间编队控制[J]. 计算机工程, 2024, 50(11): 163-172.

CHENG Wen, GUO Liuxiao. Arbitrary Preset Time Formation Control of Second-Order System Based on Integral Sliding Mode[J]. Computer Engineering, 2024, 50(11): 163-172.

https://www.ecice06.com/CN/Y2024/V50/I11/163

图/表 7

图1 强连通有向拓扑图G

Fig.1 Strongly connected directed topology graph G

图2 智能体在空间上的编队轨迹图

Fig.2 Formation trajectory diagram of the agent in space

图3 智能体的位置随时间变化轨迹图

Fig.3 Trajectory diagram of the position of the agent variable with time

图4 智能体的速度随时间变化轨迹图

Fig.4 Trajectory diagram of the velocity of the agent variable with time

图5 滑模变量随时间变化轨迹图

Fig.5 Trajectory diagram of sliding mode variable with time

图6 智能体的横坐标误差随时间变化轨迹图

Fig.6 Trajectory diagram of agent's horizontal coordinate error variable with time

图7 3种不同外部干扰对应的误差变化对比

Fig.7 Comparison of error variations corresponding to three different external disturbances

参考文献 26

1	YE D Y, ZHANG M J, YANG Y. A multi-agent framework for packet routing in wireless sensor networks. Sensors, 2015, 15 (5): 10026- 10047. doi: 10.3390/s150510026
2	WANG H S, GUO D J, LIANG X W, et al. Adaptive vision-based leader-follower formation control of mobile robots. IEEE Transactions on Industrial Electronics, 2016, 64 (4): 2893- 2902. doi: 10.1109/TIE.2016.2631514
3	赵学远, 周绍磊, 王帅磊, 等. 基于多无人机系统的编队包含控制. 计算机工程, 2020, 46 (8): 284- 289. doi: 10.19678/j.issn.1000-3428.0055224
	ZHAO X Y, ZHOU S L, WANG S L, et al. Formation containment control based on multiple unmanned aerial vehicle system. Computer Engineering, 2020, 46 (8): 284- 289. doi: 10.19678/j.issn.1000-3428.0055224
4	LIU G P, ZHANG S J. A survey on formation control of small satellites. Proceedings of the IEEE, 2018, 106 (3): 440- 457. doi: 10.1109/JPROC.2018.2794879
5	CHEN Q, WANG Y, JIN Y, et al. A survey of an intelligent multi-agent formation control. Applied Sciences, 2023, 13 (34): 1- 10.
6	刘杨, 刘美杰. 基于领导跟随的欠驱动船舶编队跟踪控制. 控制工程, 2013, 20 (5): 980-983, 989. doi: 10.3969/j.issn.1671-7848.2013.05.043
	LIU Y, LIU M J. Leader/follower formation control of underactuated surface ships strategy. Control Engineering of China, 2013, 20 (5): 980-983, 989. doi: 10.3969/j.issn.1671-7848.2013.05.043
7	FAN J J, LIAO Y L, LI Y, et al. Formation control of multiple unmanned surface vehicles using the adaptive null-space-based behavioral method. IEEE Access, 2019, 7, 87647- 87657. doi: 10.1109/ACCESS.2019.2925466
8	CHEN X, HUANG F H, ZHANG Y G, et al. A novel virtual-structure formation control design for mobile robots with obstacle avoidance. Applied Sciences, 2020, 10 (17): 5807. doi: 10.3390/app10175807
9	ZHEN Q Z, WAN L, LI Y L, et al. Formation control of a multi-AUVs system based on virtual structure and artificial potential field on SE (3). Ocean Engineering, 2022, 253, 111148. doi: 10.1016/j.oceaneng.2022.111148
10	LIU L, PERC M, CAO J D. Aperiodically intermittent stochastic stabilization via discrete time or delay feedback control. Science China(Information Sciences), 2019, 62 (7): 183- 195.
11	ZHU J H, WEN G X, LI B. Decentralized adaptive formation control based on sliding mode strategy for a class of second-order nonlinear unknown dynamic multi-agent systems. International Journal of Adaptive Control and Signal Processing, 2022, 36 (4): 1045- 1058. doi: 10.1002/acs.3381
12	DONG L J, CHAI S C, ZHANG B H, et al. Sliding mode control for multi-agent systems under a time-varying topology. International Journal of Systems Science, 2016, 47 (9): 2193- 2200. doi: 10.1080/00207721.2014.979335
13	NAIR R R, BEHERA L, KUMAR S. Event-triggered finite-time integral sliding mode controller for consensus-based formation of multirobot systems with disturbances. IEEE Transactions on Control Systems Technology, 2017, 27 (1): 39- 47.
14	BAI J, WEN G G, RAHMANI A, et al. Consensus for the fractional-order double-integrator multi-agent systems based on the sliding mode estimator. IET Control Theory & Applications, 2018, 12 (5): 621- 628.
15	NGUYEN N P, PARK D, NGOC D N, et al. Quadrotor formation control via terminal sliding mode approach: theory and experiment results. Drones, 2022, 6 (7): 172.
16	YU S H, LONG X J. Finite-time consensus for second-order multi-agent systems with disturbances by integral sliding mode. Automatica, 2015, 54, 158- 165. doi: 10.1016/j.automatica.2015.02.001
17	PARK D J, MOON J W, HAN S I. Finite-time sliding mode controller design for formation control of multi-agent mobile robots. The Journal of Korea Robotics Society, 2017, 12 (3): 339- 349. doi: 10.7746/jkros.2017.12.3.339
18	ZHAO L, YU J P, LIN C, et al. Distributed adaptive fixed-time consensus tracking for second-order multi-agent systems using modified terminal sliding mode. Applied Mathematics and Computation, 2017, 312, 23- 35. doi: 10.1016/j.amc.2017.05.049
19	ZHANG J, YU S, YAN Y. Fixed-time velocity-free sliding mode tracking control for marine surface vessels with uncertainties and unknown actuator faults. Ocean Engineering, 2020, 201, 107107.
20	YANG X W, FAN X P, LONG F, et al. Predefined-time robust control with formation constraints and saturated controls. nonlinear dynamics. Nonlinear Dynamics, 2022, 110 (3): 2535- 2554.
21	WANG Y J, SONG Y D, HILL D J, et al. Prescribed-time consensus and containment control of networked multiagent systems. IEEE Transactions on Cybernetics, 2018, 49 (4): 1138- 1147. doi: 10.1109/TCYB.2017.2788874
22	ZHOU Y, LIU Y F, ZHAO Y. Prescribed-time bipartite consensus formation control for general linear multi-agent systems[C]//Proceedings of the 46th Annual Conference of the IEEE Industrial Electronics Society. Washington D. C., USA: IEEE Press, 2020: 3562-3567.
23	REN Y, ZHOU W, LI Z, et al. Prescribed-time leader-following consensus for stochastic second-order multi-agent systems subject to actuator failures via sliding mode control strategy. Neurocomputing, 2021, 425, 82- 95.
24	LI X, YU Z Y, JIANG H J. Event-triggered fixed-time integral sliding mode control for nonlinear multi-agent systems with disturbances. Entropy, 2021, 23 (11): 1412.
25	LU Q, HAN Q L, ZHONG C L, et al. Finite-time consensus analysis under directed communication topologies for multi-agent systems[C]//Proceedings of the 20th World Congress IFAC. Washington D. C., USA: IEEE Press, 2017: 619-624,
26	WANG J R, LUO X Y, LI X L, et al. Sliding mode formation control of nonlinear multi-agent systems with local Lipschitz continuous dynamics. Journal of Systems Science and Complexity, 2019, 32 (3): 759- 777. doi: 10.1007/s11424-018-7299-1

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