Tracking and Obstacle Avoidance of Multi-mobile Robots Under Model Predictive Control

Peng Ji-guang; Xiao Han-zhen

doi:10.12052/gdutxb.220074

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Tracking and Obstacle Avoidance of Multi-mobile Robots Under Model Predictive Control

更新时间：2026-05-27

- Tracking and Obstacle Avoidance of Multi-mobile Robots Under Model Predictive Control
- Vol. 39, Issue 5, Pages: 93-101(2022)
- 作者机构：
  
  广东工业大学自动化学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.220074
  CLC： TG156
- Published：2022
- 稿件说明：
移动端阅览
彭积广, 肖涵臻. 模型预测控制下多移动机器人的跟踪与避障[J]. 广东工业大学学报, 2022, 39(5): 93-101.

Peng Ji-guang, Xiao Han-zhen. Tracking and Obstacle Avoidance of Multi-mobile Robots Under Model Predictive Control[J]. 2022, 39(5): 93-101.
彭积广, 肖涵臻. 模型预测控制下多移动机器人的跟踪与避障[J]. 广东工业大学学报, 2022, 39(5): 93-101. DOI： 10.12052/gdutxb.220074.

Peng Ji-guang, Xiao Han-zhen. Tracking and Obstacle Avoidance of Multi-mobile Robots Under Model Predictive Control[J]. 2022, 39(5): 93-101. DOI： 10.12052/gdutxb.220074.

摘要

提出了一种基于距离和速度的机器人之间的避障方法，通过与机器人避开障碍物的人工势场法相结合，建立一致性控制编队控制协议。首先，建立机器人之间的通信拓扑关系，以便机器人之间的信息交流。在编队控制层面上，设计具有避碰的编队控制律。然后，在编队跟踪层面上，运用模型预测控制方法，将编队误差运动问题按代价函数转化为最小优化问题。为了在线高效地求解该优化问题，运用了一种广义投影神经网络优化的方法，以便最优解作为控制输入。最后，对多移动机器人编队进行了仿真，验证了所提出策略的有效性。

Abstract

Aiming to control the formation tracking and obstacle avoidance system of multi-mobile robots under the changing topology

an obstacle avoidance method based on distance and speed between robots and an artificial potential field method to avoid obstacles are proposed to establish a consistent control formation control protocol. Firstly

the communication topology between robots is established to facilitate the information exchange between robots. At the level of formation control

a formation control law with collision avoidance is designed. Then

at the level of formation tracking

the formation error motion problem is transformed into a minimum optimization problem according to the cost function by using model predictive control method. In order to efficiently solve the optimization problem online

a generalized projection neural network optimization method is used

in which the optimal solution is used as the control input. Finally

the simulation of multi-mobile robot formation verifies the effectiveness of the proposed strategy.

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references

WANG X, LI S, SHI P. Distributed finite-time containment control for double-integrator multiagent systems [J]. IEEE Transactions on Cybernetics, 2014, 44(9): 1518-1528.

LI S, WANG X. Finite-time consensus and collision avoidance control algorithms for multiple AUVs [J]. Automatica, 2013, 49(11): 3359-3367.

LI S, DU H, LIN X. Finite-time consensus algorithm for multi-agent systems with double-integrator dynamics [J]. Automatica, 2011, 47(8): 1706-1712.

XIAO H Z, LI Z, CHEN C L P. Formation control of leader-follower mobile robots’ systems using model predictive control based on neural-dynamic optimization [J]. IEEE Transactions on Industrial Electronics, 2016, 63(9): 5752-5762.

PANG Z H, ZHENG C B, SUN J, et al . Distance- and velocity-based collision avoidance for time-varying formation control of second-order multi-agent systems [J ] . IEEE Transactions on Circuits and Systems II:Express Briefs, 2021, 68(4): 1253-1257.

WEN G, CHEN C L P, LIU Y J, et al . Neural network-based adaptive leader-following consensus control for a class of nonlinear multiagent state-delay systems [J ] . IEEE Transactions on Cybernetics, 2016, 47(8): 2151-2160.

REN C E, CHEN L, CHEN C L P. Adaptive fuzzy leader-following consensus control for stochastic multiagent systems with heterogeneous nonlinear dynamics [J]. IEEE Transactions on Fuzzy Systems, 2016, 25(1): 181-190.

LI D P, LIU Y J, TONG S, et al . Neural networks-based adaptive control for nonlinear state constrained systems with input delay [J ] . IEEE Transactions on Cybernetics, 2018, 49(4): 1249-1258.

REN C E, CHEN C L P. Sliding mode leader-following consensus controllers for second-order non-linear multi-agent systems[J]. IET Control Theory and Applications, 20-15, 9(10): 1544-1552.

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 System Journal, 2019, 13(1): 1006-1017.

RANJBAR-SAHRAEI B, SHABANINIA F, NEMATI A, et al . A novel robust decentralized adaptive fuzzy control for swarm formation of multiagent systems [J ] . IEEE Transactions on Industrial Electronics, 2012, 59(8): 3124-3134.

MANTOVANI G, FERRARINI L. Temperature control of a commercial building with model predictive control techniques [J]. IEEE Transactions on Industrial Electronics, 2015, 62(4): 2651-2660.

LU X, CHEN H, GAO B, et al . Data-driven predictive gearshift control for dual-clutch transmissions and FPGA implementation [J ] . IEEE Transactions on Industrial Electronics, 2014, 62(1): 599-610.

XIAO H Z, CHEN C L P. Time-varying nonholonomic robot consensus formation using model predictive based protocol with switching topology [J]. Information Sciences, 2021, 567: 201-215.

DAI L, CAO Q, XIA Y Q, et al . Distributed MPC for formation of multi-agent systems with collision avoidance and obstacle avoidance [J ] . Journal of the Franklin Institute, 2017, 354(4): 2068-2085.

SEO J, KIM Y, KIM S, et al . Collision avoidance strategies for unmanned aerial vehicles in formation flight [J ] . IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(6): 2718-2734.

HE S, WANG M, DAI S L, et al . Leader-follower formation control of USVs with prescribed performance and collision avoidance [J ] . IEEE Transactions on Industrial Informatics, 2019, 15(1): 572-581.

ZHOU X, YU X, PENG X. UAV collision avoidance based on varying cells strategy [J]. IEEE Transactions on Aerospace and Electronic Systems, 2019, 55(4): 1743-1755.

SHI Q, LI T, LI J, et al . Adaptive leader-following formation control with collision avoidance for a class of second-order nonlinear multi-agent systems [J ] . Neurocomputing, 2019, 350: 282-290.

PENG Z, WANG D, LI T, et al . Output-feedback cooperative formation maneuvering of autonomous surface vehicles with connectivity preservation and collision avoidance [J ] . IEEE Transactions on Cybernetics, 2020, 50(6): 2527-2535.

XIAO H Z, CHEN C L P, YU D X. Two-level structure swarm formation system with self-organized topology network [J]. Neurocomputing, 2020, 384: 356-367.

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