无人自主系统分布式协同控制研究综述

任鸿儒; 刘庆海; 周琪; 鲁仁全

doi:10.12052/gdutxb.240085

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

无人自主系统分布式协同控制研究综述

特约综述 | 更新时间：2026-05-27

- 无人自主系统分布式协同控制研究综述
- A Review of Distributed Cooperative Control Research on Unmanned Autonomous Systems
- 广东工业大学学报 2024年41卷第4期页码：1-13
- 作者机构：
  
  1. 广东工业大学自动化学院,广东,广州,510006
  2. 广东工业大学粤港智能决策与协同控制联合实验室,广东,广州,510006
  3. 广东工业大学广东省智能决策与协同控制重点实验室,广东,广州,510006
- 作者简介：
  
  任鸿儒(1991–)，男，副教授，主要研究方向为无人自主系统、智能控制、协同控制，E-mail：renhongru2019@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目 (62121004, 62033003, U23A20341);广东省基础与应用基础研究基金资助项目 (2023A1515011527, 2022A1515011506, 2023B1515120010)
- DOI：10.12052/gdutxb.240085
  中图分类号： TP242;TP273
- 纸质出版：2024
- 稿件说明：
移动端阅览
任鸿儒, 刘庆海, 周琪, 鲁仁全. 无人自主系统分布式协同控制研究综述[J]. 广东工业大学学报, 2024, 41(4): 1-13.

Ren Hong-ru, Liu Qing-hai, Zhou Qi, et al. A Review of Distributed Cooperative Control Research on Unmanned Autonomous Systems[J]. 2024, 41(4): 1-13.
任鸿儒, 刘庆海, 周琪, 鲁仁全. 无人自主系统分布式协同控制研究综述[J]. 广东工业大学学报, 2024, 41(4): 1-13. DOI： 10.12052/gdutxb.240085.

Ren Hong-ru, Liu Qing-hai, Zhou Qi, et al. A Review of Distributed Cooperative Control Research on Unmanned Autonomous Systems[J]. 2024, 41(4): 1-13. DOI： 10.12052/gdutxb.240085.

摘要

随着信息技术、人工智能和机器人技术的飞速发展，无人自主系统在军事、航空航天、海洋探索、灾难救援以及智能交通等领域展现出巨大的应用潜力。分布式协同控制作为实现多无人自主系统高效、灵活协作的关键技术，已成为研究的热点。本文综述了无人自主系统分布式协同控制的研究进展。首先探讨了在一致性问题、编队控制和分布式优化三个方面的核心理论，然后结合当前多无人自主系统的实际应用，给出了无人机、无人车、无人水面舰艇、无人潜航器和多模态协同控制的最新研究成果介绍，最后探讨了该领域的未来挑战和发展方向。

Abstract

With the rapid development of information technology

artificial intelligence

and robotics

unmanned autonomous systems have demonstrated tremendous application potential in fields such as military

aerospace

marine exploration

disaster rescue

and intelligent transportation. Distributed cooperative control

as a key technology for achieving efficient and flexible collaboration among multiple unmanned autonomous systems

has become a research focus. A review is conducted on the research progress in distributed cooperative control of unmanned autonomous systems. Firstly

it discusses the core theories in the aspects of consensus problems

formation control

and distributed optimization. Then

combining the practical applications of current multiple unmanned autonomous systems

it presents the latest research achievements in unmanned aerial vehicles

unmanned ground vehicles

unmanned surface vessels

unmanned underwater vehicles

and multi-modal cooperative control. Finally

it explores the future challenges and development in this field.

关键词

Keywords

references

CHEN J, XIN B. Key scientific problems in the autonomous cooperation of manned-unmanned systems[J]. Scientia Sinica Informationis, 2018, 48(9): 1270-1274.

MA H, ZHOU Q, LI H, et al . Adaptive prescribed performance control of a flexible-joint robotic manipulator with dynamic uncertainties[J ] . IEEE Transactions on Cybernetics, 2021, 52(12): 12905-12915.

TAO J, XIAO Z, CHEN J, et al . Event-triggered control for Markov jump systems subject to mismatched modes and strict dissipativity[J ] . IEEE Transactions on Cybernetics, 2021, 53(3): 1537-1546.

ZHOU Q, LI H Y, WANG L J, et al . Prescribed performance observer-bas ed adaptive fuzzy control for nonstrict-feedback stochastic nonlinear systems[J ] . IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 48(10): 1747-1758.

QIU J H, CHENG Z J, LIN G H, et al . Prescribed performance control for a class of nonlinear pure-feedback systems with actuator faults[J ] . Journal of Guangdong University of Technology, 2023, 40(2): 55-63.

CAO Y C, YU W W, REN W, et al . An overview of recent progress in the study of distributed multi-agent coordination[J ] . IEEE Transactions on Industrial informatics, 2012, 9(1): 427-438.

LI T, FU M Y, XIE L H, et al . Distributed consensus with limited communication data rate[J ] . IEEE Transactions on Automatic Control, 2010, 56(2): 279-292.

GAO S, WEN G G, ZHAI X Q, et a l. Finite-/fixed-time bipartite consensus for first-order multi-agent systems via impulsive control[J ] . Applied Mathematics and Computation, 2023, 442: 127740.

SUN H, LIU Y G, LI F Z. Optimal consensus via distributed protocol for second-order multiagent systems[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(10): 6218-6228.

JIANG X, XIA G, FENG Z. Output consensus of high‐order linear multi‐agent systems with time‐varying delays[J]. IET Control Theory Applications, 2019, 13(8): 1084-1094.

LI Z K, DUAN Z S, CHEN G R, et al . Consensus of multiagent systems and synchronization of complex networks: a unified viewpoint[J ] . IEEE Transactions on Circuits and Systems I: Regular Papers, 2009, 57(1): 213-224.

WANG G. Consensus control in heterogeneous nonlinear multiagent systems with position feedback and switching topologies[J]. IEEE Transactions on Network Science and Engineering, 2022, 9(5): 3546-3557.

ZHANG Z H, PENG S G. Leader-following consensus of second-order multi-agent systems with switching topology[J]. Journal of Guangdong University of Technology, 2018, 35(2): 75-80.

YOU K Y, LI Z K, XIE L H. Consensus condition for linear multi-agent systems over randomly switching topologies[J]. Automatica, 2013, 49(10): 3125-3132.

WANG G L, SUN Y Y. Consensus of multiagent systems with random switching topologies and its application[J]. International Journal of Robust and Nonlinear Control, 2020, 30(5): 2079-2096.

HADDOCK J, JARMAN B, YAP C. Paving the way for consensus: convergence of block gossip algorithms[J]. IEEE Transactions on Information Theory, 2022, 68(11): 7515-7527.

SHANG Y L. Median-based resilient consensus over time-varying random networks[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, 69(3): 1203-1207.

LU R Q, XU Y, ZHANG R D. A new design of model predictive tracking control for networked control system under random packet loss and uncertainties[J]. IEEE Transactions on Industrial Electronics, 2016, 63(11): 6999-7007.

OLFATI-SABER R, MURRAY R M. Consensus problems in networks of agents with switching topology and time-delays[J]. IEEE Transactions on Automatic Control, 2004, 49(9): 1520-1533.

ZHU J L, JIN C Y, GUO H P. H ∞ consensus of multi-agent network with a time-varying reference state[J ] . Journal of Guangdong University of Technology, 2011, 28(3): 83-86.

DENG C, CHE W W, WU Z G. A dynamic periodic event-triggered approach to consensus of heterogeneous linear multiagent systems with time-varying communication delays[J]. IEEE Transactions on Cybernetics, 2020, 51(4): 1812-1821.

XU J, ZHANG G L, ZENG J, et al . Robust guaranteed cost consensus for high-order discrete-time multi-agent systems with switching topologies and time delays[J ] . Acta Automatica Sinica, 2019, 45(2): 360-373.

LI Z Q, WANG Y J, SONG Y D, et al . Global consensus tracking control for high-order nonlinear multiagent systems with prescribed performance[J ] . IEEE Transactions on Cybernetics, 2022, 53(10): 6529-6537.

MA Q, XU S Y, LEWIS F L. Second-order consensus for directed multi-agent systems with sampled data[J]. International Journal of Robust and Nonlinear Control, 2014, 24(16): 2560-2573.

ZHANG W B, TANG Y, HAN Q L, et al . Sampled-data consensus of linear time-varying multiagent networks with time-varying topologies[J ] . IEEE Transactions on Cybernetics, 2020, 52(1): 128-137.

SUN J, WANG Z S, RONG N N. Sampled-data consensus of multiagent systems with switching jointly connected topologies via time-varying Lyapunov function approach[J]. International Journal of Robust and Nonlinear Control, 2020, 30(14): 5369-5385.

BARKAI G, MIRKIN L, ZELAZO D. On sampled-data consensus: divide and concur[J]. IEEE Control Systems Letters, 2021, 6: 343-348.

HU R, PENG S G. Impulsive observer-based leader-following consensus for multi-agent systems[J]. Journal of Guangdong University of Technology, 2023, 40(5): 88-93.

KASHYAP A, BAŞAR T, SRIKANT R. Quantized consensus[J]. Automatica, 2007, 43(7): 1192-1203.

LAVAEI J, MURRAY R M. Quantized consensus by means of gossip algorithm[J]. IEEE Transactions on Automatic Control, 2011, 57(1): 19-32.

MA J, JI H B, SUN D, et al . An approach to quantized consensus of continuous-time linear multi-agent systems[J ] . Automatica, 2018, 91: 98-104.

ZHANG Z Q, ZHANG L, HAO F, et al . Leader-following consensus for linear and Lipschitz nonlinear multiagent systems with quantized communication[J ] . IEEE Transactions on Cybernetics, 2016, 47(8): 1970-1982.

LI X X, CHEN M Z Q, SU H S. Quantized consensus of multi-agent networks with sampled data and Markovian interaction links[J]. IEEE Transactions on Cybernetics, 2018, 49(5): 1816-1825.

CHEN S M, JIANG G L, ZHANG Z. Bipartite practical consensus control of multi-agent systems with communication constraints[J]. Acta Automatica Sinica, 2022, 48(5): 1318-1326.

LI H Y, LUO J H, MA H, et al . Observer-based event-triggered iterative learning consensus for locally lipschitz nonlinear MASs[J ] . IEEE Transactions on Cognitive and Developmental Systems, 2024, 16(1): 46-56.

REN H R, CHENG Z J, QIN J, et al . Deception attacks on event-triggered distributed consensus estimation for nonlinear systems[J ] . Automatica, 2023, 154: 111100.

XU Y, YAO Z Z, LU R, et al . A novel fixed-time protocol for first-order consensus tracking with disturbance rejection[J ] . IEEE Transactions on Automatic Control, 2021, 67(11): 6180-6186.

REN W, ATKINS E. Distributed multi-vehicle coordinated control via local information exchange[J]. International Journal of Robust and Nonlinear Control: IFAC‐Affiliated Journal, 2007, 17(10‐11): 1002-1033.

BUCKLEY I, EGERSTEDT M. Infinitesimally shape-similar motions using relative angle measurements[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . Vancouver, BC, Canada: IEEE, 2017: 1077-1082.

WANG X M, ZERR B, THOMAS H, et al . Pattern formation of multi-AUV systems with the optical sensor based on displacement-based formation control[J ] . International Journal of Systems Science, 2020, 51(2): 348-367.

LI Z, TANG Y, FAN Y Q, et al . Formation control of multi-agent systems with constrained mismatched compasses[J ] . IEEE Transactions on Network Science and Engineering, 2022, 9(4): 2224-2236.

ZHAO S Y, ZELAZO D. Bearing rigidity and almost global bearing-only formation stabilization[J]. IEEE Transactions on Automatic Control, 2015, 61(5): 1255-1268.

KRICK L, BROUCKE M E, FRANCIS B A. Stabilisation of infinitesimally rigid formations of multi-robot networks[J]. International Journal of control, 2009, 82(3): 423-439.

OH K K, AHN H S. Formation control of mobile agents based on inter-agent distance dynamics[J]. Automatica, 2011, 47(10): 2306-2312.

OH K K, AHN H S. Distance-based undirected formations of single-in tegrator and double-integrator modeled agents in n -dimensional space[J ] . International Journal of Robust and Nonlinear Control, 2014, 24(12): 1809-1820.

LIU S G, WANG H, LIU R H. Distance-based control for UAVs formation path tracking[J]. Flight Dynamics, 2023, 41(1): 27-33.

LIN Z Y, WANG L L, HAN Z M, et al . Distributed formation control of multi-agent systems using complex Laplacian[J ] . IEEE Transactions on Automatic Control, 2014, 59(7): 1765-1777.

HAN Z M, WANG L L, LIN Z Y, et al . Formation control with size scaling via a complex Laplacian-based approach[J ] . IEEE Transactions on Cybernetics, 2015, 46(10): 2348-2359.

CONNELLY R. Generic global rigidity[J]. Discrete Computational Geometry, 2005, 33: 549-563.

XU Y, ZHAO S Y, LUO D L, et al . Affine formation maneuver control of multi-agent systems with directed interaction graphs[C ] //2018 37th Chinese Control Conference (CCC) . Wuhan, China: IEEE, 2018: 4563-4568.

YANG Q K, CAO M, SUN Z Y, et al . Formation scaling control using the stress matrix[C ] //2017 IEEE 56t h Annual Conference on Decision and Control (CDC) . Melbourne, VIC, Australia: IEEE, 2017: 3449-3454.

LI X, CAI G B, WU T, et al . Formation transformation control of UAV swarm based on stress matrix[J ] . Control and Decision, 2024, 39(7): 2195-2204.

XIN B, CHEN J, PENG Z H. Intelligent optimized control: overview and prospect[J]. Acta Automatica Sinica, 2013, 39(11): 1831-1848.

WANG H W, LIAO X F, HUANG T W, et al . Cooperative distributed optimization in multiagent networks with delays[J ] . IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2014, 45(2): 363-369.

HONG Y G, ZHAI C. Dynamic coordination and distributed control design of multi-agent systems[J]. Control Theory and Technology, 2011, 28(10): 1506-1512.

YANG T, CAI T Y. Research status and prospects of distributed collaborative optimization[J]. Scientia Sinica Technologica, 2020, 50(11): 1414-1425.

NEDIC A, OZDAGLAR A. Distributed subgradient methods for multi-agent optimization[J]. IEEE Transactions on Automatic Control, 2009, 54(1): 48-61.

JAKOVETIĆ D, XAVIER J, MOURA J M F. Fast distributed gradient methods[J]. IEEE Transactions on Automatic Control, 2014, 59(5): 1131-1146.

SHI W, LING Q, WU G, et al . Extra: an exact first-order algorithm for decentralized consensus optimization[J ] . SIAM Journal on Optimization, 2015, 25(2): 944-966.

NEDIC A, OLSHEVSKY A, SHI W. Achieving geometric convergence for distributed optimization over time-varying graphs[J]. SIAM Journal on Optimization, 2017, 27(4): 2597-2633.

QU G N, LI N. Harnessing smoothness to accelerate distributed optimization[J]. IEEE Transactions on Control of Network Systems, 2017, 5(3): 1245-1260.

LI H Q, ZHENG L F, WANG Z, et al . S-DIGing: a stochastic gradient tracking algorithm for distributed optimization[J ] . IEEE Transactions on Emerging Topics in Computational Intelligence, 2022, 6(1): 53-65.

TSIANOS K I, LAWLOR S, RABBAT M G. Push-sum distributed dual averaging for convex optimization[C]//2012 IEEE 51st IEEE Conference on Decision and Control (cdc). Hawall: IEEE, 2012: 5453-5458.

NEDIĆ A, OLSHEVSKY A. Distributed optimization over time-varying directed graphs[J]. IEEE Transactions on Automatic Control, 2014, 60(3): 601-615.

PU S, SHI W, XU J M, et al . Push-pull gradient methods for distributed optimization in networks[J ] . IEEE Transactions on Automatic Control, 2020, 66(1): 1-16.

ZHANG S J, YI X L, GEORGE J, et al . Computational convergence analysis of distributed optimization algorithms for directed graphs[C ] //2019 IEEE 15th International Conference on Control and Automation (ICCA) . Edinburgh, UK: IEEE, 2019: 1096-1101.

XI C G, MAI V S, XIN R, et al . Linear convergence in optimization over directed graphs with row-stochastic matrices[J ] . IEEE Transactions on Automatic Control, 2018, 63(10): 3558-3565.

LU Q G, LIAO X F, Li H Q, et al . A Nesterov-like gradient tracking algorithm for distributed optimization over directed networks[J ] . IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(10): 6258-6270.

GHARESIFARD B, CORTÉS J. Distributed continuous-time convex optimization on weight-balanced digraphs[J]. IEEE Transactions on Automatic Control, 2013, 59(3): 781-786.

LI Z H, DING Z T, SUN J Y, et al . Distributed adaptive convex optimization on directed graphs via continuous-time algorithms[J ] . IEEE Transactions on Automatic Control, 2017, 63(5): 1434-1441.

WANG Q L, WU W Q. A distributed finite-time optimization algorithm for directed networks of continuous‐time agents[J]. International Journal of Robust and Nonlinear Control, 2024, 34(6): 4032-4050.

NEDIC A, OZDAGLAR A, PARRILO P A. Constrained consensus and optimization in multi-agent networks[J]. IEEE Transactions on Automatic Control, 2010, 55(4): 922-938.

QIU Z R, LIU S, XIE L H. Distributed constrained optimal consensus of multi-agent systems[J]. Automatica, 2016, 68: 209-215.

LIN P, REN W, SONG Y D. Distributed multi-agent optimization subject to nonidentical constraints and communication delays[J]. Automatica, 2016, 65: 120-131.

LEI J L, CHEN H F, FANG H T. Primal-dual algorithm for distributed constrained optimization[J]. Systems Control Letters, 2016, 96: 110-117.

ZENG J S, YIN W T. Extrapush for convex smooth decentralized optimization over directed networks[J]. Journal of Computational Mathematics, 2017: 383-396.

WANG Z, WANG D, SUN J Z, et al . Exponential convergence rate of distributed optimisation for multi‐agent systems with constraints set over a directed graph[J ] . IET Control Theory Applications, 2018, 12(9): 1201-1207.

ZHU M H, MARTINEZ S. On distributed convex optimization under inequality and equality constraints[J]. IEEE Transactions on Automatic Control, 2011, 57(1): 151-164.

YUAN D M, XU S Y, ZHAO H Y. Distributed primal-dual subgradient method for multiagent optimization via consensus algorithms[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) , 2011, 41(6): 1715-1724.

YUAN D M, HO D W C, XU S Y. Regularized primal-dual subgradient method for distributed constrained optimization[J]. IEEE Transactions on Cybernetics, 2015, 46(9): 2109-2118.

SUN S, XU J, REN W. Distributed continuous-time algorithms for time-varying constrained convex optimization[J]. IEEE Transactions on Automatic Control, 2023, 68(7): 3931-3946.

LIU Q S, YANG S F, HONG Y G. Constrained consensus algorithms with fixed step size for distributed convex optimization over multiagent networks[J]. IEEE Transactions on Automatic Control, 2017, 62(8): 4259-4265.

YANG S F, LIU Q S, WANG J. A multi-agent system with a proportional-integral protocol for distributed constrained optimization[J]. IEEE Transactions on Automatic Control, 2016, 62(7): 3461-3467.

ZHU Y N, YU W W, WEN G H, et al . Continuous-time distributed subgradient algorithm for convex optimization with general constraints[J ] . IEEE Transactions on Automatic Control, 2018, 64(4): 1694-1701.

XIE P, YOU K Y, TEMPO R, et al . Distributed convex optimization with inequality constraints over time-varying unbalanced digraphs[J ] . IEEE Transactions on Automatic Control, 2018, 63(12): 4331-4337.

JIANG X, ZENG X L, SUN J, et al. Research status and prospect of distributed optimization for multiple aircraft[J ] . Acta Aeronauticaet Astronautica Sinica, 2021, 42(4): 524551.

FU X, LIU K, GAO X. Multi-UAVs communication-aware cooperative target tracking[J]. Applied Sciences, 2018, 8(6): 870.

WU J, LUO C, LUO Y, et al . Distributed UAV swarm formation and collision avoidance strategies over fixed and switching topologies[J ] . IEEE Transactions on Cybernetics, 2021, 52(10): 10969-10979.

GUO Z J, LI H Y, MA H, et al . Distributed optimal attitude synchronization control of multiple QUAVs via adaptive dynamic programming[J ] . IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(6): 8053-8063.

MENG W, YANG Y L, ZANG J Y, et al . DTUAV: a novel cloud-based digital twin system for unmanned aerial vehicles[J ] . Simulation, 2023, 99(1): 69-87.

SUN J, DENG F, CHEN J. Control system of ground-based moving platforms: state of the art and future trends. Acta Automatica Sinica, 2018, 44(11): 1985-1999.

LIU Y G, GAO H L, XU B G, et al . Autonomous coordinated control of a platoon of vehicles with multiple disturbances[J ] . IET Control Theory Applications, 2014, 8(18): 2325-2335.

GUO X G, WANG J L, LIAO F, et al . Distributed adaptive sliding mod e control strategy for vehicle-following systems with nonlinear acceleration uncertainties[J ] . IEEE Transactions on Vehicular Technology, 2016, 66(2): 981-991.

KWON J W, CHWA D. Adaptive bidirectional platoon control using a coupled sliding mode control method[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(5): 2040-2048.

XIE G Q, ZHAO J W, LI Y, et al . Cooperative lane-changing based on multi-cluster system[J ] . Journal of Guangdong University of Technology, 2021, 38(5): 1-9.

ZHANG W D, LIU X C, HAN P. Progress and challenges of overwater unmanned systems[J]. Acta Automatica Sinica, 2020, 46(5): 847-857.

DO K D. Synchronization motion tracking control of multiple underactuated ships with collision avoidance[J]. IEEE Transactions on Industrial Electronics, 2016, 63(5): 2976-2989.

PENG Z, WANG J, WANG D. Distributed maneuvering of autonomous surface vehicles based on neurodynamic optimization and fuzzy approximation[J]. IEEE Transactions on Control Systems Technology, 2017, 26(3): 1083-1090.

PARK B S, YOO S J. An error transformation approach for connectivity-preserving and collision-avoiding formation tracking of networked uncertain underactuated surface vessels[J]. IEEE Transactions on Cybernetics, 2018, 49(8): 2955-2966.

MENG C C, ZHANG X Y. Distributed leaderless formation control for multiple autonomous underwater vehicles based on adaptive nonsingular terminal sliding mode[J]. Applied Ocean Research, 2021, 115: 102781.

SHI L L, ZHENG R H, LIU M Q, et al . Distributed circumnavigation control of autonomous underwater vehicles based on local information[J ] . Systems Control Letters, 2021, 148: 104873.

LI J, DU J L, LEWIS F L. Distributed three‐dimension time‐varying formation control with prescribed performance for multiple underactuated autonomous underwater vehicles[J]. International Journal of Robust and Nonlinear Control, 2021, 31(13): 6272-6287.

SU L F, HUA Y Z , DONG X W, et al . Human-UAV swarm multi-modal intelligent interaction methods[J ] . Acta Aeronauticaet Astronautica Sinica, 2022, 43(S1): 727001.

MA Y L, LI Z X, MALEKIAN R, et al . A novel multimode hybrid control method for cooperative driving of an automated vehicle platoon[J ] . IEEE Internet of Things Journal, 2020, 8(7): 5822-583 8.

LU Y Z, SHAN Q H, XIAO G Y, et al . Green polymorphic cooperative formation strategy of low-carbon unmanned surface vessels[J ] . Frontiers in Energy Research, 2022, 10: 953485.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于多集群系统的车辆协同换道控制

基于数字混频的脉冲式激光测距方法的研究与实现

SCA-YOLO: 基于可变形卷积与上下文感知注意力的实时目标检测

储能变流器电能质量治理策略研究

基于PSI技术的福州市地面形变监测与影响因素分析