面向不确定性的信息物理系统鲁棒性研究综述

doi:10.11896/jsjkx.250700017

Abstract

Abstract: Cyber-Physical Systems(CPSs) are complex systems that are deeply coupled between the physical and digital worlds.They have been widely applied in fields such as intelligent transportation,industrial automation,and smart energy.However,the design and operation of CPS often involve various uncertainties.As a result,robustness against these uncertainties has become a critical requirement for ensuring stable and reliable system performance.Currently,the key technologies for ensuring CPS robustness include uncertainty testing,robustness evaluation,and robustness optimization.Uncertainty testing focuses on constructing diverse disturbance scenarios to reveal potential vulnerabilities of the system under complex and uncertain conditions.Robustness evaluation quantifies the system's stability and reliability under various disturbances using multi-dimensional metrics.Robustness optimization,in turn,targets the identified weak points by adjusting system architecture,control strategies,or resource configurations to enhance the system's resilience and adaptability.This paper reviews the progress in these three research areas.It also analyzes the major challenges in current CPS robustness assurance.These include difficulties in identifying and modeling multi-source uncertainties,the absence of unified standards and efficient evaluation methods,and the complexity of deploying robustness optimization strategies in practice.On this basis,the paper outlines potential future research directions,such as test case generation for evolving multi-source uncertainties,AI-driven predictive robustness evaluation,and adaptive robustness repair strategies.This paper aims to provide a structured technical reference for CPS robustness research and promote the development of highly reliable CPS applications.

Key words: Uncertainty, Cyber-physical systems, Robustness, Reliability, Systematic study

CLC Number:

TP311

HAN Liping, YU Le, HU Mingzhe, NIE Tingting. Challenges and Methods for Robust Cyber-Physical Systems Under Uncertainty:A Systematic Review[J].Computer Science, 2026, 53(6A): 250700017-8.

References

[1] ZHANG M,ALI S,YUE T,et al.Uncertainty-wise cyber-physi-cal system test modeling[J].Software & Systems Modeling,2019,18(2):1379-1418.
[2] LI R F,XIE Y,LI R,et al.Survey of Cyber-Physical Systems [J].Journal of Computer Research and Development,2012,49(6):1149-1161.
[3] SAMPIGETHAYA K,POOVENDRAN R.Cyber-physical integration in future aviation information systems[C]//Proceedings of 2012 IEEE/AIAA 31st Digital Avionics Systems Conference(DASC).IEEE,2012:7C2-1-7C2-12.
[4] ZHANG L.Convergence of physical system and cyber system modeling methods for aviation cyber physical control system[C]//Proceedings of 2014 IEEE International Conference on Information and Automation(ICIA).IEEE,2014:542-547.
[5] ALSULAMI A A,ZEIN-SABATTO S.Resilient cyber-security approach for aviation cyber-physical systems protection against sensor spoofing attacks[C]//Proceedings of 2021 IEEE 11th Annual Computing and Communication Workshop and Confe-rence(CCWC).IEEE,2021:0565-0571.
[6] LEE I,SOKOLSKY O,CHEN S,et al.Challenges and research directions in medical cyber-physical systems [J].Proceedings of the IEEE,2011,100(1):75-90.
[7] SANKAVARAM C,KODALI A,PATTIPATI K.An integratedhealth management process for automotive cyber-physical systems[C]//Proceedings of 2013 International Conference on Computing,Networking and Communications(ICNC).IEEE,2013:82-86.
[8] BANERJEE A,GUPTA S K.Spatio-temporal hybrid automatafor safe cyber-physical systems:A medical case study[C]//Proceedings of Proceedings of the ACM/IEEE 4^th International Conference on Cyber-Physical Systems.IEEE,2013:71-80.
[9] PLAZA A M,DIÍAZ J,PEÉREZ J.Software architectures forhealth care cyber-physical systems:A systematic literature review [J].Journal of Software:Evolution and Process,2018,30(7):e1930.
[10] XIE Y,ZENG G,KURACHI R,et al.Optimizing extensibility of CAN FD for automotive cyber-physical systems [J].IEEE Transactions on Intelligent Transportation Systems,2021,22(12):7875-7886.
[11] XIE Y,ZENG G,KURACHI R,et al.Timing analysis of CAN FD for security-aware automotive cyber-physical systems [J].IEEE Transactions on Dependable and Secure Computing,2022,20(4):3064-3078.
[12] HUSSAIN M M,BEG M S.Using vehicles as fog infrastruc-tures for transportation cyber-physical systems(T-CPS):Fog computing for vehicular networks [J].International Journal of Software Science and Computational Intelligence(IJSSCI),2019,11(1):47-69.
[13] GARTZIANDIA A,AYERDI J,ARRIETA A,et al.Microser-vices for Continuous Deployment,Monitoring and Validation in Cyber-Physical Systems:an Industrial Case Study for Elevators Systems[C]//Proceedings of 2021 IEEE 18th International Conference on Software Architecture Companion(ICSA-C).IEEE,2021:46-53.
[14] FAMELIS M,SANTOSA S.MAV-Vis:a notation for model uncertainty[C]//Proceedings of 2013 5th International Workshop on Modeling in Software Engineering(MiSE). IEEE,2013:7-12.
[15] O'KANE S.NIO EV gets stuck on highway after driver triggers over-the-air update[EB/OL].(2019-01-31) [2025-07-02] .https://www.theverge.com/2019/1/31/18205774/nio-ota-update-traffic-china-es8.
[16] LIU Z,CAI Y F,WANG H,et al.Robust target recognition and tracking of self-driving cars with radar and camera information fusion under severe weather conditions[J].IEEE Transactions on Intelligent Transportation Systems,2021,23(7):6640-6653.
[17] CHEN W N,ZHU L,LOO S Y,et al.Robustness improvement of using pre-trained network in visual odometry for on-road driving[J].IEEE Transactions on Vehicular Technology,2021,70(12):12415-12426.
[18] CELENTANO L,SANTINI S,PETRILLO A.Robust Tracking Controller Design for Uncertain Nonlinear Self-Driving Cars[C]//2019 18th European Control Conference(ECC).IEEE,2019:947-954.
[19] DONG Z J,ZHU D L,MENG M Q H.An autonomous elevatorbutton recognition system based on convolutional neural networks[C]//2017 IEEE International Conference on Robotics and Biomimetics(ROBIO).IEEE,2017:2533-2539.
[20] SORSA J,EHTAMO H,KUUSINEN J M,et al.Modeling uncertain passenger arrivals in the elevator dispatching problem with destination control [J].Optimization Letters,2018,12:171-185.
[21] WOLSZCZAK P,LONKWIC P,LITAK G,et al.Robust optimization and uncertainty quantification in the nonlinear mechanics of an elevator brake system[J].Meccanica,2019,54:1057-1069.
[22] BLANCO-ORTEGA A,GÓMEZ-BECERRA F A,MAGADÁN-SALAZAR A,et al.Algebraic Identification and Robust GPI Controller in Elevator System[C]// 2020 International Confe-rence on Mechatronics,Electronics and Automotive Engineering(ICMEAE).IEEE,2020:147-155.
[23] SHISHEBORI D,YOUSEFI BABADI A.Robust and reliablemedical services network design under uncertain environment and system disruptions [J].Transportation Research Part E:Logistics and Transportation Review,2015,77:268-288.
[24] ZARRINPOOR N,FALLAHNEZHAD M S,PISHVAEE M S.Design of a reliable hierarchical location-allocation model under disruptions for health service networks:A two-stage robust approach [J].Computers & Industrial Engineering,2017,109:130-150.
[25] HAWKINS-DAARUD A,JOHNSTON S K,SWANSON K R.Quantifying uncertainty and robustness in a biomathematical model-based patient-specific response metric for glioblastoma [J].JCO Clinical Cancer Informatics,2019,3:1-8.
[26] ZHANG M,ALI S,YUE T.Uncertainty-wise test case generation and minimization for cyber-physical systems [J].Journal of Systems and Software,2019,153:1-21.
[27] CAMILLI M,BELLETTINI C,GARGANTINI A,et al.Online model-based testing under uncertainty[C]//2018 IEEE 29th International Symposium on Software Reliability Engineering(ISSRE).IEEE,2018:36-46.
[28] ALI S,LU H,WANG S,et al.Uncertainty-wise testing of cyber-physical systems[C]//Advances in Computers.Elsevier,2017:23-94.
[29] MENGHI C,NEJATI S,GAALOUL K,et al.Generating automated and online test oracles for simulink models with continuous and uncertain behaviors[C]//Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Confe-rence and Symposium on the Foundations of Software Engineering.ACM,2019:27-38.
[30] WALKINSHAW N,FRASER G.Uncertainty-driven black-box test data generation[C]//2017 IEEE International Conference on Software Testing,Verification and Validation(ICST).IEEE,2017:253-263.
[31] HAQ F U,SHIN D,BRIAND L.Efficient online testing forDNN-enabled systems using surrogate-assisted and many-objective optimization[C]//Proceedings of the 44th International Conference on Software Engineering.IEEE,2022:811-822.
[32] MA T,ALI S,YUE T,et al.Testing self-healing cyber-physical systems under uncertainty:a fragility-oriented approach[J].Software Quality Journal,2019,27:615-649.
[33] HUTCHISON C,ZIZYTE M,LANIGAN P E,et al.Robustness testing of autonomy software[C]//Proceedings of the 40th International Conference on Software Engineering:Software Engineering in Practice.Gothenburg,Sweden,2018:276-285.
[34] ZHOU X,KOUZEL M,ALEMZADEH H.Robustness testingof data and knowledge driven anomaly detection in cyber-physical systems[C]//2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops(DSN-W).IEEE,2022:44-51.
[35] SHEIKHI S,KIM E,DUGGIRALA P S,et al.Coverage-guided fuzz testing for cyber-physical systems[C]//2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems(ICCPS).Milano,Italy:IEEE,2022:24-33.
[36] SOMERS R J,DOUTHWAITE J A,WAGG D,et al.Digital-twin-based testing for cyber-physical systems:A systematic li-terature review [J].Information and Software Technology,2023,156:107145.
[37] GARTZIANDIA A,ARRIETA A,AYERDI J,et al.Machinelearning-based test oracles for performance testing of cyber-physical systems:An industrial case study on elevators dispatching algorithms [J].Journal of Software:Evolution and Process,2022,34(11):e2465.
[38] CASTIGLIONI V,LORETI M,TINI S.STARK:a tool for the analysis of CPSs robustness [J].Science of Computer Programming,2024,236:103134.
[39] CASTIGLIONI V,LORETI M,TINI S.RobTL:robustnesstemporal logic for CPS[C]//35th International Conference on Concurrency Theory(CONCUR 2024).Schloss Dagstuhl-Leibniz-Zentrum für Informatik,2024:15:1-15:23.
[40] CHEN L J,WANG B.Robustness assessment of weakly coupled cyber-physical power systems under multi-stage attacks [J].Electric Power Systems Research,2024,231:110325.
[41] SCHMIDT M,PLAMBECK S,FEY G.Assessing Robustness in Data-Driven Modeling of Cyber-Physical Systems[C]//Machine Learning for Cyber Physical Systems.Berlin,Germany,2025:1-11.
[42] TU H C,XIA Y X,WU J J,et al.Robustness assessment of cyber-physical systems with weak interdependency [J].Physica A:Statistical Mechanics and its Applications,2019,522:9-17.
[43] HAN L,YUE T,ALI S,et al.Are elevator software robust against uncertainties? results and experiences from an industrial case study[C]//Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering.ACM,2022:1331-1342.
[44] HAN L,ALI S,YUE T,et al.Uncertainty-aware robustness assessment of industrial elevator systems [J].ACM Transactions on Software Engineering and Methodology,2023,32(4):1-51.
[45] GAO X,LI X,YANG X.Robustness assessment of the cyber-physical system against cascading failure in a virtual power plant based on complex network theory [J].International Transactions on Electrical Energy Systems,2021,31(11):e13039.
[46] STOCKMANN C,WINKLER H,KUNATH M.Robustness assessment in production systems [J].Journal of Manufacturing Technology Management,2021,32(4):932-951.
[47] CHEN L,HU F,WANG S,et al.Cyber-physical system fusion modeling and robustness evaluation [J].Electric Power Systems Research,2022,213:108654.
[48] PALFRAHAM M C,FRY J M,FARHOOD M.Robustnessanalysis of flight controllers for fixed-wing unmanned aircraft systems using integral quadratic constraints [J].IEEE Transactions on Control Systems Technology,2019,27(1):86-102.
[49] JOHNSON M,SMITH J,BROWN E,et al.Evaluating Robustness in Adaptive UAV Navigation Systems under Uncertainty[EB/OL].(2024-10) [2024-11-20] .https://www.researchgate.net/publication/385092218_Evaluating_Robustness_in_Adaptive_UAV_Navigation_Systems_under_Uncertainty.
[50] WANG X H,ZHANG Y,WANG L Z,et al.Robustness evaluation method for unmanned aerial vehicle swarms based on complex network theory [J].Chinese Journal of Aeronautics,2020,33(1):352-364.
[51] LI H,SUN Q,WANG M,et al.A baseline-resilience assessment method for UAV swarms under heterogeneous communication networks [J].IEEE Systems Journal,2022,16(4):6107-6118.
[52] ELYOUSSEPH H,ALTAMIMI M.Robustness of deep-lear-ning-based RF UAV detectors [J].Sensors,2024,24(22):7339.
[53] BILLAH M A,FARUQUE I A.Robustness in bio-inspired visually guided multi-agent flight and the gain modulation hypothesis [J].International Journal of Robust and Nonlinear Control,2023,33(2):1316-1334.
[54] BANERJEE A,CHOPPELLA V.AKnowledge-Driven Ap-proach for Dynamic Reconfiguration of Control Design in Internet of Things and Cyber-Physical Systems [J].IEEE Internet of Things Journal,2024,12(5):5615-5641.
[55] TOMIYAMA T,MOYEN F.Resilient architecture for cyber-physical production systems [J].CIRP Annals,2018,67(1):161-164.
[56] MATEI I,PIOTROWSKI W,PEREZ A,et al.System resilience through health monitoring and reconfiguration [J].ACM Transactions on Cyber-Physical Systems,2024,8(1):1-27.
[57] MIHALACHE S F,PRICOP E,FATTAHI J.Resilience en-hancement of cyber-physical systems:A review[C]//Power Systems Resilience.Cham:Springer,2019:269-287.
[58] PIARDI L,LEITÃO P,DE OLIVEIRA A S.Fault-tolerance in cyber-physical systems:literature review and challenges[C]//2020 IEEE 18th International Conference on Industrial Informatics(INDIN).IEEE,2020:29-34.
[59] WANG X F,HOVAKIMYAN N,SHA L.RSimplex:A robust control architecture for cyber and physical failures [J].ACM Transactions on Cyber-Physical Systems,2018,2(4):1-26.
[60] SOOD S,MALIK A,ROOP P.Robust design and validation of cyber-physical systems [J].ACM Transactions on Embedded Computing Systems(TECS),2019,18(6):1-21.
[61] AYU W S,RUSMIN P H,HIDAYAT E M I.Robust PID control design in CPS-based batch distillation column[C]//2019 6th International Conference on Electrical Engineering,Computer Science and Informatics(EECSI).IEEE,2019:95-100.
[62] YU R R,ZHAO X,LU S,et al.Intelligent Game-Theoretic Approach for Resilient Robust Control Design of Cyber-Physical Systems:Application to Intelligent Transportation Systems [J].IEEE Transactions on Intelligent Transportation Systems,2024,25(11):16072-16083.
[63] YU R R,LIU M X,ZHU W Y,et al.Robust control design for nonlinear cyber-physical systems:application to the U-turn in autonomous vehicles [J].Nonlinear Dynamics,2025,113(7):6997-7011.
[64] WANG X F,HOVAKIMYAN N,SHA L.RSimplex:A robust control architecture for cyber and physical failures [J].ACM Transactions on Cyber-Physical Systems,2018,2(4):1-26.
[65] PARNIANIFARD A,ZEMOUCHE A,CHANCHAROEN R,et al.Robust optimal design of FOPID controller for five bar linkage robot in a Cyber-Physical System:A new simulation-optimization approach [J].PLoS One,2020,15(11):e0242613.
[66] FANG Z H,MO H D,WANG Y,et al.Performance and reliability improvement of cyber-physical systems subject to degraded communication networks through robust optimization [J].Computers & Industrial Engineering,2017,114:166-174.
[67] DAI Y F,LI M Y,ZHANG K W,et al.Robust and resilient distributed MPC for cyber-physical systems against DoS attacks[J].IEEE Transactions on Industrial Cyber-Physical Systems,2023,1:44-55.
[68] XU L,GUO Q L,YANG T Y,et al.Robust routing optimization for smart grids considering cyber-physical interdependence[J].IEEE Transactions on Smart Grid,2019,10(5):5620-5629.
[69] ZHU Y S,LIU J Z,HU Y,et al.Distributionally robust optimization model considering deep peak shaving and uncertainty of renewable energy[J].Energy,2024,288:129935.
[70] ZHANG S G,ZHOU J S,TIAN D X,et al.Robust cooperative communication optimization for multi-UAV-aided vehicular networks[J].IEEE Wireless Communications Letters,2021,10(4):780-784.
[71] CHENG H Z,LI Z L,DUAN P H,et al.Robust optimization and uncertainty quantification of a micro axial compressor for unmanned aerial vehicles [J].Applied Energy,2023,352:121972.
[72] XU D F,SUN Y,SCHOBER R,et al.Multiuser MISO UAV communications in uncertain environments with no-fly zones:Robust trajectory and resource allocation design [J].IEEE Transactions on Communications,2020,68(5):3153-3172.
[73] WANG C Y,AOUF N.Explainable deep adversarial reinforcement learning approach for robust autonomous driving [J].IEEE Transactions on Intelligent Vehicles,2024,10(4):2551-2563.
[74] HE X K,HUANG W H,CHEN L.Trustworthy autonomousdriving via defense-aware robust reinforcement learning against worst-case observational perturbations[J].Transportation Research Part C:Emerging Technologies,2024,163:104632.
[75] SHARIF A,MARIJAN D.Adversarial deep reinforcementlearning for improving the robustness of multi-agent autonomous driving policies[C]//2022 29th Asia-Pacific Software Engineering Conference(APSEC).IEEE,2022.
[76] CHEN W N,ZHU L,LOO S Y,et al.Robustness improvement of using pre-trained network in visual odometry for on-road driving [J].IEEE Transactions on Vehicular Technology,2021,70(12):12415-12426.
[77] WANG M H,CHONG K,LIN Z Y,et al.An explainable artificial intelligence-based robustness optimization approach for age-related macular degeneration detection based on medical IOT systems [J].Electronics,2023,12(12):2697.
[78] AZIZI S A,SOLEIMANI R,AHMADI M,et al.Performance enhancement of an uncertain nonlinear medical robot with optimal nonlinear robust controller [J].Computers in Biology and Medicine,2022,146:105567.
[79] MAGNANINI M C,TERKAJ W,TOLIO T.Robust optimization of manufacturing systems flexibility [J].Procedia CIRP,2021,96:63-68.
[80] GOLI A,ZARE H K,SADEGHIEH A,et al.Hybrid artificial intelligence and robust optimization for a multi-objective product portfolio problem Case study:The dairy products industry [J].Computers & Industrial Engineering,2019,137:106090.

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