Moving horizon estimation meets multi-sensor information fusion: Development, opportunities and challenges

Abstract Since the proposal of moving horizon (MH) estimation in 1960s, the MH estimation approach has drawn ever-increasing research interests due mainly to its inherent capability of handling complex nonlinear systems and constrained systems. Recent years have witnessed considerable progress on the theoretical and practical research of MH estimation. In this work, a bibliographical review is provided on the moving horizon estimation problem and its applications. The basic idea of MH estimation is first introduced in detail. Then recent advances of MH estimation according to the underlying systems are summarized. Furthermore, some applications of MH estimation are presented. Finally, some research challenges of MH estimation problem are outlined for the further research.

[1]  Tengpeng Chen,et al.  Robust state estimation for power systems via moving horizon strategy , 2017 .

[2]  Dongpu Cao,et al.  Condition Monitoring in Advanced Battery Management Systems: Moving Horizon Estimation Using a Reduced Electrochemical Model , 2018, IEEE/ASME Transactions on Mechatronics.

[3]  Jianhua Lu,et al.  Decentralized state estimation for networked spatial-navigation systems with mixed time-delays and quantized complementary measurements: The moving horizon case , 2018 .

[4]  Antonios Armaou,et al.  Output feedback receding horizon regulation via moving horizon estimation and model predictive control , 2018, Journal of Process Control.

[5]  Lei Zou,et al.  Set-membership filtering for time-varying systems with mixed time-delays under Round-Robin and Weighted Try-Once-Discard protocols , 2016, Autom..

[6]  Fuad E. Alsaadi,et al.  Fusion estimation for multi-rate linear repetitive processes under weighted try-once-discard protocol , 2020, Inf. Fusion.

[7]  Moritz Diehl,et al.  Robust Stability of Moving Horizon Estimation Under Bounded Disturbances , 2016, IEEE Transactions on Automatic Control.

[8]  Yun Chen,et al.  Distributed $H_\infty$ Filtering for Switched Stochastic Delayed Systems Over Sensor Networks With Fading Measurements , 2020, IEEE Transactions on Cybernetics.

[9]  Tor Arne Johansen,et al.  Linear Moving Horizon Estimation With Pre-Estimating Observer , 2010, IEEE Transactions on Automatic Control.

[10]  Lei Zou,et al.  Moving Horizon Estimation for Networked Time-Delay Systems Under Round-Robin Protocol , 2019, IEEE Transactions on Automatic Control.

[11]  Biao Huang,et al.  A moving horizon approach to a noncontinuum state estimation , 2013, Int. J. Control.

[12]  Lei Zou,et al.  Recursive Filtering for Time-Varying Systems With Random Access Protocol , 2019, IEEE Transactions on Automatic Control.

[13]  Jinfeng Liu,et al.  Distributed moving horizon state estimation for nonlinear systems with bounded uncertainties , 2013 .

[14]  Bernt S. Aadnøy,et al.  Rate of Penetration Optimization using Moving Horizon Estimation , 2016 .

[15]  Xiangyu Zhang,et al.  MEMS gyroscope/TAM-integrated attitude estimation based on moving horizon estimation , 2017 .

[16]  Torbjörn Thiringer,et al.  Motion Sensorless IPMSM Control Using Linear Moving Horizon Estimation With Luenberger Observer State Feedback , 2018, IEEE Transactions on Transportation Electrification.

[17]  Lei Guo,et al.  Distributed quantized multi-modal H∞ fusion filtering for two-time-scale systems , 2017, Inf. Sci..

[18]  Zidong Wang,et al.  Observer-Based Consensus Control for Discrete-Time Multiagent Systems With Coding–Decoding Communication Protocol , 2019, IEEE Transactions on Cybernetics.

[19]  H. Sorenson.  Least-squares estimation: from Gauss to Kalman , 1970, IEEE Spectrum.

[20]  D. Mayne,et al.  MOVING HORIZON OBSERVERS , 1992 .

[21]  Qing-Long Han,et al.  Optimal Communication Network-Based $H_\infty $ Quantized Control With Packet Dropouts for a Class of Discrete-Time Neural Networks With Distributed Time Delay , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[22]  Zidong Wang,et al.  Noncooperative Event-Triggered Control Strategy Design With Round-Robin Protocol: Applications to Load Frequency Control of Circuit Systems , 2020, IEEE Transactions on Industrial Electronics.

[23]  X. Rong Li,et al.  Robust linear estimation fusion with allowable unknown cross-covariance , 2014, 17th International Conference on Information Fusion (FUSION).

[24]  Eric C. Kerrigan,et al.  Noise covariance identification for nonlinear systems using expectation maximization and moving horizon estimation , 2017, Autom..

[25]  Qing-Long Han,et al.  Finite-Time $H_{\infty}$ State Estimation for Discrete Time-Delayed Genetic Regulatory Networks Under Stochastic Communication Protocols , 2018, IEEE Transactions on Circuits and Systems I: Regular Papers.

[26]  Fuad E. Alsaadi,et al.  Event-based recursive filtering for time-delayed stochastic nonlinear systems with missing measurements , 2017, Signal Process..

[27]  Li Yu,et al.  Moving horizon estimation for networked systems with multiple packet dropouts , 2012 .

[28]  Yang Liu,et al.  Layout optimization of large-scale oil-gas gathering system based on combined optimization strategy , 2019, Neurocomputing.

[29]  Manfred Morari,et al.  Application of moving horizon estimation based fault detection to cold tandem steel mill , 2000 .

[30]  Jinfeng Liu.  Moving horizon state estimation for nonlinear systems with bounded uncertainties , 2013 .

[31]  Johannes P. Schlöder,et al.  A real-time algorithm for moving horizon state and parameter estimation , 2011, Comput. Chem. Eng..

[32]  Dong Wang,et al.  Finite-horizon filtering for a class of nonlinear time-delayed systems with an energy harvesting sensor , 2019, Autom..

[33]  Zhidong Li,et al.  Multi-objective optimization of energy consumption in crude oil pipeline transportation system operation based on exergy loss analysis , 2019, Neurocomputing.

[34]  Angelo Alessandri,et al.  Moving-horizon estimation with guaranteed robustness for discrete-time linear systems and measurements subject to outliers , 2016, Autom..

[35]  Aleksandar Haber,et al.  Moving Horizon Estimation for Large-Scale Interconnected Systems , 2013, IEEE Transactions on Automatic Control.

[36]  Guoqiang Hu,et al.  Distributed Robust Fusion Estimation With Application to State Monitoring Systems , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[37]  Jan Swevers,et al.  Spacecraft Attitude Estimation and Sensor Calibration Using Moving Horizon Estimation , 2013 .

[38]  Jian Yang,et al.  Moving Horizon Estimation for Cooperative Localisation with Communication Delay , 2015 .

[39]  Tyrone L. Vincent,et al.  Multiple window moving horizon estimation , 2014, Autom..

[40]  Giorgio Battistelli,et al.  Receding-horizon estimation for discrete-time linear systems , 2003, IEEE Trans. Autom. Control..

[41]  Lei Zou,et al.  Ultimate Boundedness Control for Networked Systems With Try-Once-Discard Protocol and Uniform Quantization Effects , 2017, IEEE Transactions on Automatic Control.

[42]  Fuad E. Alsaadi,et al.  Nonfragile $H_{\infty}$ Fuzzy Filtering With Randomly Occurring Gain Variations and Channel Fadings , 2016, IEEE Transactions on Fuzzy Systems.

[43]  Yurong Liu,et al.  Distributed filtering for nonlinear time‐delay systems over sensor networks subject to multiplicative link noises and switching topology , 2019, International Journal of Robust and Nonlinear Control.

[44]  Fuad E. Alsaadi,et al.  Partial-Nodes-Based State Estimation for Complex Networks With Unbounded Distributed Delays , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[45]  Yi-Jun He,et al.  Accurate State of Charge Estimation With Model Mismatch for Li-Ion Batteries: A Joint Moving Horizon Estimation Approach , 2019, IEEE Transactions on Power Electronics.

[46]  Lei Zou,et al.  On ${\mathcal H}_{\infty }$ Finite-Horizon Filtering Under Stochastic Protocol: Dealing With High-Rate Communication Networks , 2017, IEEE Transactions on Automatic Control.

[47]  Giorgio Battistelli,et al.  On estimation error bounds for receding-horizon filters using quadratic boundedness , 2004, IEEE Transactions on Automatic Control.

[48]  Fuad E. Alsaadi,et al.  A Partial-Nodes-Based Information fusion approach to state estimation for discrete-Time delayed stochastic complex networks , 2019, Inf. Fusion.

[49]  Shaoyuan Li,et al.  Receding horizon estimation to networked control systems with multirate scheme , 2009, Science in China Series F: Information Sciences.

[50]  Li Sheng,et al.  Reliable Data Fusion of Hierarchical Wireless Sensor Networks With Asynchronous Measurement for Greenhouse Monitoring , 2019, IEEE Transactions on Control Systems Technology.

[51]  Giorgio Battistelli,et al.  Robust receding-horizon state estimation for uncertain discrete-time linear systems , 2005, Syst. Control. Lett..

[52]  Boris Rohaľ-Ilkiv,et al.  Real-time moving horizon estimation for a vibrating active cantilever , 2017 .

[53]  Fei Liu,et al.  Constrained state estimation for stochastic jump systems: moving horizon approach , 2017, Int. J. Syst. Sci..

[54]  Jun-Ho Oh,et al.  Humanoid state estimation using a moving horizon estimator , 2017, Adv. Robotics.

[55]  Qing-Long Han,et al.  Distributed Event-Triggered Estimation Over Sensor Networks: A Survey , 2020, IEEE Transactions on Cybernetics.

[56]  Randal W. Beard,et al.  Parameter estimation for towed cable systems using moving horizon estimation , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[57]  Mohamed Darouach,et al.  Moving horizon state estimation for linear discrete-time singular systems , 2010 .

[58]  Efstratios N. Pistikopoulos,et al.  Simultaneous design of explicit/multi-parametric constrained moving horizon estimation and robust model predictive control , 2013, Comput. Chem. Eng..

[59]  G. Ferrari-Trecate,et al.  Distributed Moving Horizon Estimation for Nonlinear Constrained Systems , 2010 .

[60]  L. Biegler,et al.  A fast moving horizon estimation algorithm based on nonlinear programming sensitivity , 2008 .

[61]  L Giovanini,et al.  Adaptive arrival cost update for improving Moving Horizon Estimation performance. , 2017, ISA transactions.

[62]  Marcello Farina,et al.  Moving horizon estimation for distributed nonlinear systems with application to cascade river reaches , 2011 .

[63]  Qing-Long Han,et al.  Networked control systems: a survey of trends and techniques , 2020, IEEE/CAA Journal of Automatica Sinica.

[64]  Zidong Wang,et al.  A Constrained Optimization Approach to Dynamic State Estimation for Power Systems Including PMU and Missing Measurements , 2013, IEEE Transactions on Control Systems Technology.

[65]  Zidong Wang,et al.  H∞ state estimation with fading measurements, randomly varying nonlinearities and probabilistic distributed delays , 2015 .

[66]  Ling Chen,et al.  An optimization based Moving Horizon Estimation with application to localization of Autonomous Underwater Vehicles , 2014, Robotics Auton. Syst..

[67]  Lei Guo,et al.  An information aware event-triggered scheme for particle filter based remote state estimation , 2019, Autom..

[68]  Graham C. Goodwin,et al.  Moving horizon Monte Carlo state estimation for linear systems with output quantization , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[69]  Le Yin,et al.  Moving horizon estimation for ARMAX processes with additive output noise , 2019, J. Frankl. Inst..

[70]  Johannes P. Schlöder,et al.  Online identification of adsorption isotherms in SMB processes via efficient moving horizon state and parameter estimation , 2010, Comput. Chem. Eng..

[71]  Salah Sukkarieh,et al.  Metamorphic Moving Horizon Estimation , 2018, Autom..

[72]  Giorgio Battistelli,et al.  Min-Max Moving-Horizon Estimation for Uncertain Discrete-Time Linear Systems , 2012, SIAM J. Control. Optim..

[73]  Zidong Wang,et al.  $H_{\infty}$ State Estimation for Discrete-Time Nonlinear Singularly Perturbed Complex Networks Under the Round-Robin Protocol , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[74]  Qing-Long Han,et al.  Envelope-constrained H∞ filtering for nonlinear systems with quantization effects: The finite horizon case , 2018, Autom..

[75]  Graham C. Goodwin,et al.  A combined MAP and Bayesian scheme for finite data and/or moving horizon estimation , 2014, Autom..

[76]  Giorgio Battistelli,et al.  Moving-horizon state estimation for nonlinear discrete-time systems: New stability results and approximation schemes , 2008, Autom..

[77]  Jicheng Liu,et al.  Moving horizon estimation for multirate systems with time-varying time-delays , 2019, J. Frankl. Inst..

[78]  Hongli Dong,et al.  A survey on set-membership filtering for networked control systems under communication protocols , 2018 .

[79]  David Q. Mayne,et al.  Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations , 2003, IEEE Trans. Autom. Control..

[80]  Yurong Liu,et al.  Event-Triggered Partial-Nodes-Based State Estimation for Delayed Complex Networks With Bounded Distributed Delays , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[81]  Joao P. Hespanha,et al.  Simultaneous model predictive control and moving horizon estimation for blood glucose regulation in Type 1 diabetes , 2018 .

[82]  José A. De Doná,et al.  Moving Horizon Estimation of Constrained Nonlinear Systems by Carleman Approximations , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[83]  Yi-Jun He,et al.  Online state of charge estimation of lithium-ion batteries: A moving horizon estimation approach , 2016 .

[84]  Xiaoliang Feng,et al.  Sequential fusion H ∞ filtering for multi-rate multi-sensor time-varying systems – a Krein-space approach , 2017 .

[85]  Fei Liu,et al.  Moving horizon estimation for Markov jump systems , 2016, Inf. Sci..

[86]  Victor M. Zavala,et al.  Inference of building occupancy signals using moving horizon estimation and Fourier regularization , 2014 .

[87]  Gibson,et al.  A steady-state optimal control problem , 1976 .

[88]  Morten Hovd,et al.  Robust output feedback time optimal decomposed controllers for linear systems via moving horizon estimation , 2010 .

[89]  Quanmin Zhu,et al.  Moving Horizon State Estimation for Networked Control Systems With Multiple Packet Dropouts , 2012, IEEE Transactions on Automatic Control.

[90]  Jinfeng Liu,et al.  Lyapunov-based MPC with robust moving horizon estimation and its triggered implementation , 2013 .

[91]  Matthias Albrecht Müller.  Nonlinear moving horizon estimation in the presence of bounded disturbances , 2017, Autom..

[92]  S. Ungarala.  Computing arrival cost parameters in moving horizon estimation using sampling based filters , 2009 .

[93]  Yuanqing Xia,et al.  Optimal sequential and distributed fusion for state estimation in cross-correlated noise , 2013, Autom..

[94]  Tor Arne Johansen,et al.  Adaptive Model Estimation of Vibration Motion for a Nanopositioner With Moving Horizon Optimized Extended Kalman Filter , 2013 .

[95]  Shu-Li Sun,et al.  Multi-sensor optimal information fusion Kalman filter , 2004, Autom..

[96]  Xunyuan Yin,et al.  Distributed moving horizon state estimation of two-time-scale nonlinear systems , 2017, Autom..

[97]  Giorgio Battistelli,et al.  Moving horizon estimation for discrete-time linear systems with binary sensors: Algorithms and stability results , 2016, Autom..

[98]  A. Jazwinski.  Limited memory optimal filtering , 1968 .

[99]  Jing Zeng,et al.  Distributed moving horizon state estimation: Simultaneously handling communication delays and data losses , 2015, Syst. Control. Lett..

[100]  Jing Zhang,et al.  Robust moving horizon estimation based output feedback economic model predictive control , 2014, Syst. Control. Lett..

[101]  Efstratios N. Pistikopoulos,et al.  Moving horizon estimation: Error dynamics and bounding error sets for robust control , 2013, Autom..

[102]  Wen-an Zhang,et al.  Distributed Finite-Horizon Fusion Kalman Filtering for Bandwidth and Energy Constrained Wireless Sensor Networks , 2014, IEEE Transactions on Signal Processing.

[103]  Fuad E. Alsaadi,et al.  Variance-constrained H∞ state estimation for time-varying multi-rate systems with redundant channels: The finite-horizon case , 2019, Inf. Sci..

[104]  Giorgio Battistelli,et al.  Moving-horizon state estimation for nonlinear systems using neural networks , 2008, 2008 47th IEEE Conference on Decision and Control.

[105]  Raj Jena,et al.  Optimal dosing of cancer chemotherapy using model predictive control and moving horizon state/parameter estimation , 2012, Comput. Methods Programs Biomed..

[106]  G. Ferrari-Trecate,et al.  Distributed moving horizon estimation for nonlinear constrained systems , 2010 .

[107]  Han Chunyan,et al.  Optimal filtering for networked systems with Markovian communication delays , 2012, Proceedings of the 31st Chinese Control Conference.

[108]  Angelo Alessandri,et al.  Fast Moving Horizon State Estimation for Discrete-Time Systems Using Single and Multi Iteration Descent Methods , 2017, IEEE Transactions on Automatic Control.

[109]  Jing Zhang,et al.  Two triggered information transmission algorithms for distributed moving horizon state estimation , 2014, Syst. Control. Lett..

[110]  Manfred Morari,et al.  Moving horizon estimation for hybrid systems , 2002, IEEE Trans. Autom. Control..

[111]  Qing-Long Han,et al.  Event-Based Variance-Constrained ${\mathcal {H}}_{\infty }$ Filtering for Stochastic Parameter Systems Over Sensor Networks With Successive Missing Measurements , 2018, IEEE Transactions on Cybernetics.

[112]  Taous-Meriem Laleg-Kirati,et al.  Moving-Horizon Modulating Functions-Based Algorithm for Online Source Estimation in a First-Order Hyperbolic Partial Differential Equation , 2017 .

[113]  René Schneider,et al.  A Solution for the Partitioning Problem in Partition-Based Moving-Horizon Estimation , 2017, IEEE Transactions on Automatic Control.

[114]  Qing-Long Han,et al.  A Dynamic Event-Triggered Transmission Scheme for Distributed Set-Membership Estimation Over Wireless Sensor Networks , 2019, IEEE Transactions on Cybernetics.

[115]  Moritz Diehl,et al.  Convergence Guarantees for Moving Horizon Estimation Based on the Real-Time Iteration Scheme , 2014, IEEE Transactions on Automatic Control.

[116]  Marcello Farina,et al.  Moving-horizon partition-based state estimation of large-scale systems , 2024, Autom..

[117]  Douglas A. Allan,et al.  Moving Horizon Estimation , 2018, Handbook of Model Predictive Control.

[118]  Qi Li,et al.  Recursive distributed filtering over sensor networks on Gilbert-Elliott channels: A dynamic event-triggered approach , 2020, Autom..

[119]  Quan Pan,et al.  Multi-rate stochastic H∞ filtering for networked multi-sensor fusion , 2010, Autom..

[120]  Frank Allgöwer,et al.  Enhancing Output-Feedback MPC With Set-Valued Moving Horizon Estimation , 2018, IEEE Transactions on Automatic Control.

[121]  Shuai Liu,et al.  On quantized H∞ filtering for multi-rate systems under stochastic communication protocols: The finite-horizon case , 2018, Inf. Sci..

[122]  Le Yin,et al.  Regularised estimation for ARMAX process with measurements subject to outliers , 2018 .

[123]  Yugang Niu,et al.  Networked predictive control of constrained linear systems with input quantisation , 2013, Int. J. Syst. Sci..

[124]  Qing-Long Han,et al.  A Survey on Model-Based Distributed Control and Filtering for Industrial Cyber-Physical Systems , 2019, IEEE Transactions on Industrial Informatics.

[125]  Juergen Hahn,et al.  Computation of arrival cost for moving horizon estimation via unscented Kalman filtering , 2009 .

[126]  Qing-Long Han,et al.  A Recursive Approach to Quantized ${H_{\infty}}$ State Estimation for Genetic Regulatory Networks Under Stochastic Communication Protocols , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[127]  Yang Xiang,et al.  A survey on security control and attack detection for industrial cyber-physical systems , 2018, Neurocomputing.

[128]  Boris Lohmann,et al.  Estimation of process parameters on a moving horizon for a class of distributed parameter systems , 2010 .

[129]  Lorenzo Fagiano,et al.  A combined Moving Horizon and Direct Virtual Sensor approach for constrained nonlinear estimation , 2013, Autom..

[130]  Wen-an Zhang,et al.  Moving Horizon Estimation for Networked Systems With Quantized Measurements and Packet Dropouts , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.

[131]  Wen-an Zhang,et al.  Distributed H∞ fusion filtering with communication bandwidth constraints , 2014, Signal Process..

[132]  Raquel Caballero-Águila,et al.  Optimal state estimation for networked systems with random parameter matrices, correlated noises and delayed measurements , 2015, Int. J. Gen. Syst..

[133]  Jianhua Lu,et al.  Decentralised moving-horizon state estimation for a class of networked spatial-navigation systems with random parametric uncertainties and communication link failures , 2015 .

[134]  Wen-an Zhang,et al.  Moving Horizon SINR Estimation for Wireless Networked Systems , 2014, IEEE Transactions on Industrial Informatics.

[135]  Wim Desmet,et al.  Exploiting input sparsity for joint state/input moving horizon estimation , 2018 .

[136]  Na Li,et al.  Multi-sensor information fusion estimators for stochastic uncertain systems with correlated noises , 2016, Inf. Fusion.

[137]  Eric Bullinger,et al.  Simple and efficient moving horizon estimation based on the fast gradient method , 2015 .

[138]  Jay H. Lee,et al.  Constrained linear state estimation - a moving horizon approach , 2001, Autom..

[139]  Huisheng Shu,et al.  Finite-time resilient H∞ state estimation for discrete-time delayed neural networks under dynamic event-triggered mechanism , 2020, Neural Networks.

[140]  Richard D. Braatz,et al.  Fast moving horizon estimation for a two-dimensional distributed parameter system , 2014, Comput. Chem. Eng..

[141]  Lei Zou,et al.  Finite-horizon H∞ state estimation for artificial neural networks with component-based distributed delays and stochastic protocol , 2018, Neurocomputing.

[142]  Shuai Liu,et al.  Error-constrained reliable tracking control for discrete time-varying systems subject to quantization effects , 2016, Neurocomputing.

[143]  Xinghuo Yu,et al.  Survey on Recent Advances in Networked Control Systems , 2016, IEEE Transactions on Industrial Informatics.

[144]  Jing Zhang,et al.  Observer-enhanced distributed moving horizon state estimation subject to communication delays , 2014 .

[145]  Honglei Qin,et al.  Application of the constrained moving horizon estimation method for the ultra-short baseline attitude determination , 2013, Acta Geodaetica et Geophysica.

[146]  Qing-Long Han,et al.  A Threshold-Parameter-Dependent Approach to Designing Distributed Event-Triggered $H_{\infty}$ Consensus Filters Over Sensor Networks , 2019, IEEE Transactions on Cybernetics.

[147]  Panagiotis D. Christofides,et al.  A method for handling batch-to-batch parametric drift using moving horizon estimation: Application to run-to-run MPC of batch crystallization , 2015 .

[148]  Jun Hu,et al.  Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements , 2012, Autom..

[149]  L. Biegler,et al.  Constrained particle filter approach to approximate the arrival cost in Moving Horizon Estimation , 2011 .

[150]  Mário A. T. Figueiredo,et al.  Moving horizon estimation of pedestrian interactions using multiple velocity fields , 2015, Signal Image Video Process..

[151]  Tamás Keviczky,et al.  Real-Time Fault-Tolerant Moving Horizon Air Data Estimation for the RECONFIGURE Benchmark , 2019, IEEE Transactions on Control Systems Technology.

[152]  Biao Huang,et al.  Moving horizon estimation for switching nonlinear systems , 2013, Autom..

[153]  Wen-an Zhang,et al.  Moving Horizon Estimation for Mobile Robots With Multirate Sampling , 2017, IEEE Transactions on Industrial Electronics.

[154]  Tingwen Huang,et al.  Synchronization Control for A Class of Discrete Time-Delay Complex Dynamical Networks: A Dynamic Event-Triggered Approach , 2019, IEEE Transactions on Cybernetics.

[155]  Marcello Farina,et al.  Distributed Moving Horizon Estimation for Linear Constrained Systems , 2010, IEEE Transactions on Automatic Control.