REFERENCES
1. Barfoot TD. State estimation for robotics. Cambridge University Press; 2017.
2. Shukla N, Tiwari MK, Beydoun G. Next generation smart manufacturing and service systems using big data analytics. Elsevier; 2019.
3. Ben-Akiva M, Bierlaire M, Burton D, Koutsopoulos HN, Mishalani R. Network state estimation and prediction for real-time transportation management applications. In: Transportation Research Board 81st Annual Meeting; 2002.
4. Tang X, Zhang Q, Hu L. An EKF-based performance enhancement scheme for stochastic nonlinear systems by dynamic set-point adjustment. IEEE Access 2020;8:62261-72.
5. Zhou Y, Zhang Q, Wang H, Zhou P, Chai T. Ekf-based enhanced performance controller design for nonlinear stochastic systems. IEEE Transactions on Automatic Control 2017;63:1155-62.
6. Kalman RE. A new approach to linear filtering and prediction problems 1960.
7. Ribeiro MI. Kalman and extended kalman filters: Concept, derivation and properties. Institute for Systems and Robotics 2004;43:46.
8. Xiong K, Zhang H, Chan C. Performance evaluation of UKF-based nonlinear filtering. Automatica 2006;42:261-70.
9. Kalman RE, Bucy RS. New results in linear filtering and prediction theory 1961.
10. Xiao X, Xi H, Zhu J, Ji H. Robust Kalman filter of continuous-time Markov jump linear systems based on state estimation performance. International Journal of Systems Science 2008;39:9-16.
11. Sarkka S. On unscented Kalman filtering for state estimation of continuous-time nonlinear systems. IEEE Transactions on Automatic Control 2007;52:1631-41.
12. Djuric PM, Kotecha JH, Zhang J, Huang Y, Ghirmai T, et al. Particle filtering. IEEE signal processing magazine 2003;20:19-38.
13. Guo L, Wang H. Minimum entropy filtering for multivariate stochastic systems with non-Gaussian noises. IEEE Transactions on Automatic control 2006;51:695-700.
14. Zhang Q. Performance enhanced Kalman filter design for non-Gaussian stochastic systems with data-based minimum entropy optimisation. AIMS Electronics and Electrical Engineering 2019;3:382-96.
15. Ghoreyshi A, Sanger TD. A nonlinear stochastic filter for continuous-time state estimation. IEEE Transactions on Automatic Control 2015;60:2161-65.
16. Ross SM, Kelly JJ, Sullivan RJ, Perry WJ, Mercer D, et al. Stochastic processes. vol. 2. Wiley New York; 1996.
17. Zhang Q, Wang Z, Wang H. Parametric covariance assignment using a reduced-order closed-form covariance model. Systems Science & Control Engineering 2016;4:78-86.
18. Zhang Q, Wang H. A Novel Data-based Stochastic Distribution Control for Non-Gaussian Stochastic Systems. IEEE Transactions on Automatic Control 2021; doi: 10.1109/TAC.2021.3064991.
19. Zhang Q, Sepulveda F. A model study of the neural interaction via mutual coupling factor identification. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; 2017. pp. 3329-32.
20. Zhang Q, Sepulveda F. A statistical description of pairwise interaction between nerve fibres. In: 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE; 2017. pp. 194-98.
21. Minskii R. Stochastic stability of differential equations, vol. 7 of Monographs and Textbooks on Mechanics of Solids and Fluids: Mechanics and Analysis. Sijthoff & Noordhoff, Alphen aan den Rijn 1980.
22. Liu SJ, Zhang JF, Jiang ZP. Decentralized adaptive output-feedback stabilization for large-scale stochastic nonlinear systems. Automatica 2007;43:238-51.
23. Zhang QC, Hu L, Gow J. Output feedback stabilization for MIMO semi-linear stochastic systems with transient optimisation. International Journal of Automation and Computing 2020;17:83-95.
24. Bejarano FJ, Fridman L. High order sliding mode observer for linear systems with unbounded unknown inputs. International Journal of Control 2010;83:1920-29.
25. Benallegue A, Mokhtari A, Fridman L. High-order sliding-mode observer for a quadrotor UAV. International Journal of Robust and Nonlinear Control: IFAC-Affiliated Journal 2008;18:427-40.
