estkit

References

Every publication cited in the report and in the source headers — grouped by topic. Each estimator is implemented from its primary references; see its catalogue page.

All 314 publications cited in the report and in the source headers, grouped by the bibliography file they live in (`report/*.bib`). Every estimator in estkit is implemented from the primary references listed in its chapter and in [CATALOGUE.md](CATALOGUE.md). DOIs were transcribed by the author and should be verified before formal reuse.

Core (battery modelling, estimation theory, standards) (146)

  1. Ackermann, J. (1972). Der Entwurf linearer Regelungssysteme im Zustandsraum. Regelungstechnik und Prozess-Datenverarbeitung 20(7), 297–300.
  2. Alspach, D. L. and Sorenson, H. W. (1972). Nonlinear Bayesian estimation using Gaussian sum approximations. IEEE Transactions on Automatic Control 17(4), 439–448. doi:10.1109/TAC.1972.1100034
  3. Anderson, B. D. O. and Moore, J. B. (1979). Optimal Filtering. Prentice-Hall.
  4. Arasaratnam, I. and Haykin, S. (2009). Cubature Kalman filters. IEEE Transactions on Automatic Control 54(6), 1254–1269. doi:10.1109/TAC.2009.2019800
  5. Arulampalam, M. S., Maskell, S., Gordon, N. and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174–188. doi:10.1109/78.978374
  6. Bar-Shalom, Y., Li, X. R. and Kirubarajan, T. (2001). Estimation with Applications to Tracking and Navigation: Theory, Algorithms and Software. Wiley. doi:10.1002/0471221279
  7. Baronti, F., Femia, N., Saletti, R., Visone, C. and Zamboni, W. (2014). Hysteresis modeling in Li-ion batteries. IEEE Transactions on Magnetics 50(11), 7300704. doi:10.1109/TMAG.2014.2323426
  8. Barrau, A. and Bonnabel, S. (2017). The invariant extended Kalman filter as a stable observer. IEEE Transactions on Automatic Control 62(4), 1797–1812. doi:10.1109/TAC.2016.2594085
  9. Barrau, A. and Bonnabel, S. (2018). Invariant Kalman filtering. Annual Review of Control, Robotics, and Autonomous Systems 1, 237–257. doi:10.1146/annurev-control-060117-105010
  10. Beale, S. and Shafai, B. (1989). Robust control system design with a proportional integral observer. International Journal of Control 50(1), 97–111. doi:10.1080/00207178908953350
  11. Bell, B. M. and Cathey, F. W. (1993). The iterated Kalman filter update as a Gauss-Newton method. IEEE Transactions on Automatic Control 38(2), 294–297. doi:10.1109/9.250476
  12. Bernardi, D., Pawlikowski, E. and Newman, J. (1985). A general energy balance for battery systems. Journal of The Electrochemical Society 132(1), 5–12. doi:10.1149/1.2113792
  13. Bierman, G. J. (1977). Factorization Methods for Discrete Sequential Estimation. Academic Press.
  14. Bishop, C. H., Etherton, B. J. and Majumdar, S. J. (2001). Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Monthly Weather Review 129(3), 420–436. doi:10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2129<0420:ASWTET>2.0.CO;2)
  15. Blackman, D. and Vigna, S. (2021). Scrambled linear pseudorandom number generators. ACM Transactions on Mathematical Software 47(4), 36:1–36:32. doi:10.1145/3460772
  16. Blom, H. A. P. and Bar-Shalom, Y. (1988). The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Transactions on Automatic Control 33(8), 780–783. doi:10.1109/9.1299
  17. Bucy, R. S. and Joseph, P. D. (1968). Filtering for Stochastic Processes with Applications to Guidance. Interscience Publishers.
  18. Burgers, G., van Leeuwen, P. J. and Evensen, G. (1998). Analysis scheme in the ensemble Kalman filter. Monthly Weather Review 126(6), 1719–1724. doi:10.1175/1520-0493(1998)126<1719:ASITEK>2.0.CO;2126<1719:ASITEK>2.0.CO;2)
  19. Carlson, N. A. (1990). Federated square root filter for decentralized parallel processes. IEEE Transactions on Aerospace and Electronic Systems 26(3), 517–525. doi:10.1109/7.106130
  20. Charkhgard, M. and Farrokhi, M. (2010). State-of-charge estimation for lithium-ion batteries using neural networks and EKF. IEEE Transactions on Industrial Electronics 57(12), 4178–4187. doi:10.1109/TIE.2010.2043035
  21. Chemali, E., Kollmeyer, P. J., Preindl, M. and Emadi, A. (2018). State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach. Journal of Power Sources 400, 242–250. doi:10.1016/j.jpowsour.2018.06.104
  22. Chen, B., Liu, X., Zhao, H. and Principe, J. C. (2017). Maximum correntropy Kalman filter. Automatica 76, 70–77. doi:10.1016/j.automatica.2016.10.004
  23. Chen, C. H., Brosa Planella, F., O'Regan, K., Gastol, D., Widanage, W. D. and Kendrick, E. (2020). Development of experimental techniques for parameterization of multi-scale lithium-ion battery models. Journal of The Electrochemical Society 167(8), 080534. doi:10.1149/1945-7111/ab9050
  24. Chen, J. and Patton, R. J. (1999). Robust Model-Based Fault Diagnosis for Dynamic Systems. Kluwer Academic Publishers. doi:10.1007/978-1-4615-5149-2
  25. Chen, J., Patton, R. J. and Zhang, H. Y. (1996). Design of unknown input observers and robust fault detection filters. International Journal of Control 63(1), 85–105. doi:10.1080/00207179608921833
  26. Chen, W. H., Yang, J., Guo, L. and Li, S. (2016). Disturbance-observer-based control and related methods—An overview. IEEE Transactions on Industrial Electronics 63(2), 1083–1095. doi:10.1109/TIE.2015.2478397
  27. Chen, X., Shen, W., Cao, Z. and Kapoor, A. (2014). A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles. Journal of Power Sources 246, 667–678. doi:10.1016/j.jpowsour.2013.08.039
  28. Crassidis, J. L., Markley, F. L. and Cheng, Y. (2007). Survey of nonlinear attitude estimation methods. Journal of Guidance, Control, and Dynamics 30(1), 12–28. doi:10.2514/1.22452
  29. Davila, J., Fridman, L. and Levant, A. (2005). Second-order sliding-mode observer for mechanical systems. IEEE Transactions on Automatic Control 50(11), 1785–1789. doi:10.1109/TAC.2005.858636
  30. Doucet, A., de Freitas, N. and Gordon, N. (2001). Sequential Monte Carlo Methods in Practice. Springer. doi:10.1007/978-1-4757-3437-9
  31. Doucet, A., de Freitas, N., Murphy, K. and Russell, S. (2000). Rao-Blackwellised particle filtering for dynamic Bayesian networks. In Proc. 16th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 176–183.
  32. Doucet, A., Godsill, S. and Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10(3), 197–208. doi:10.1023/A:1008935410038
  33. Doyle, M., Fuller, T. F. and Newman, J. (1993). Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of The Electrochemical Society 140(6), 1526–1533. doi:10.1149/1.2221597
  34. Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. Journal of Geophysical Research: Oceans 99(C5), 10143–10162. doi:10.1029/94JC00572
  35. Evensen, G. (2003). The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dynamics 53(4), 343–367. doi:10.1007/s10236-003-0036-9
  36. Forgez, C., Do, D. V., Friedrich, G., Morcrette, M. and Delacourt, C. (2010). Thermal modeling of a cylindrical LiFePO4/graphite lithium-ion battery. Journal of Power Sources 195(9), 2961–2968. doi:10.1016/j.jpowsour.2009.10.105
  37. Fredericks, W. L., Sripad, S., Bower, G. C. and Viswanathan, V. (2018). Performance metrics required of next-generation batteries to electrify vertical takeoff and landing (VTOL) aircraft. ACS Energy Letters 3(12), 2989–2994. doi:10.1021/acsenergylett.8b02195
  38. Gao, Z. (2003). Scaling and bandwidth-parameterization based controller tuning. In Proc. American Control Conference, pp. 4989–4996. doi:10.1109/ACC.2003.1242516
  39. Garcia, M. (2019–2024). AHRS: Attitude and Heading Reference Systems in Python. \urlhttps://github.com/Mayitzin/ahrs.
  40. Garcia-Fernandez, A. F., Svensson, L., Morelande, M. R. and Särkkä, S. (2015). Posterior linearization filter: Principles and implementation using sigma points. IEEE Transactions on Signal Processing 63(20), 5561–5573. doi:10.1109/TSP.2015.2454485
  41. Gelb, A. (1974). Applied Optimal Estimation. MIT Press.
  42. Golub, G. H. and Van Loan, C. F. (2013). Matrix Computations. Johns Hopkins University Press, 4th ed..
  43. Gordon, N. J., Salmond, D. J. and Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing) 140(2), 107–113. doi:10.1049/ip-f-2.1993.0015
  44. Gouze, J. L., Rapaport, A. and Hadj-Sadok, M. Z. (2000). Interval observers for uncertain biological systems. Ecological Modelling 133(1–2), 45–56. doi:10.1016/S0304-3800(00)00279-900279-9)
  45. Grewal, M. S. and Andrews, A. P. (2014). Kalman Filtering: Theory and Practice Using MATLAB. Wiley, 4th ed..
  46. Gustafsson, F. (2010). Particle filter theory and practice with positioning applications. IEEE Aerospace and Electronic Systems Magazine 25(7), 53–82. doi:10.1109/MAES.2010.5546308
  47. Habibi, S. (2007). The smooth variable structure filter. Proceedings of the IEEE 95(5), 1026–1059. doi:10.1109/JPROC.2007.893255
  48. Han, J. (2009). From PID to active disturbance rejection control. IEEE Transactions on Industrial Electronics 56(3), 900–906. doi:10.1109/TIE.2008.2011621
  49. Hassibi, B., Sayed, A. H. and Kailath, T. (1996). Linear estimation in Krein spaces—Part II: Applications. IEEE Transactions on Automatic Control 41(1), 34–49. doi:10.1109/9.481606
  50. Higgins, W. T. (1975). A comparison of complementary and Kalman filtering. IEEE Transactions on Aerospace and Electronic Systems AES-11(3), 321–325. doi:10.1109/TAES.1975.308081
  51. Hornik, K., Stinchcombe, M. and White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks 2(5), 359–366. doi:10.1016/0893-6080(89)90020-890020-8)
  52. Hu, X., Li, S. and Peng, H. (2012). A comparative study of equivalent circuit models for Li-ion batteries. Journal of Power Sources 198, 359–367. doi:10.1016/j.jpowsour.2011.10.013
  53. Huang, G. B., Zhu, Q. Y. and Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing 70(1–3), 489–501. doi:10.1016/j.neucom.2005.12.126
  54. Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics 35(1), 73–101. doi:10.1214/aoms/1177703732
  55. Ito, K. and Xiong, K. (2000). Gaussian filters for nonlinear filtering problems. IEEE Transactions on Automatic Control 45(5), 910–927. doi:10.1109/9.855552
  56. Jazwinski, A. H. (1970). Stochastic Processes and Filtering Theory. Academic Press.
  57. Jia, B., Xin, M. and Cheng, Y. (2013). High-degree cubature Kalman filter. Automatica 49(2), 510–518. doi:10.1016/j.automatica.2012.11.014
  58. Julier, S. J. and Uhlmann, J. K. (2004). Unscented filtering and nonlinear estimation. Proceedings of the IEEE 92(3), 401–422. doi:10.1109/JPROC.2003.823141
  59. Julier, S. J., Uhlmann, J. K. and Durrant-Whyte, H. F. (2000). A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control 45(3), 477–482. doi:10.1109/9.847726
  60. Kailath, T., Sayed, A. H. and Hassibi, B. (2000). Linear Estimation. Prentice Hall.
  61. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering 82(1), 35–45. doi:10.1115/1.3662552
  62. Kalman, R. E. and Bucy, R. S. (1961). New results in linear filtering and prediction theory. Journal of Basic Engineering 83(1), 95–108. doi:10.1115/1.3658902
  63. Kaminski, P. G., Bryson, A. E. and Schmidt, S. F. (1971). Discrete square root filtering: A survey of current techniques. IEEE Transactions on Automatic Control 16(6), 727–736. doi:10.1109/TAC.1971.1099816
  64. Karlgaard, C. D. and Schaub, H. (2007). Huber-based divided difference filtering. Journal of Guidance, Control, and Dynamics 30(3), 885–891. doi:10.2514/1.27968
  65. Khalil, H. K. (2002). Nonlinear Systems. Prentice Hall, 3rd ed..
  66. Khalil, H. K. and Praly, L. (2014). High-gain observers in nonlinear feedback control. International Journal of Robust and Nonlinear Control 24(6), 993–1015. doi:10.1002/rnc.3051
  67. Kim, I. S. (2006). The novel state of charge estimation method for lithium battery using sliding mode observer. Journal of Power Sources 163(1), 584–590. doi:10.1016/j.jpowsour.2006.09.006
  68. Kim, I. S. (2008). Nonlinear state of charge estimator for hybrid electric vehicle battery. IEEE Transactions on Power Electronics 23(4), 2027–2034. doi:10.1109/TPEL.2008.924629
  69. Kingma, D. P. and Ba, J. (2015). Adam: A method for stochastic optimization. In Proc. 3rd International Conference on Learning Representations (ICLR).
  70. Kitagawa, G. (1996). Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics 5(1), 1–25. doi:10.1080/10618600.1996.10474692
  71. Kreisselmeier, G. (1977). Adaptive observers with exponential rate of convergence. IEEE Transactions on Automatic Control 22(1), 2–8. doi:10.1109/TAC.1977.1101401
  72. Kühl, P., Diehl, M., Kraus, T., Schlöder, J. P. and Bock, H. G. (2011). A real-time algorithm for moving horizon state and parameter estimation. Computers & Chemical Engineering 35(1), 71–83. doi:10.1016/j.compchemeng.2010.07.012
  73. Labbe, R. R. (2014–2024). FilterPy: Kalman filtering and optimal estimation library for Python. \urlhttps://github.com/rlabbe/filterpy.
  74. Labbe, R. R. (2020). Kalman and Bayesian Filters in Python. \urlhttps://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python.
  75. Levant, A. (1993). Sliding order and sliding accuracy in sliding mode control. International Journal of Control 58(6), 1247–1263. doi:10.1080/00207179308923053
  76. Lin, X. (2018). Theoretical analysis of battery SOC estimation errors under sensor bias and variance. IEEE Transactions on Industrial Electronics 65(9), 7220–7230. doi:10.1109/TIE.2018.2795521
  77. Lin, X., Perez, H. E., Mohan, S., Siegel, J. B., Stefanopoulou, A. G., Ding, Y. et al. (2014). A lumped-parameter electro-thermal model for cylindrical batteries. Journal of Power Sources 257, 1–11. doi:10.1016/j.jpowsour.2014.01.097
  78. Ljung, L. (1999). System Identification: Theory for the User. Prentice Hall, 2nd ed..
  79. Loebis, D., Sutton, R., Chudley, J. and Naeem, W. (2004). Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system. Control Engineering Practice 12(12), 1531–1539. doi:10.1016/j.conengprac.2003.11.008
  80. Luenberger, D. G. (1964). Observing the state of a linear system. IEEE Transactions on Military Electronics 8(2), 74–80. doi:10.1109/TME.1964.4323124
  81. Luenberger, D. G. (1971). An introduction to observers. IEEE Transactions on Automatic Control 16(6), 596–602. doi:10.1109/TAC.1971.1099826
  82. Madgwick, S. O. H., Harrison, A. J. L. and Vaidyanathan, R. (2011). Estimation of IMU and MARG orientation using a gradient descent algorithm. In Proc. IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 1–7. doi:10.1109/ICORR.2011.5975346
  83. Magill, D. T. (1965). Optimal adaptive estimation of sampled stochastic processes. IEEE Transactions on Automatic Control 10(4), 434–439. doi:10.1109/TAC.1965.1098191
  84. Mahony, R., Hamel, T. and Pflimlin, J. M. (2008). Nonlinear complementary filters on the special orthogonal group. IEEE Transactions on Automatic Control 53(5), 1203–1218. doi:10.1109/TAC.2008.923738
  85. Markley, F. L. (2003). Attitude error representations for Kalman filtering. Journal of Guidance, Control, and Dynamics 26(2), 311–317. doi:10.2514/2.5048
  86. Marquis, S. G., Sulzer, V., Timms, R., Please, C. P. and Chapman, S. J. (2019). An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society 166(15), A3693–A3706. doi:10.1149/2.0341915jes
  87. Maybeck, P. S. (1979). Stochastic Models, Estimation, and Control, Volume 1. Academic Press.
  88. Mayergoyz, I. D. (1991). Mathematical Models of Hysteresis. Springer. doi:10.1007/978-1-4612-3028-1
  89. Mehra, R. K. (1970). On the identification of variances and adaptive Kalman filtering. IEEE Transactions on Automatic Control 15(2), 175–184. doi:10.1109/TAC.1970.1099422
  90. Mohamed, A. H. and Schwarz, K. P. (1999). Adaptive Kalman filtering for INS/GPS. Journal of Geodesy 73(4), 193–203. doi:10.1007/s001900050236
  91. Moore, J. B. (1973). Discrete-time fixed-lag smoothing algorithms. Automatica 9(2), 163–173. doi:10.1016/0005-1098(73)90071-X90071-X)
  92. Moreno, J. A. and Osorio, M. (2008). A Lyapunov approach to second-order sliding mode controllers and observers. In Proc. 47th IEEE Conference on Decision and Control, pp. 2856–2861. doi:10.1109/CDC.2008.4739356
  93. Musso, C., Oudjane, N. and Le Gland, F. c. (2001). Improving regularised particle filters. In Sequential Monte Carlo Methods in Practice, pp. 247–271. doi:10.1007/978-1-4757-3437-912
  94. Mutambara, A. G. O. (1998). Decentralized Estimation and Control for Multisensor Systems. CRC Press.
  95. Ng, K. S., Moo, C. S., Chen, Y. P. and Hsieh, Y. C. (2009). Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Applied Energy 86(9), 1506–1511. doi:10.1016/j.apenergy.2008.11.021
  96. Nocedal, J. and Wright, S. J. (2006). Numerical Optimization. Springer, 2nd ed.. doi:10.1007/978-0-387-40065-5
  97. Nørgaard, M., Poulsen, N. K. and Ravn, O. (2000). New developments in state estimation for nonlinear systems. Automatica 36(11), 1627–1638. doi:10.1016/S0005-1098(00)00089-300089-3)
  98. Ogata, K. (2010). Modern Control Engineering. Prentice Hall, 5th ed..
  99. Ohnishi, K., Shibata, M. and Murakami, T. (1996). Motion control for advanced mechatronics. IEEE/ASME Transactions on Mechatronics 1(1), 56–67. doi:10.1109/3516.491410
  100. Olfati-Saber, R. (2007). Distributed Kalman filtering for sensor networks. In Proc. 46th IEEE Conference on Decision and Control, pp. 5492–5498. doi:10.1109/CDC.2007.4434303
  101. Olfati-Saber, R., Fax, J. A. and Murray, R. M. (2007). Consensus and cooperation in networked multi-agent systems. Proceedings of the IEEE 95(1), 215–233. doi:10.1109/JPROC.2006.887293
  102. Pitt, M. K. and Shephard, N. (1999). Filtering via simulation: Auxiliary particle filters. Journal of the American Statistical Association 94(446), 590–599. doi:10.1080/01621459.1999.10474153
  103. Plett, G. L. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs—Part 1. Background. Journal of Power Sources 134(2), 252–261. doi:10.1016/j.jpowsour.2004.02.031
  104. Plett, G. L. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs—Part 2. Modeling and identification. Journal of Power Sources 134(2), 262–276. doi:10.1016/j.jpowsour.2004.02.032
  105. Plett, G. L. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs—Part 3. State and parameter estimation. Journal of Power Sources 134(2), 277–292. doi:10.1016/j.jpowsour.2004.02.033
  106. Plett, G. L. (2006). Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs—Part 1: Introduction and state estimation. Journal of Power Sources 161(2), 1356–1368. doi:10.1016/j.jpowsour.2006.06.003
  107. Plett, G. L. (2006). Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs—Part 2: Simultaneous state and parameter estimation. Journal of Power Sources 161(2), 1369–1384. doi:10.1016/j.jpowsour.2006.06.004
  108. Plett, G. L. (2009). Efficient battery pack state estimation using bar-delta filtering. In Proc. 24th International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium (EVS24).
  109. Plett, G. L. (2011). Recursive approximate weighted total least squares estimation of battery cell total capacity. Journal of Power Sources 196(4), 2319–2331. doi:10.1016/j.jpowsour.2010.09.048
  110. Plett, G. L. (2015). Battery Management Systems, Volume I: Battery Modeling. Artech House.
  111. Plett, G. L. (2016). Battery Management Systems, Volume II: Equivalent-Circuit Methods. Artech House.
  112. Potter, J. E. and Stern, R. G. (1963). Statistical filtering of space navigation measurements. In Proc. AIAA Guidance and Control Conference. doi:10.2514/6.1963-333
  113. Preisach, F. (1935). Über die magnetische Nachwirkung. Zeitschrift für Physik 94, 277–302. doi:10.1007/BF01349418
  114. Raissi, M., Perdikaris, P. and Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics 378, 686–707. doi:10.1016/j.jcp.2018.10.045
  115. Rajamani, R. (1998). Observers for Lipschitz nonlinear systems. IEEE Transactions on Automatic Control 43(3), 397–401. doi:10.1109/9.661604
  116. Rao, C. V., Rawlings, J. B. and Mayne, D. Q. (2003). Constrained state estimation for nonlinear discrete-time systems: Stability and moving horizon approximations. IEEE Transactions on Automatic Control 48(2), 246–258. doi:10.1109/TAC.2002.808470
  117. Rauch, H. E., Tung, F. and Striebel, C. T. (1965). Maximum likelihood estimates of linear dynamic systems. AIAA Journal 3(8), 1445–1450. doi:10.2514/3.3166
  118. Rawlings, J. B., Mayne, D. Q. and Diehl, M. M. (2017). Model Predictive Control: Theory, Computation, and Design. Nob Hill Publishing, 2nd ed..
  119. Raïssi, T., Efimov, D. and Zolghadri, A. (2012). Interval state estimation for a class of nonlinear systems. IEEE Transactions on Automatic Control 57(1), 260–265. doi:10.1109/TAC.2011.2164820
  120. Roth, M., Özkan, E. and Gustafsson, F. (2013). A Student's t filter for heavy tailed process and measurement noise. In Proc. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5770–5774. doi:10.1109/ICASSP.2013.6638770
  121. RTCA (2017). DO-311A: Minimum Operational Performance Standards for Rechargeable Lithium Batteries and Battery Systems. RTCA, Inc..
  122. Sage, A. P. and Husa, G. W. (1969). Adaptive filtering with unknown prior statistics. In Proc. Joint Automatic Control Conference, pp. 760–769. doi:10.1109/JACC.1969.4169352
  123. Schmalstieg, J., Käbitz, S., Ecker, M. and Sauer, D. U. (2014). A holistic aging model for Li(NiMnCo)O2 based 18650 lithium-ion batteries. Journal of Power Sources 257, 325–334. doi:10.1016/j.jpowsour.2014.02.012
  124. Schmidt, S. F. (1966). Application of state-space methods to navigation problems. In Advances in Control Systems, pp. 293–340. doi:10.1016/B978-1-4831-6716-9.50011-4
  125. Schön, T., Gustafsson, F. and Nordlund, P. J. (2005). Marginalized particle filters for mixed linear/nonlinear state-space models. IEEE Transactions on Signal Processing 53(7), 2279–2289. doi:10.1109/TSP.2005.849151
  126. Shen, X. and Deng, L. (1997). Game theory approach to discrete H∞ filter design. IEEE Transactions on Signal Processing 45(4), 1092–1095. doi:10.1109/78.564201
  127. Simon, D. (2006). Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches. Wiley. doi:10.1002/0470045345
  128. Simon, D. (2010). Kalman filtering with state constraints: a survey of linear and nonlinear algorithms. IET Control Theory & Applications 4(8), 1303–1318. doi:10.1049/iet-cta.2009.0032
  129. Sorenson, H. W. and Sacks, J. E. (1971). Recursive fading memory filtering. Information Sciences 3(2), 101–119. doi:10.1016/S0020-0255(71)80001-480001-4)
  130. Sulzer, V., Marquis, S. G., Timms, R., Robinson, M., Chapman, S. J. and Howey, D. A. (2021). Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software 9(1), 14. doi:10.5334/jors.309
  131. Särkkä, S. (2008). Unscented Rauch–Tung–Striebel smoother. IEEE Transactions on Automatic Control 53(3), 845–849. doi:10.1109/TAC.2008.919531
  132. Särkkä, S. (2013). Bayesian Filtering and Smoothing. Cambridge University Press. doi:10.1017/CBO9781139344203
  133. Särkkä, S. and Nummenmaa, A. (2009). Recursive noise adaptive Kalman filtering by variational Bayesian approximations. IEEE Transactions on Automatic Control 54(3), 596–600. doi:10.1109/TAC.2008.2008348
  134. Tapley, B. D., Schutz, B. E. and Born, G. H. (2004). Statistical Orbit Determination. Elsevier Academic Press.
  135. Thornton, C. L. and Bierman, G. J. (1977). Gram-Schmidt algorithms for covariance propagation. International Journal of Control 25(2), 243–260. doi:10.1080/00207177708922227
  136. Utkin, V. I. (1992). Sliding Modes in Control and Optimization. Springer. doi:10.1007/978-3-642-84379-2
  137. van der Merwe, R. and Wan, E. A. (2001). The square-root unscented Kalman filter for state and parameter-estimation. In Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), pp. 3461–3464. doi:10.1109/ICASSP.2001.940586
  138. van der Merwe, R., Doucet, A., de Freitas, N. and Wan, E. (2000). The unscented particle filter. Cambridge University Engineering Department, CUED/F-INFENG/TR 380.
  139. Verhaegen, M. and Van Dooren, P. (1986). Numerical aspects of different Kalman filter implementations. IEEE Transactions on Automatic Control 31(10), 907–917. doi:10.1109/TAC.1986.1104128
  140. Wan, E. A. and van der Merwe, R. (2000). The unscented Kalman filter for nonlinear estimation. In Proc. IEEE Adaptive Systems for Signal Processing, Communications, and Control Symposium (AS-SPCC), pp. 153–158. doi:10.1109/ASSPCC.2000.882463
  141. Wang, J., Liu, P., Hicks-Garner, J., Sherman, E., Soukiazian, S., Verbrugge, M. et al. (2011). Cycle-life model for graphite-LiFePO4 cells. Journal of Power Sources 196(8), 3942–3948. doi:10.1016/j.jpowsour.2010.11.134
  142. Xiong, R., Cao, J., Yu, Q., He, H. and Sun, F. (2018). Critical review on the battery state of charge estimation methods for electric vehicles. IEEE Access 6, 1832–1843. doi:10.1109/ACCESS.2017.2780258
  143. Yang, X. G., Liu, T., Ge, S., Rountree, E. and Wang, C. Y. (2021). Challenges and key requirements of batteries for electric vertical takeoff and landing aircraft. Joule 5(7), 1644–1659. doi:10.1016/j.joule.2021.05.001
  144. Zhang, Q. (2002). Adaptive observer for multiple-input-multiple-output (MIMO) linear time-varying systems. IEEE Transactions on Automatic Control 47(3), 525–529. doi:10.1109/9.989154
  145. Zhao, S., Duncan, S. R. and Howey, D. A. (2017). Observability analysis and state estimation of lithium-ion batteries in the presence of sensor biases. IEEE Transactions on Control Systems Technology 25(1), 326–333. doi:10.1109/TCST.2016.2542115
  146. Zhou, D. H. and Frank, P. M. (1996). Strong tracking filtering of nonlinear time-varying stochastic systems with coloured noise: application to parameter estimation and empirical robustness analysis. International Journal of Control 65(2), 295–307. doi:10.1080/00207179608921698

Adaptive Kalman filters (10)

  1. Beal, M. J. (2003). Variational Algorithms for Approximate Bayesian Inference. PhD thesis, Gatsby Computational Neuroscience Unit, University College London.
  2. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
  3. Hanlon, P. D. and Maybeck, P. S. (2000). Multiple-model adaptive estimation using a residual correlation Kalman filter bank. IEEE Transactions on Aerospace and Electronic Systems 36(2), 393–406.
  4. Huang, Y., Zhang, Y., Wu, Z., Li, N. and Chambers, J. (2018). A novel adaptive Kalman filter with inaccurate process and measurement noise covariance matrices. IEEE Transactions on Automatic Control 63(2), 594–601.
  5. Jwo, D. J. and Wang, S. H. (2007). Adaptive fuzzy strong tracking extended Kalman filtering for GPS navigation. IEEE Sensors Journal 7(5), 778–789.
  6. Li, X. R. and Jilkov, V. P. (2005). Survey of maneuvering target tracking. Part V: Multiple-model methods. IEEE Transactions on Aerospace and Electronic Systems 41(4), 1255–1321. doi:10.1109/TAES.2005.1561886
  7. Mamdani, E. H. and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies 7(1), 1–13. doi:10.1016/S0020-7373(75)80002-280002-2)
  8. Sage, A. P. and Melsa, J. L. (1971). Estimation Theory with Applications to Communications and Control. McGraw-Hill.
  9. Xia, Q., Rao, M., Ying, Y. and Shen, X. (1994). Adaptive fading Kalman filter with an application. Automatica 30(8), 1333–1338.
  10. Zadeh, L. A. (1965). Fuzzy sets. Information and Control 8(3), 338–353. doi:10.1016/S0019-9958(65)90241-X90241-X)

Attitude estimation (22)

  1. Black, H. D. (1964). A passive system for determining the attitude of a satellite. AIAA Journal 2(7), 1350–1351.
  2. Bonnabel, S. (2007). Left-invariant extended Kalman filter and attitude estimation. In Proceedings of the 46th IEEE Conference on Decision and Control, pp. 1027–1032.
  3. Bonnabel, S., Martin, P. and Rouchon, P. (2009). Non-linear symmetry-preserving observers on Lie groups. IEEE Transactions on Automatic Control 54(7), 1709–1713.
  4. Crassidis, J. L. and Markley, F. L. (2003). Unscented filtering for spacecraft attitude estimation. Journal of Guidance, Control, and Dynamics 26(4), 536–542.
  5. Euston, M., Coote, P., Mahony, R., Kim, J. and Hamel, T. (2008). A complementary filter for attitude estimation of a fixed-wing UAV. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 340–345.
  6. Farrenkopf, R. L. (1978). Analytic steady-state accuracy solutions for two common spacecraft attitude estimators. Journal of Guidance and Control 1(4), 282–284.
  7. Gebre-Egziabher, D., Elkaim, G. H., Powell, J. D. and Parkinson, B. W. (2006). Calibration of strapdown magnetometers in magnetic field domain. Journal of Aerospace Engineering 19(2), 87–102.
  8. Hamel, T. and Mahony, R. (2006). Attitude estimation on SO(3) based on direct inertial measurements. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 2170–2175.
  9. Hua, M. D. (2010). Attitude estimation for accelerated vehicles using GPS/INS measurements. Control Engineering Practice 18(7), 723–732.
  10. Keat, J. (1977). Analysis of least-squares attitude determination routine DOAID. Computer Sciences Corporation, CSC/TM-77/6034.
  11. Kuipers, J. B. (1999). Quaternions and Rotation Sequences: A Primer with Applications to Orbits, Aerospace and Virtual Reality. Princeton University Press.
  12. Lefferts, E. J., Markley, F. L. and Shuster, M. D. (1982). Kalman filtering for spacecraft attitude estimation. Journal of Guidance, Control, and Dynamics 5(5), 417–429.
  13. Madgwick, S. O. H. (2010). An efficient orientation filter for inertial and inertial/magnetic sensor arrays.
  14. Markley, F. L. (1988). Attitude determination using vector observations and the singular value decomposition. The Journal of the Astronautical Sciences 36(3), 245–258.
  15. Markley, F. L. and Crassidis, J. L. (2014). Fundamentals of Spacecraft Attitude Determination and Control. Springer.
  16. Markley, F. L. and Mortari, D. (2000). Quaternion attitude estimation using vector observations. The Journal of the Astronautical Sciences 48(2-3), 359–380.
  17. Shepperd, S. W. (1978). Quaternion from rotation matrix. Journal of Guidance and Control 1(3), 223–224.
  18. Shuster, M. D. and Oh, S. D. (1981). Three-axis attitude determination from vector observations. Journal of Guidance and Control 4(1), 70–77.
  19. Thebault, E., Finlay, C. C., Beggan, C. D. and others (2015). International Geomagnetic Reference Field: the 12th generation. Earth, Planets and Space 67, 79.
  20. Titterton, D. H. and Weston, J. L. (2004). Strapdown Inertial Navigation Technology. The Institution of Engineering and Technology, 2nd ed..
  21. Van Loan, C. F. (1978). Computing integrals involving the matrix exponential. IEEE Transactions on Automatic Control 23(3), 395–404.
  22. Wahba, G. (1965). A least squares estimate of satellite attitude. SIAM Review 7(3), 409.

Foundations, datasets, methodology, library (42)

  1. (2008). MISRA C++:2008 — Guidelines for the Use of the C++ Language in Critical Systems.
  2. (2017). ISO/IEC 14882:2017 — Programming Languages — C++.
  3. (2019). IEEE Standard for Floating-Point Arithmetic.
  4. (2021). Arm Cortex-M7 Processor Technical Reference Manual.
  5. Anderson, B. D. O. (1978). Second-order convergent algorithms for the steady-state Riccati equation. International Journal of Control 28(2), 295–306.
  6. Andersson, J. A. E., Gillis, J., Horn, G., Rawlings, J. B. and Diehl, M. (2019). CasADi: A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation 11(1), 1–36.
  7. Birkl, C. R., Roberts, M. R., McTurk, E., Bruce, P. G. and Howey, D. A. (2017). Degradation diagnostics for lithium ion cells. Journal of Power Sources 341, 373–386.
  8. Bizeray, A. M., Zhao, S., Duncan, S. R. and Howey, D. A. (2015). Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter. Journal of Power Sources 296, 400–412.
  9. Brosa Planella, F., Ai, W., Boyce, A. M., Ghosh, A., Korotkin, I., Sahu, S. et al. (2022). A continuum of physics-based lithium-ion battery models reviewed. Progress in Energy 4(4), 042003.
  10. Chen, C. T. (1999). Linear System Theory and Design. Oxford University Press, 3rd ed..
  11. Chu, E. K. w., Fan, H. Y., Lin, W. W. and Wang, C. S. (2004). Structure-preserving algorithms for periodic discrete-time algebraic Riccati equations. International Journal of Control 77(8), 767–788.
  12. Chulliat, A., Brown, W., Alken, P., Beggan, C., Nair, M., Cox, G. et al. (2020). The US/UK World Magnetic Model for 2020–2025. National Centers for Environmental Information, NOAA.
  13. Defense, U. S. D. o. (1997). Flying Qualities of Piloted Aircraft. US Department of Defense, MIL-HDBK-1797.
  14. Dreyer, W., Jamnik, J., Guhlke, C., Huth, R., Moškon, J. and Gaberšček, M. (2010). The thermodynamic origin of hysteresis in insertion batteries. Nature Materials 9(5), 448–453.
  15. Gill, P. E., Golub, G. H., Murray, W. and Saunders, M. A. (1974). Methods for modifying matrix factorizations. Mathematics of Computation 28(126), 505–535.
  16. Groves, P. D. (2013). Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems. Artech House, 2nd ed..
  17. Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D. et al. (2020). Array programming with NumPy. Nature 585, 357–362.
  18. Hermann, R. and Krener, A. J. (1977). Nonlinear controllability and observability. IEEE Transactions on Automatic Control 22(5), 728–740. doi:10.1109/TAC.1977.1101601
  19. Ho, Y. C. and Lee, R. C. K. (1964). A Bayesian approach to problems in stochastic estimation and control. IEEE Transactions on Automatic Control 9(4), 333–339.
  20. Holzmann, G. J. (2006). The power of ten: Rules for developing safety-critical code. Computer 39(6), 95–99.
  21. Jaguemont, J., Boulon, L. and Dube, Y. (2016). A comprehensive review of lithium-ion batteries used in hybrid and electric vehicles at cold temperatures. Applied Energy 164, 99–114.
  22. Kalman, R. E. (1960). On the general theory of control systems. In Proceedings of the First International Congress of the International Federation of Automatic Control (IFAC), Moscow, pp. 481–492.
  23. Kautsky, J., Nichols, N. K. and Van Dooren, P. (1985). Robust pole assignment in linear state feedback. International Journal of Control 41(5), 1129–1155.
  24. Krener, A. J. and Ide, K. (2009). Measures of unobservability. In Proceedings of the 48th IEEE Conference on Decision and Control (CDC), pp. 6401–6406.
  25. Levenberg, K. (1944). A method for the solution of certain non-linear problems in least squares. Quarterly of Applied Mathematics 2(2), 164–168.
  26. Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics 11(2), 431–441. doi:10.1137/0111030
  27. Marsaglia, G. and Bray, T. A. (1964). A convenient method for generating normal variables. SIAM Review 6(3), 260–264.
  28. Masreliez, C. J. (1975). Approximate non-Gaussian filtering with linear state and observation relations. IEEE Transactions on Automatic Control 20(1), 107–110.
  29. Moura, S. J., Argomedo, F. B., Klein, R., Mirtabatabaei, A. and Krstic, M. (2017). Battery state estimation for a single particle model with electrolyte dynamics. IEEE Transactions on Control Systems Technology 25(2), 453–468.
  30. Newman, J. and Thomas-Alyea, K. E. (2004). Electrochemical Systems. Wiley, 3rd ed..
  31. Patankar, S. V. (1980). Numerical Heat Transfer and Fluid Flow. Hemisphere Publishing Corporation.
  32. Santhanagopalan, S. and White, R. E. (2006). Online estimation of the state of charge of a lithium ion cell. Journal of Power Sources 161(2), 1346–1355.
  33. Smith, A. J., Burns, J. C., Trussler, S. and Dahn, J. R. (2010). Precision measurements of the coulombic efficiency of lithium-ion batteries and of electrode materials for lithium-ion batteries. Journal of The Electrochemical Society 157(2), A196–A202.
  34. Stevens, B. L., Lewis, F. L. and Johnson, E. N. (2015). Aircraft Control and Simulation: Dynamics, Controls Design, and Autonomous Systems. Wiley, 3rd ed..
  35. Stratonovich, R. L. (1960). Conditional Markov processes. Theory of Probability and its Applications 5(2), 156–178.
  36. Subramanian, V. R., Diwakar, V. D. and Tapriyal, D. (2005). Efficient macro-micro scale coupled modeling of batteries. Journal of The Electrochemical Society 152(10), A2002–A2008.
  37. Thomas, L. H. (1949). Elliptic Problems in Linear Difference Equations over a Network. Watson Scientific Computing Laboratory, Columbia University.
  38. Tichavsky, P., Muravchik, C. H. and Nehorai, A. (1998). Posterior Cramer–Rao bounds for discrete-time nonlinear filtering. IEEE Transactions on Signal Processing 46(5), 1386–1396. doi:10.1109/78.668800
  39. Van Trees, H. L. (1968). Detection, Estimation, and Modulation Theory, Part I. Wiley.
  40. Waag, W., Käbitz, S. and Sauer, D. U. (2013). Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application. Applied Energy 102, 885–897.
  41. Weng, C., Sun, J. and Peng, H. (2014). A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring. Journal of Power Sources 258, 228–237.
  42. Widrow, B. and Kollar, I. (2008). Quantization Noise: Roundoff Error in Digital Computation, Signal Processing, Control, and Communications. Cambridge University Press.

Classical & sigma-point Kalman filters (14)

  1. Andrews, A. (1968). A square root formulation of the Kalman covariance equations. AIAA Journal 6(6), 1165–1166.
  2. Arasaratnam, I., Haykin, S. and Elliott, R. J. (2007). Discrete-time nonlinear filtering algorithms using Gauss–Hermite quadrature. Proceedings of the IEEE 95(5), 953–977.
  3. Athans, M., Wishner, R. P. and Bertolini, A. (1968). Suboptimal state estimation for continuous-time nonlinear systems from discrete noisy measurements. IEEE Transactions on Automatic Control 13(5), 504–514. doi:10.1109/TAC.1968.1098986
  4. Bass, R. W., Norum, V. D. and Schwartz, L. (1966). Optimal multichannel nonlinear filtering. Journal of Mathematical Analysis and Applications 16(1), 152–164.
  5. Carlson, N. A. (1973). Fast triangular formulation of the square root filter. AIAA Journal 11(9), 1259–1265.
  6. Garcia-Fernandez, A. F., Svensson, L. and Särkkä, S. (2017). Iterated posterior linearization smoother. IEEE Transactions on Automatic Control 62(4), 2056–2063.
  7. Gill, P. E., Golub, G. H., Murray, W. and Saunders, M. A. (1974). Methods for modifying matrix factorizations. Mathematics of Computation 28(126), 505–535.
  8. Golub, G. H. and Welsch, J. H. (1969). Calculation of Gauss quadrature rules. Mathematics of Computation 23(106), 221–230.
  9. Maybeck, P. S. (1982). Stochastic Models, Estimation, and Control, Volume 2. Academic Press.
  10. Schei, T. S. (1997). A finite-difference method for linearization in nonlinear estimation algorithms. Automatica 33(11), 2053–2058.
  11. Sorenson, H. W. (1970). Least-squares estimation: from Gauss to Kalman. IEEE Spectrum 7(7), 63–68.
  12. Stroud, A. H. (1971). Approximate Calculation of Multiple Integrals. Prentice-Hall.
  13. Thrun, S., Burgard, W. and Fox, D. (2005). Probabilistic Robotics. MIT Press.
  14. Wu, Y., Hu, D., Wu, M. and Hu, X. (2006). A numerical-integration perspective on Gaussian filters. IEEE Transactions on Signal Processing 54(8), 2910–2921.

Data-driven hybrids (8)

  1. Agency, E. U. A. S. (2023). EASA Artificial Intelligence Concept Paper Issue 2: Guidance for Level 1&2 Machine Learning Applications. European Union Aviation Safety Agency.
  2. Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press.
  3. Glorot, X. and Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 249–256.
  4. Liang, N. Y., Huang, G. B., Saratchandran, P. and Sundararajan, N. (2006). A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks 17(6), 1411–1423. doi:10.1109/TNN.2006.880583
  5. Pao, Y. H., Park, G. H. and Sobajic, D. J. (1994). Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6(2), 163–180.
  6. RTCA (2011). DO-178C: Software Considerations in Airborne Systems and Equipment Certification. RTCA, Inc..
  7. Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986). Learning representations by back-propagating errors. Nature 323(6088), 533–536. doi:10.1038/323533a0
  8. Vidal, C., Malysz, P., Kollmeyer, P. and Emadi, A. (2020). Machine learning applied to electrified vehicle battery state of charge and state of health estimation: State-of-the-art. IEEE Access 8, 52796–52814.

Deterministic observers (23)

  1. Bernard, P., Andrieu, V. and Astolfi, D. (2022). Observer design for continuous-time dynamical systems. Annual Reviews in Control 53, 224–248.
  2. Darouach, M., Zasadzinski, M. and Xu, S. J. (1994). Full-order observers for linear systems with unknown inputs. IEEE Transactions on Automatic Control 39(3), 606–609.
  3. Efimov, D. and Raïssi, T. (2016). Design of interval observers for uncertain dynamical systems. Automation and Remote Control 77(2), 191–225.
  4. Esfandiari, F. and Khalil, H. K. (1992). Output feedback stabilization of fully linearizable systems. International Journal of Control 56(5), 1007–1037.
  5. Franklin, G. F., Powell, J. D. and Workman, M. L. (1998). Digital Control of Dynamic Systems. Addison-Wesley, 3rd ed..
  6. Friedland, B. (1969). Treatment of bias in recursive filtering. IEEE Transactions on Automatic Control 14(4), 359–367.
  7. Hou, M. and Müller, P. C. (1992). Design of observers for linear systems with unknown inputs. IEEE Transactions on Automatic Control 37(6), 871–875.
  8. Ioannou, P. A. and Kokotovic, P. V. (1984). Instability analysis and improvement of robustness of adaptive control. Automatica 20(5), 583–594.
  9. Ioannou, P. A. and Sun, J. (1996). Robust Adaptive Control. Prentice Hall.
  10. Johnson, C. D. (1971). Accommodation of external disturbances in linear regulator and servomechanism problems. IEEE Transactions on Automatic Control 16(6), 635–644.
  11. Kautsky, J., Nichols, N. K. and Van Dooren, P. (1985). Robust pole assignment in linear state feedback. International Journal of Control 41(5), 1129–1155.
  12. Khalil, H. K. (2017). High-Gain Observers in Nonlinear Feedback Control. SIAM.
  13. Mazenc, F. and Bernard, O. (2011). Interval observers for linear time-invariant systems with disturbances. Automatica 47(1), 140–147.
  14. Moreno, J. A. and Osorio, M. (2012). Strict Lyapunov functions for the super-twisting algorithm. IEEE Transactions on Automatic Control 57(4), 1035–1040.
  15. Petkov, P. H., Christov, N. D. and Konstantinov, M. M. (1984). A computational algorithm for pole assignment of linear single input systems. IEEE Transactions on Automatic Control 29(11), 1045–1048.
  16. Plestan, F., Shtessel, Y., Bregeault, V. and Poznyak, A. (2010). New methodologies for adaptive sliding mode control. International Journal of Control 83(9), 1907–1919.
  17. Shafai, B., Beale, S., Niemann, H. H. and Stoustrup, J. L. (1996). LTR design of discrete-time proportional-integral observers. IEEE Transactions on Automatic Control 41(7), 1056–1062.
  18. Shamma, J. S. and Athans, M. (1990). Analysis of gain scheduled control for nonlinear plants. IEEE Transactions on Automatic Control 35(8), 898–907.
  19. Slotine, J. J. E., Hedrick, J. K. and Misawa, E. A. (1987). On sliding observers for nonlinear systems. Journal of Dynamic Systems, Measurement, and Control 109(3), 245–252.
  20. Smith, H. L. (1995). Monotone Dynamical Systems: An Introduction to the Theory of Competitive and Cooperative Systems. American Mathematical Society.
  21. Wojciechowski, B. (1978). Analysis and synthesis of proportional-integral observers for single-input-single-output time-invariant continuous systems. PhD thesis, Gliwice Technical University.
  22. Zhang, Q. and Clavel, A. (2001). Adaptive observer with exponential forgetting factor for linear time varying systems. In Proc. 40th IEEE Conference on Decision and Control, pp. 3886–3891.
  23. Zheng, Q., Gao, L. Q. and Gao, Z. (2007). On stability analysis of active disturbance rejection control for nonlinear time-varying plants with unknown dynamics. In Proc. 46th IEEE Conference on Decision and Control, pp. 3501–3506.

Moving-horizon estimation (8)

  1. Alessandri, A., Baglietto, M. and Battistelli, G. (2008). Moving-horizon state estimation for nonlinear discrete-time systems: New stability results and approximation schemes. Automatica 44(7), 1753–1765.
  2. Bertsekas, D. P. (1982). Projected Newton methods for optimization problems with simple constraints. SIAM Journal on Control and Optimization 20(2), 221–246. doi:10.1137/0320018
  3. Bock, H. G. and Plitt, K. J. (1984). A multiple shooting algorithm for direct solution of optimal control problems. In Proceedings of the 9th IFAC World Congress, pp. 242–247.
  4. Diehl, M., Bock, H. G. and Schlöder, J. P. (2005). A real-time iteration scheme for nonlinear optimization in optimal feedback control. SIAM Journal on Control and Optimization 43(5), 1714–1736. doi:10.1137/S0363012902400713
  5. Haseltine, E. L. and Rawlings, J. B. (2005). Critical evaluation of extended Kalman filtering and moving-horizon estimation. Industrial & Engineering Chemistry Research 44(8), 2451–2460.
  6. Muske, K. R., Rawlings, J. B. and Lee, J. H. (1993). Receding horizon recursive state estimation. In Proceedings of the 1993 American Control Conference, pp. 900–904.
  7. Robertson, D. G., Lee, J. H. and Rawlings, J. B. (1996). A moving horizon-based approach for least-squares estimation. AIChE Journal 42(8), 2209–2224.
  8. Wang, Y. and Boyd, S. (2010). Fast model predictive control using online optimization. IEEE Transactions on Control Systems Technology 18(2), 267–278. doi:10.1109/TCST.2009.2017934

Pack-level estimation (7)

  1. Carlson, N. A. (1988). Federated filter for fault-tolerant integrated navigation systems. In Proc. IEEE Position Location and Navigation Symposium (PLANS '88), pp. 110–119.
  2. Carlson, N. A. and Berarducci, M. P. (1994). Federated Kalman filter simulation results. Navigation: Journal of the Institute of Navigation 41(3), 297–321.
  3. Grime, S. and Durrant-Whyte, H. F. (1994). Data fusion in decentralized sensor networks. Control Engineering Practice 2(5), 849–863.
  4. Olfati-Saber, R. (2005). Distributed Kalman filter with embedded consensus filters. In Proc. 44th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), pp. 8179–8184.
  5. Olfati-Saber, R. (2009). Kalman-consensus filter: optimality, stability, and performance. In Proc. 48th IEEE Conference on Decision and Control (CDC) held jointly with the 28th Chinese Control Conference, pp. 7036–7042.
  6. Rao, B. S. Y., Durrant-Whyte, H. F. and Sheen, J. A. (1993). A fully decentralized multi-sensor system for tracking and surveillance. The International Journal of Robotics Research 12(1), 20–44.
  7. Sun, S. L. and Deng, Z. L. (2004). Multi-sensor optimal information fusion Kalman filter. Automatica 40(6), 1017–1023.

Particle, ensemble & Gaussian-sum filters (18)

  1. Anderson, J. L. and Anderson, S. L. (1999). A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Monthly Weather Review 127(12), 2741–2758.
  2. Cappe, O., Godsill, S. J. and Moulines, E. (2007). An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE 95(5), 899–924. doi:10.1109/JPROC.2007.893250
  3. Casella, G. and Robert, C. P. (1996). Rao-Blackwellisation of sampling schemes. Biometrika 83(1), 81–94. doi:10.1093/biomet/83.1.81
  4. Chen, R. and Liu, J. S. (2000). Mixture Kalman filters. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 62(3), 493–508. doi:10.1111/1467-9868.00246
  5. Crisan, D. and Doucet, A. (2002). A survey of convergence results on particle filtering methods for practitioners. IEEE Transactions on Signal Processing 50(3), 736–746. doi:10.1109/78.984773
  6. Douc, R., Cappe, O. and Moulines, E. (2005). Comparison of resampling schemes for particle filtering. In Proc. 4th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 64–69. doi:10.1109/ISPA.2005.195385
  7. Doucet, A. and Johansen, A. M. (2011). A tutorial on particle filtering and smoothing: Fifteen years later. In The Oxford Handbook of Nonlinear Filtering, pp. 656–704.
  8. Hol, J. D., Schön, T. B. and Gustafsson, F. (2006). On resampling algorithms for particle filters. In Proc. IEEE Nonlinear Statistical Signal Processing Workshop (NSSPW), pp. 79–82. doi:10.1109/NSSPW.2006.4378824
  9. Hunt, B. R., Kostelich, E. J. and Szunyogh, I. (2007). Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D: Nonlinear Phenomena 230(1-2), 112–126. doi:10.1016/j.physd.2006.11.008
  10. Kong, A., Liu, J. S. and Wong, W. H. (1994). Sequential imputations and Bayesian missing data problems. Journal of the American Statistical Association 89(425), 278–288. doi:10.1080/01621459.1994.10476469
  11. Liu, J. S. and Chen, R. (1998). Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association 93(443), 1032–1044. doi:10.1080/01621459.1998.10473765
  12. Runnalls, A. R. (2007). Kullback-Leibler approach to Gaussian mixture reduction. IEEE Transactions on Aerospace and Electronic Systems 43(3), 989–999. doi:10.1109/TAES.2007.4383588
  13. Salmond, D. J. (1990). Mixture reduction algorithms for target tracking in clutter. In Signal and Data Processing of Small Targets 1990, Proc. SPIE, pp. 434–445.
  14. Schwunk, S., Armbruster, N., Straub, S., Kehl, J. and Vetter, M. (2013). Particle filter for state of charge and state of health estimation for lithium-iron phosphate batteries. Journal of Power Sources 239, 705–710. doi:10.1016/j.jpowsour.2012.10.058
  15. Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis. Chapman & Hall.
  16. Sorenson, H. W. and Alspach, D. L. (1971). Recursive Bayesian estimation using Gaussian sums. Automatica 7(4), 465–479. doi:10.1016/0005-1098(71)90097-590097-5)
  17. Tippett, M. K., Anderson, J. L., Bishop, C. H., Hamill, T. M. and Whitaker, J. S. (2003). Ensemble square root filters. Monthly Weather Review 131(7), 1485–1490.
  18. Whitaker, J. S. and Hamill, T. M. (2002). Ensemble data assimilation without perturbed observations. Monthly Weather Review 130(7), 1913–1924.

Robust & embedded Kalman filters (12)

  1. Agamennoni, G., Nieto, J. I. and Nebot, E. M. (2012). Approximate inference in state-space models with heavy-tailed noise. IEEE Transactions on Signal Processing 60(10), 5024–5037.
  2. Basar, T. and Bernhard, P. (1995). H^∞-Optimal Control and Related Minimax Design Problems: A Dynamic Game Approach. Systems & Control: Foundations & Applications.
  3. Gadsden, S. A. and Habibi, S. R. (2010). A new form of the smooth variable structure filter with a covariance derivation. In Proceedings of the 49th IEEE Conference on Decision and Control (CDC), pp. 7389–7394.
  4. Gadsden, S. A., Al-Shabi, M. and Habibi, S. R. (2011). Estimation strategies for the condition monitoring of a battery system in a hybrid electric vehicle. ISRN Signal Processing 2011, 120351.
  5. Hampel, F. R. (1974). The influence curve and its role in robust estimation. Journal of the American Statistical Association 69(346), 383–393.
  6. Huang, Y., Zhang, Y., Li, N. and Chambers, J. A. (2016). A robust Student's t based cubature filter. In Proceedings of the 19th International Conference on Information Fusion (FUSION), pp. 9–16.
  7. Huber, P. J. and Ronchetti, E. M. (2009). Robust Statistics. Wiley, 2nd ed..
  8. Kotz, S. and Nadarajah, S. (2004). Multivariate t Distributions and Their Applications. Cambridge University Press.
  9. Liu, W., Pokharel, P. P. and Principe, J. C. (2007). Correntropy: properties and applications in non-Gaussian signal processing. IEEE Transactions on Signal Processing 55(11), 5286–5298.
  10. Simon, D. and Chia, T. L. (2002). Kalman filtering with state equality constraints. IEEE Transactions on Aerospace and Electronic Systems 38(1), 128–136.
  11. Woodbury, M. A. (1950). Inverting Modified Matrices. Statistical Research Group Memorandum Report 42, Princeton University, 1–4.
  12. Yang, F., Wang, Z. and Hung, Y. S. (2002). Robust Kalman filtering for discrete time-varying uncertain systems with multiplicative noises. IEEE Transactions on Automatic Control 47(7), 1179–1183.

Smoothers (2)

  1. Bell, B. M. (1994). The iterated Kalman smoother as a Gauss–Newton method. SIAM Journal on Optimization 4(3), 626–636.
  2. Fraser, D. C. and Potter, J. E. (1969). The optimum linear smoother as a combination of two optimum linear filters. IEEE Transactions on Automatic Control 14(4), 387–390.

Joint / dual state–parameter estimation (2)

  1. Ljung, L. (1979). Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems. IEEE Transactions on Automatic Control 24(1), 36–50.
  2. Wan, E. A. and Nelson, A. T. (2001). Dual extended Kalman filter methods. In Kalman Filtering and Neural Networks, pp. 123–173.