Constrained state estimation for stochastic jump systems: moving horizon approach

References

• Al-Matouq, A.A., & Vincent, T.L. (2015). Multiple window moving horizon estimation. Automatica, 53, 264–274.
   Web of Science ®Google Scholar
• Alessandri, A., Baglietto, M., & Battistelli, G. (2005). Receding-horizon estimation for switching discrte-time linear systems. IEEE Transactions on Automatic Control, 50(11), 1736–1748.
   Web of Science ®Google Scholar
• Alessandri, A., Baglietto, M., & Battistelli, G. (2008). Moving-horizon state estimation for nonlinear discrete-time systems: New stability results and approximation schemes. Automatica, 44(7), 1753–1765.
   Web of Science ®Google Scholar
• Blom, H.A.P., & Bar-Shalom, Y. (1988). The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Transactions on Automatic Control, 33(8), 780–783.
   Web of Science ®Google Scholar
• Chen, C.M., Guan, D.J., Huang, Y.Z., & Ou, Y.H. (2016). Anomaly network intrusion detection using hidden Markov model. International Journal of Innovative Computing, Information and Control, 12(2), 569–580.
   Google Scholar
• Chen, H., Shi, P., Lim, C.C., & Hu, P. (2016). Exponential stability for neutral stochastic Markov systems with time-varying delay and its applications. IEEE Transactions on Cybernetics, 46(6), 1350–1362.
   PubMed Web of Science ®Google Scholar
• Choi, J., & Lim, C.C. (2008). A Cholesky factorization based approach for blind FIR channel identification. IEEE Transactions on Signal Processing, 56(4), 1730–1735.
   Web of Science ®Google Scholar
• Costa, O.L.V., & Fragoso, M.D. (1993). Stability results for discrete-time linear systems with Markovian jumping parameters. Journal of Mathematical Analysis and Applications, 179(1), 154–178.
   Web of Science ®Google Scholar
• Costa, O.L.V., & Guerra, S. (2002). Stationary filter for linear minimum mean square error estimator of discrete-time Markovian jump systems. IEEE Transactions on Automatic Control, 47(8), 1351–1356.
   Web of Science ®Google Scholar
• Darby, M.L., & Nikolaou, M. (2007). A parametric programming approach to moving-horizon state estimation. Automatica, 43(5), 885–891.
   Web of Science ®Google Scholar
• Ferrari-Trecate, G., Mignone, D., & Morari, M. (2002). Moving horizon estimation for hybrid systems. IEEE Transactions on Automatic Control, 47(11), 1663–1676.
   Web of Science ®Google Scholar
• Ghahramani, Z. (2001). An introduction to hidden Markov models and Bayesian networks. International Journal of Pattern Recognition and Artificial Intelligence, 15(1), 9–42.
   Web of Science ®Google Scholar
• Guo, Y., & Huang, B. (2013). Moving horizon estimation for switching nonlinear systems. Automatica, 49(11), 3270–3281.
   Web of Science ®Google Scholar
• Hochbaum, D.S. (2007). Complexity and algorithms for nonlinear optimization problems. Annals of Operations Research, 153, 257–296.
   Web of Science ®Google Scholar
• Huang, H., Long, F., & Li, C. (2015). Stabilization for a class of Markovian jump linear systems with linear fractional uncertainties. International Journal of Innovative Computing, Information and Control, 11(1), 295–307.
   Web of Science ®Google Scholar
• Li, F., Shi, P., Wu, L., Basin, M.V., & Lim, C.C. (2015). Quantized control design for cognitive radio networks modeled as nonlinear semi-Markovian jump systems. IEEE Transactions on Industrial Electronics, 62(4), 2330–2340.
   Web of Science ®Google Scholar
• Lim, C.C., & Teo, K.L. (1991). Optimal insulin infusion control via a mathematical blood glucoregulatory model with fuzzy parameters. Cybernetics and Systems, 22(1), 1–16.
   Web of Science ®Google Scholar
• Logothetis, A., & Krishnamurthy, V. (1999). Expectation maximization algorithms for MAP estimation of jump Markov linear systems. IEEE Transactions on Signal Processing, 47(8), 2139–2156.
   Web of Science ®Google Scholar
• Lopez-Negrete, R., Patwardhan, S.C., & Biegler, L.T. (2011). Constrained particle filter approach to approximate the arrival cost in moving horizon estimation. Journal of Process Control, 21(6), 909–919.
   Web of Science ®Google Scholar
• Orguner, U. (2006). Improved state estimation for jump Markov linear systems ( Doctoral dissertation). Middle East Technical University, Ankara, Turkey.
   Google Scholar
• Petrov, A.I., & Zubov, A.G. (2002). On applicability of the interacting multiple-model approach to state estimation for systems with sojourn-time-dependent Markov model switching. IEEE Transactions on Automatic Control, 41(1), 136–140.
   Web of Science ®Google Scholar
• Rao, C.V., & Rawlings, J.B. (2002a). Constrained process monitoring: Moving-horizon approach. AIChE Journal, 48(1), 97–109.
   Web of Science ®Google Scholar
• Rao, C.V., & Rawlings, J.B. (2000b). Nonlinear moving horizon state estimation. Nonlinear Mode Predictive Control, Progress in Systems and Control Theory, 26, 45–69.
   Google Scholar
• Rao, C.V., Rawlings, J.B., & Maynei, 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.
   Web of Science ®Google Scholar
• Robertson, D. (1996). Development and statistical Interpretation of tools for nonlinear estimation ( Doctoral dissertation). Auburn University, Auburn, AL.
   Google Scholar
• Robertson, D.G., Lee, J.H., & Rawlings, J.B. (1996). A moving horizon-based approach for least-squares estimation. AIChE Journal, 42(8), 2209–2224.
   Web of Science ®Google Scholar
• Shen, Q., Shi, P., Zhang, T., & Lim, C.C. (2014). Novel neural control for a class of uncertain pure-feedback systems. IEEE Transactions on Neural and Learning Systems, 25(4), 718–727.
   PubMed Web of Science ®Google Scholar
• Shi, P., Liu, M., & Zhang, L. (2015). Fault-tolerant sliding-mode-observer synthesis of Markovian jump systems using quantized measurements. IEEE Transactions on Industrial Electronics, 62(9), 5910–5918.
   Web of Science ®Google Scholar
• Simon, D., & Simon, D.L. (2005). Aircraft turbofan engine health estimation using constrained Kalman filtering. Journal of Engineering for Gas Turbines and Power, 127, 323–328.
   Web of Science ®Google Scholar
• Tugnait, J.K. (1982). Detection and estimation for abruptly changing systems. Automatica, 18(5), 607–615.
   Web of Science ®Google Scholar
• Zhang, L. (2009). H∞ estimation for discrete-time piecewise homogeneous Markov jump linear systems. Automatica, 45(11), 2570–2576.
   Web of Science ®Google Scholar