References
- Alamo, T., Bravo, J., & Camacho, E. (2005). Guaranteed state estimation by zonotopes. Automatica, 41(6), 1035–1043.
- Arulampalam, M.S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–188.
- Blesa, J., Puig, V., & Saludes, J. (2011). Identification for passive robust fault detection using zonotope-based set-membership approaches. International Journal of Adaptive Control and Signal Processing, 25(9), 788–812.
- Blesa, J., Puig, V., & Saludes J. (2012). Robust fault detection using polytope-based set-membership consistency test. IET Control Theory & Applications, 6(12), 1767–1777.
- Bolstad, W.M. (2010). Understanding computational Bayesian statistics. Hoboken, NJ: John Wiley.
- Campi, M.C., Calafiore, G., & Garatti, S. (2009). Interval predictor models: Identification and reliability. Automatica, 45(8), 382–392.
- Chen, J., & Patton R. (1999). Robust model-based fault diagnosis for dynamic systems. Boston, MA: Kluwer Academic .
- Eykhoff, P. (1974). System identification parameter and state estimation. Chichester: John Wiley.
- Fernandez-Canti, R.M., Tornil-Sin, S., Blesa, J., & Puig, V. (2013). Nonlinear set-membership identification and fault detection using a Bayesian framework: Application to the wind turbine benchmark. IEEE 52nd Annual Conference on Decision and Control (CDC), Florence, Italy.
- Garulli, A., & Reinelt, W. (2000). On model error modelling in set membership identification. Proceeding of the SYSID.
- Goodwin, G.C., Braslavsky, J.H., & Seron, M.M. (2002). Non-stationary stochastic embedding for transfer function estimation. Automatica, 38(1), 47–62.
- Ingimundarson, A., Puig, V., Álamo, T., Bravo, J.M, & Guerra, P. (2008). Robust fault detection using zonotope-based set-membership consistency test. Journal of Adaptive Control and Signal Processing, 23(4), 311–330.
- Jaulin, L. (2010). Probabilistic set-membership approach for robust regression. Journal of Statistical Theory and Practice, 4(1) 155–167.
- Jaulin, L., Kieffer, M., Didrit O., & Walter, E. (2001). Applied interval analysis with examples in parameter and state estimation, Robust Control and Robotics. London: Springer-Verlag.
- Johansson, K.H. (2000). The quadruple-tank process: A multivariable laboratory process with an adjustable zero. IEEE Transactions on Control Systems Technology, 8(3), 456–465.
- Lagoa, C.M., Li, X., & Sznaier, M. (2005). Probabilistically constrained linear programs and risk-adjusted controller design. SIAM Journal on Optimization, 15(3), 938–951.
- Milanese, M., Norton, J.P., Piet-Lahanier, H., & Walter, E. (1996). Bounding approaches to system identification. New York, NY: Plenum Press.
- Milanese, M., & Taragna, M. (2002). Optimality, approximation, and complexity in set membership H∞ identification. IEEE Transactions on Automatic Control, 47(10), 1682–1690.
- Milanese, M., & Taragna, M. (2005). H∞ set membership identification: A survey. Automatica, 41(12), 2019–2032.
- Ninness, B., & Goodwin, G.C. (1995). Rapprochement between bounded-error and stochastic estimation theory. International Journal of Adaptive Control and Signal Processing, 9(1), 107–132.
- Ninness, B., & Henriksen, S. (2010). Bayesian system identification via Markov chain Monte Carlo techniques. Automatica, 46, 40–51.
- Peterka, V. (1981). Bayesian system identification. Automatica, 17, 41–53.
- Reinelt, W., Garulli, A., & Ljung, L. (2002). Comparing different approaches to model error modelling in robust identification. Automatica, 38 (5), 787–803.
- Reppa, V., & Tzes, A. (2011). Fault detection and diagnosis based on parameter set estimation. IET Control Theory and Applications, 5, 69–83.
- Robert, C.P. (2001). The Bayesian choice, In Springer texts in statistics (2nd ed.). New York, NY: Springer Verlag.
- Sánchez Peña, R.S., & Sznaier, M. (1998). Robust systems theory and applications. New York, NY: John Wiley & Sons.
- Schön, T.B., Wills, A., & Ninness, B. (2011). System identification of nonlinear state-space models. Automatica, 47, 39–49.
- Sorenson, H.W. (1970). Least-squares estimation: From Gauss to Kalman. IEEE Spectrum, 7(7), 63–68.
- Verma, V., Gordon, G., Simmons, R., & Thrun, S. (2004). Real-time fault diagnosis. IEEE Robotics and Automation Magazine, 11(2), 56–66.
- Ziegler, G.M. (1995). Lectures on polytopes graduate texts in mathematics 152. New York, NY: Springer-Verlag.