Adaptive tracking of a class of first-order systems with binary-valued observations and fixed thresholds

1. J. C. Agüero, G. C. Goodwin, and J. I. Yuz, System identification using quantized data, Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, USA, 2007.

2. M. Casini, A. Garulli, and A. Vicino, Time complexity and input design in worst-case identification using binary sensors, Proceedings of the 46th IEEE Conference on Decision and Control, New Orleans, USA, 2007.

3. B. I. Godoya, G. C. Goodwin, J. C. Agüero, D. Marelli, and T. Wigrenb, On identification of FIR systems having quantized output data, Automatica, 2011, 47: 1905–1915.

   Article  Google Scholar 

4. J. Guo, J. F. Zhang, and Y. L. Zhao, Adaptive tracking control of a class of first-order systems with binary-valued observations and time-varying thresholds, IEEE Trans. on Automatic Control, 2011, 56(12): 2991–2996.

   Article  MathSciNet  Google Scholar 

5. D. Marelli, K. You, and M. Fu, Identification of ARMA models using intermittent and quantized output observations, Proceedings of the 36th International Conference on Acoustics, Speech and Signal Processing, Prague, Czech Republic, 2011.

6. L. Y. Wang, G. Yin, J. F. Zhang, and Y. L. Zhao, System Identification with Quantized Observations, Boston: Birkhüser, 2010.

   Book  MATH  Google Scholar 

7. L. Y. Wang, J. F. Zhang, and G. Yin, System identification using binary sensors, IEEE Trans. on Automatic Control, 2003, 48: 1892–1907.

   Article  MathSciNet  Google Scholar 

8. K. You, L. H. Xie, S. Sun, and W. Xiao, Multiple-level quantized innovation Kalman filter, Proceedings of the 17th IFAC World Congress, Korea, July 6–11, 2008.

9. Y. L. Zhao, J. Guo, and J. F. Zhang, Adaptive tracking control of linear systems to periodic target with set-valued information, Proceedings of the 30th Chinese Control Conference, Yantai, July 22–24, 2011.

10. L. Y. Wang, G. H. Xu, and G. Yin, State reconstruction for linear time-invariant systems with binary-valued output observations, Systems & Control Letters, 2008, 57: 958–963.

    Article  MathSciNet  MATH  Google Scholar 

11. L. Y. Wang, Y. Kim, and J. Sun, Prediction of oxygen storage capacity and stored NOx using HEGO sensor model for improved LNT control strategies, Proceedings of ASME International Mechanical Engineering Congress and Exposition, New Orleans, November 17–22, 2002.

12. I. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, Wireless sensor networks: A survey, Computer Networks, 2002, 38: 393–422.

    Article  Google Scholar 

13. N. Dokuchaev and A. Savkin, A new class of hybrid dynamical systems: state estimators with bit-rate constraints, International Journal of Hybrid Systems, 2001, 1(1): 33–50.

    Google Scholar 

14. A. Ribeiro, G. Giannakis, and S. Roumeliotis, SOI-KF: Distributed Kalman filtering with low-cost communications using the sign of innovations, IEEE Trans. on Signal Processing, 2006, 54(12): 4782–4795.

    Article  Google Scholar 

15. L. Y. Wang, C. Y. Li, G. Yin, L. Guo, and C. Z. Xu, State observability and observers of lineartime-invariant systems under irregular sampling and sensor limitations, IEEE Trans. Automatic Control, 2011, 56(11): 2639–2654.

    Article  MathSciNet  Google Scholar 

16. Y. L. Zhao and J. Guo, Quantization system identification under a class of persistent excitations, Proceedings of the 29th Chinese Control Conference, Beijing, July 29–31, 2010.

17. L. Guo, Time-Varying Stochastic Systems — Stability, Estimation and Control, Jilin Science and Technology Press, Changchun, China, 1993.

    Google Scholar 

18. Y. S. Chow and H. Teicher, Probability Theory: Independence, Interchangeability, Martingales, Springer-Verlag, New York, 3rd Edition, 1997.

    MATH  Google Scholar 

19. H. F. Chen, Stochastic Approximation and Its Application, Kluwer Academic Publishers, Dordrecht, 2002.

    Google Scholar 

Download references