A rational spline model approximation and control of output probability density functions for dynamic stochastic systems

Transactions of the Institute of Measurement & Control

Volumn 25, Issue 2, 2003, Pages 93-105

  Wang, Hong ^a   Yue, Hong ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

B spline neural network; dynamic stochastic system; probability density function; robust control

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; NEURAL NETWORKS; NONLINEAR CONTROL SYSTEMS; PROBABILITY DENSITY FUNCTION; ROBUSTNESS (CONTROL SYSTEMS); SPLINES; PROBABILITY DISTRIBUTIONS; RATIONAL FUNCTIONS; ROBUST CONTROL; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

DYNAMIC STOCHASTIC SYSTEM; B-SPLINE NEURAL NETWORK; DYNAMIC STOCHASTIC SYSTEMS; MODEL APPROXIMATIONS; MODELING AND CONTROL; MODELLING AND CONTROLS; NON-LINEAR CONTROLLERS; RATIONAL SPLINES; TRACKING PERFORMANCE;

STOCHASTIC CONTROL SYSTEMS; PROBABILITY DENSITY FUNCTION;

EID: 0037986590     PISSN: 01423312     EISSN: None     Source Type: Journal    
DOI: 10.1191/0142331203tm076oa     Document Type: Article

References (16)

1. Anderson, B. D. O. and Moore, J. B. 1971: Linear optimal control. Englewood Cliffs, New Jersey: Prentice-Hall.
2. Astrom, K. J. 1970: Introduction to stochastic control theory. New York: Academic Press.
3. Astrom, K. J. and Wittenmark, B. 1973: On self-tuning regulators. Automatica 9, 466-478.
4. Astrom, K. J. and Wittenmark, B. 1980: Self-tuning controllers based on pole-zero placement. Proceedings of IEE, Part D: Control Theory and Applications 127, 120-130.
5. Dennis, J. E. 1977: Nonlinear least square. In Jacobs, D., editor, State of the art in numerical analysis. New York: Academic Press.
6. Dennis, J. E. and Schnabel, R. B. 1983: Numerical methods for unconstrained optimization and nonlinear equations. Englewood Cliffs, New Jersey: Prentice-Hall.
7. Gajic, Z. R. and Losada, R. 1998: Monotonicity of algebraic Lyapunov iterations for optimal control of jump parameter linear systems. Proceeding of the 1998 American Control Conference, Pennsylvania, USA, 1, 746-750.
8. Goodwin, C. C. and Sin, K. S. 1984: Adaptive filtering, prediction and control. Englewood Cliffs, New Jersey: Prentice Hall.
9. Kalman, R. E. and Bucy, R. S. 1961: New results in linear filtering and prediction theory. Transactions of the ASME, Series D, Journal of Basic Engineering 83, 95-107.
10. Karny, M. 1996: Towards fully probabilistic control design. Automatica 32, 1719-1722.
11. Wang, H. 1999: Robust control of output probability density functions for multivariable stochastic systems with guaranteed stability. IEEE Transactions on Automatic Control 44, 2103-2107.
12. Wang, H. 2000: Bounded dynamic stochastic systems: modelling and control. London: Springer-Verlag.
13. Wang, H. 2002: Minimum entropy control of non-Gaussian dynamic stocastic systems. IEEE Transactions on Automatic Control 47, 398-403.
14. Wang, H., Baki, H. and Kabore, P. 2001: Control of bounded dynamic stochastic distributions using square root models: an applicability study in papermaking systems. Transactions of the Institute of Measurement and Control 23, 51-68.
15. Wang, H. and Zhang, J. H. 2001: Bounded stochastic distribution control for pseudo ARMAX stochastic systems. IEEE Transactions on Automatic Control 46, 486-490.
16. Wellstead, P. E. and Zarrop, M. B. 1991: Self-tuning systems, control and signal processing. Chichester: John Wiley & Sons.