A LOOK AT A BILINEAR MODEL FOR MULTIDIMENSIONAL STOCHASTIC SYSTEMS IN CONTINUOUS TIME

1983 ◽  
Vol 1 (3) ◽  
Author(s):  
Alain Le Breton ◽  
Marek Musiela
Keyword(s):  
Continuous Time ◽  
Stochastic Systems ◽  
Bilinear Model

Open-loop optimal control of a class of continuous-time stochastic systems—A simulation study†

1974 ◽  
Vol 19 (6) ◽  
pp. 1165-1175 ◽  
Author(s):  
EDGAR C. TACKER ◽  
THOMAS D. LINTON ◽  
CHARLES W. SANDERS
Keyword(s):  
Optimal Control ◽  
Simulation Study ◽  
Continuous Time ◽  
Stochastic Systems ◽  
Open Loop

STATE-SPACE SELF-TUNING CONTROL FOR NONLINEAR STOCHASTIC AND CHAOTIC HYBRID SYSTEMS

2001 ◽  
Vol 11 (04) ◽  
pp. 1079-1113 ◽  
Author(s):  
SHU-MEI GUO ◽  
LEANG-SAN SHIEH ◽  
CHING-FANG LIN ◽  
JAGDISH CHANDRA
Keyword(s):  
State Space ◽  
Hybrid Systems ◽  
Continuous Time ◽  
Digital Control ◽  
Stochastic Systems ◽  
Moving Average ◽  
Computation Time ◽  
Sampling Period ◽  
Noise Model ◽  
Self Tuning

This paper presents a new state-space self-tuning control scheme for adaptive digital control of continuous-time multivariable nonlinear stochastic and chaotic systems, which have unknown system parameters, system and measurement noises, and inaccessible system states. Instead of using the moving average (MA)-based noise model commonly used for adaptive digital control of linear discrete-time stochastic systems in the literature, an adjustable auto-regressive moving average (ARMA)-based noise model with estimated states is constructed for state-space self-tuning control of nonlinear continuous-time stochastic systems. By taking advantage of a digital redesign methodology, which converts a predesigned high-gain analog tracker/observer into a practically implementable low-gain digital tracker/observer, and by taking the non-negligible computation time delay and a relatively longer sampling period into consideration, a digitally redesigned predictive tracker/observer has been newly developed in this paper for adaptive chaotic orbit tracking. The proposed method enables the development of a digitally implementable advanced control algorithm for nonlinear stochastic and chaotic hybrid systems.

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