State-space pole-placing self-tuning regulator using input-output values

1982 ◽  
Vol 129 (4) ◽  
pp. 123 ◽  
Author(s):  
T. Hesketh
Keyword(s):  
State Space ◽  
Input Output ◽  
Self Tuning

Hybrid state-space self-tuning control using dual-rate sampling

1991 ◽  
Vol 138 (1) ◽  
pp. 50 ◽  
Author(s):  
Leang S. Shieh ◽  
Xiao M. Zhao ◽  
John W. Sunkel
Keyword(s):  
State Space ◽  
Hybrid State ◽  
Self Tuning

Parametrizations of All Wavelet Filters: Input-Output and State-Space

2013 ◽  
Vol 12 (2-3) ◽  
pp. 159-188
Author(s):  
Daniel Alpay ◽  
Palle Jorgensen ◽  
Izchak Lewkowicz
Keyword(s):  
State Space ◽  
Input Output ◽  
Wavelet Filters

scholarly journals Comparing global input-output behavior of frozen-equivalent LPV state-space models

2017 ◽  
Vol 50 (1) ◽  
pp. 9766-9771 ◽  
Author(s):  
Ziad Alkhoury ◽  
Mihály Petreczky ◽  
Guillaume Mercère
Keyword(s):  
State Space ◽  
State Space Models ◽  
Input Output ◽  
Output Behavior

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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