Model transformations for state–space self–tuning control of multivariable stochastic systems

1988 ◽  
Vol 6 (1) ◽  
pp. 91-101
Author(s):  
Leang S. Sheih ◽  
Yuan L. Bao ◽  
Norman P. coleman
Keyword(s):  
State Space ◽  
Stochastic Systems ◽  
Model Transformations ◽  
Self Tuning

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.

scholarly journals An Active Fault-Tolerant PWM Tracker for Unknown Nonlinear Stochastic Hybrid Systems: NARMAX Model and OKID-Based State-Space Self-Tuning Control

2010 ◽  
Vol 2010 ◽  
pp. 1-27 ◽  
Author(s):  
Chu-Tong Wang ◽  
Jason S. H. Tsai ◽  
Chia-Wei Chen ◽  
You Lin ◽  
Shu-Mei Guo ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
Active Fault ◽  
Stochastic Systems ◽  
Fault Tolerant ◽  
Initial Guess ◽  
Process Time ◽  
Parameter Estimates ◽  
Unknown Parameters ◽  
Self Tuning

An active fault-tolerant pulse-width-modulated tracker using the nonlinear autoregressive moving average with exogenous inputs model-based state-space self-tuning control is proposed for continuous-time multivariable nonlinear stochastic systems with unknown system parameters, plant noises, measurement noises, and inaccessible system states. Through observer/Kalman filter identification method, a good initial guess of the unknown parameters of the chosen model is obtained so as to reduce the identification process time and enhance the system performances. Besides, by modifying the conventional self-tuning control, a fault-tolerant control scheme is also developed. For the detection of fault occurrence, a quantitative criterion is exploited by comparing the innovation process errors estimated by the Kalman filter estimation algorithm. In addition, the weighting matrix resetting technique is presented by adjusting and resetting the covariance matrix of parameter estimates to improve the parameter estimation for faulty system recovery. The technique can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection.

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

scholarly journals Measurement Feedback Self-Tuning Weighted Measurement Fusion Kalman Filter for Systems with Correlated Noises

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Xin Wang ◽  
Shu-Li Sun
Keyword(s):  
Kalman Filter ◽  
Stochastic Systems ◽  
Tracking System ◽  
Weighted Least Squares ◽  
Full Rank ◽  
Global Optimality ◽  
Measurement Feedback ◽  
Innovation Model ◽  
Self Tuning ◽  
Noise Statistics

For the linear discrete stochastic systems with multiple sensors and unknown noise statistics, an online estimators of the noise variances and cross-covariances are designed by using measurement feedback, full-rank decomposition, and weighted least squares theory. Further, a self-tuning weighted measurement fusion Kalman filter is presented. The Fadeeva formula is used to establish ARMA innovation model with unknown noise statistics. The sampling correlated function of the stationary and reversible ARMA innovation model is used to identify the noise statistics. It is proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion Kalman filter, which means its asymptotic global optimality. The simulation result of radar-tracking system shows the effectiveness of the presented algorithm.

scholarly journals Ruelle–Pollicott Resonances of Stochastic Systems in Reduced State Space. Part I: Theory

2020 ◽  
Vol 179 (5-6) ◽  
pp. 1366-1402 ◽  
Author(s):  
Mickaël D. Chekroun ◽  
Alexis Tantet ◽  
Henk A. Dijkstra ◽  
J. David Neelin
Keyword(s):  
State Space ◽  
Stochastic Systems ◽  
Reduced State

scholarly journals State space based dual-rate self-tuning

Automatica ◽  
1994 ◽  
Vol 30 (12) ◽  
pp. 1999-2007
Author(s):  
M.S. Ahmed
Keyword(s):  
State Space ◽  
Self Tuning

On Self-Tuning Control of Nonminimum Phase Discrete-Time Systems Using Approximate Inverse Systems

1993 ◽  
Vol 115 (1) ◽  
pp. 12-18 ◽  
Author(s):  
Takashi Yahagi ◽  
Jianming Lu
Keyword(s):  
Discrete Time ◽  
Stochastic Systems ◽  
Output Data ◽  
Approximate Inverse ◽  
Nonminimum Phase ◽  
Discrete Time Systems ◽  
Inverse Systems ◽  
Self Tuning ◽  
The Stability ◽  
Time Systems

This paper presents a new method for self-tuning control of nonminimum phase discrete-time stochastic systems using approximate inverse systems obtained from the least-squares approximation. We show how unstable pole-zero cancellations can be avoided, and that this method has the advantage of being able to determine an approximate inverse system independently of the plant zeros. The proposed scheme uses only the available input and output data and the stability using approximate inverse systems is analyzed. Finally, the results of computer simulation are presented to show the effectiveness of the proposed method.

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