A Hebbian feedback covariance learning paradigm for self-tuning optimal control

2001 ◽  
Vol 31 (2) ◽  
pp. 173-186 ◽  
Author(s):  
D.L. Young ◽  
C.-S. Poon
Keyword(s):  
Optimal Control ◽  
Learning Paradigm ◽  
Self Tuning

scholarly journals A Simulation Study on the Optimal Control of Lock and Dam Gate Openings by the Self-Tuning Controller

1987 ◽  
Vol 31 ◽  
pp. 299-304
Author(s):  
Reynaldo R. Medina ◽  
Kenji Jinno ◽  
Toshihiko Ueda ◽  
Akira Kawamura
Keyword(s):  
Optimal Control ◽  
Simulation Study ◽  
The Self ◽  
Self Tuning

Self-Tuning Optimal Control of an Active Suspension

1989 ◽  
Author(s):  
Hung-Hsu Fred Chen ◽  
Dennis A. Guenther
Keyword(s):  
Optimal Control ◽  
Active Suspension ◽  
Self Tuning

Self-Tuning Control of a Coal-Fired Fluidized Bed Combustor

1992 ◽  
Vol 114 (1) ◽  
pp. 139-144
Author(s):  
K. W. Junk ◽  
R. R. Fullmer ◽  
R. C. Brown
Keyword(s):  
Optimal Control ◽  
Control Algorithm ◽  
Design Procedure ◽  
Gain Control ◽  
Recursive Least Squares ◽  
Self Tuning ◽  
Improved Performance ◽  
Secondary Air ◽  
Classical Control ◽  
Fluidized Bed Combustor

This paper investigates the temperature control of a two-bed fluidized combustor using a self-tuning control algorithm to vary secondary air flow rate. The controller consists of a recursive least-squares parameter estimator, an observer, and a linear optimal control design procedure. This combination enables the controller to estimate the system parameters and update the feedback gains when necessary. Further, this study addresses the tracking form of optimal control, accomplished by augmenting the state vector with an integrator. The self-tuning control algorithm was compared with a simple PI controller, which was tuned using the Ziegler-Nichols method. In this study, self-tuning control provided improved performance over classical control. Compared with conventional, constant-gain control, self-tuning control reduced steady-state variance by a factor of 6.67 while maintaining good tracking characteristics.

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