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Robust fractional adaptive control based on the strictly positive realness condition

Ladaci S., Charef A., Loiseau J. J.

International Journal of Applied Mathematics and Computer Science

2009

Vol. 19, no 1

69-76

This paper presents a new approach to robust adaptive control, using fractional order systems as parallel feedforward in the adaptation loop. The problem is that adaptive control systems may diverge when confronted with finite sensor and actuator dynamics, or with parasitic disturbances. One of the classical robust adaptive control solutions to these problems makes use of parallel feedforward and simplified adaptive controllers based on the concept of positive realness. The proposed control scheme is based on the Almost Strictly Positive Realness (ASPR) property of the plant. We show that this condition implies also robust stability in the case of fractional order controllers. An application to Model Reference Adaptive Control (MRAC) with a fractional order adaptation rule is provided with an implementable algorithm. A simulation example of a SISO robust adaptive control system illustrates the advantages of the proposed method in the presence of disturbances and noise.

Bayesian neural networks for prediction of response spectra

Waszczyszyn Z., Słoński M.

Foundations of Civil and Environmental Engineering

2006

No. 7

343-361

Standard artificial neural networks and Baycsian neural networks (BNNs) are briefly discussed on example of a simple feed-forward layered neural networks (FLNN). Main ideas of the Baycsian approach and basics of the applied BNNs are presented in short. A study case corresponds to prediction of Displacement Response Spectrum inside buildings at the basement level (DRSb). Data for network training and testing were adopted as DRS corresponding to the preprocessed accelerograms. They were taken from measurements in the Lcgnica-Gtogow Copperfield at monitored 5-storey buildings subjected to paraseismic excitations from explosives in nearby strip mines. Results of neural predictions by three NNs (standard FLNN trained by means of the conjugate gradient learning method, Simple Bayesian SBNN and Full Bayesian FBNN) are presented. The errors of predictions are on average on the level of 4% errors.

Optimal design of model following control with genetic algorithm

Zhang X., Yamane Y.

Control and Cybernetics

2001

Vol. 30, no 1

71-79

This paper presents a genetic algorithm for the optimal design of model following control in which there are nonlinear disturbance and unceratin parameters, where the output is regulated to follow the output of reference model. The effectiveness of the proposed algorithm is illustrated by numerical examples.