TY - JOUR
T1 - Linear quadratic Nash game-based tracker for multiparameter singularly perturbed sampled-data systems
T2 - Digital redesign approach
AU - Tsai, Jason Sheng Hong
AU - Yang, Zi Yi
AU - Guo, Shu Mei
AU - Shieh, Leang San
AU - Chen, Chia Wei
N1 - Funding Information:
This work was supported by the National Science Council of Republic of China under contract NSC95-2221-E-006-109, NSC95-2221-E-006-362 the TXDOT under contract 466PVIA003 and the U.S. Army Research Office under grant W911NF-06-1-0507.
PY - 2007/12
Y1 - 2007/12
N2 - In this paper, a linear quadratic Nash game-based tracker for multiparameter singularly perturbed sample-data systems is developed. A generalized cross-coupled multiparameter algebraic Riccati equation (GCMARE) with two quadratic cost functions is solved by applying the LQR design methodology for the optimal tracker design. Firstly, the asymptotic expansions of the GCMARE are newly established, and the proposed algorithm is able to effectively solve the GCMARE with the quadratic convergence rate. Then, the low-gain digital controller with a high design performance is realized through the prediction-based digital redesign method. Finally, for further improving the tracking performance, the chaos-evolutionary-programming algorithm (CEPA) is utilized to optimally tune the parameters of the tracker. An example is presented to demonstrate the effectiveness of the proposed methodology.
AB - In this paper, a linear quadratic Nash game-based tracker for multiparameter singularly perturbed sample-data systems is developed. A generalized cross-coupled multiparameter algebraic Riccati equation (GCMARE) with two quadratic cost functions is solved by applying the LQR design methodology for the optimal tracker design. Firstly, the asymptotic expansions of the GCMARE are newly established, and the proposed algorithm is able to effectively solve the GCMARE with the quadratic convergence rate. Then, the low-gain digital controller with a high design performance is realized through the prediction-based digital redesign method. Finally, for further improving the tracking performance, the chaos-evolutionary-programming algorithm (CEPA) is utilized to optimally tune the parameters of the tracker. An example is presented to demonstrate the effectiveness of the proposed methodology.
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U2 - 10.1080/03081070701441205
DO - 10.1080/03081070701441205
M3 - Article
AN - SCOPUS:36448938367
SN - 0308-1079
VL - 36
SP - 643
EP - 672
JO - International Journal of General Systems
JF - International Journal of General Systems
IS - 6
ER -