Neurodynamics-based robust pole assignment for synthesizing second-order control systems via output feedback based on a convex feasibility problem reformulation

Publication Type:
Conference Proceeding
Citation:
INISTA 2014 - IEEE International Symposium on Innovations in Intelligent Systems and Applications, Proceedings, 2014, pp. 1 - 6
Issue Date:
2014-01-01
Full metadata record
Files in This Item:
Filename Description Size
neu.pdfPublished version946.91 kB
Adobe PDF
A neurodynamic optimization approach is proposed for robust pole assignment problem of second-order control systems via output feedback. With a suitable robustness measure serving as the objective function, the robust pole assignment problem is formulated as a quasi-convex optimization problem with linear constraints. Next, the problem further is reformulated as a convex feasibility problem. Two coupled recurrent neural networks are applied for solving the optimization problem with guaranteed optimality and exact pole assignment. Simulation results are included to substantiate the effectiveness of the proposed approach. © 2014 IEEE.
Please use this identifier to cite or link to this item: