Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 25, Issue 2, Pages 383-393Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2013.2275732
Keywords
Pseudoconvexity; recurrent neural networks; robust pole assignment; state and output feedback control; state estimation
Categories
Funding
- Research Grants Council of the Hong Kong Special Administrative Region [CUHK417209E, CUHK416811E, CUHK416812E]
- National Natural Science Foundation of China [61273307]
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This paper presents a neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems via state and output feedback. The problem is formulated as a pseudoconvex optimization problem with robustness measure: i.e., the spectral condition number as the objective function and linear matrix equality constraints for exact pole assignment. Two coupled recurrent neural networks are applied for solving the formulated problem in real time. In contrast to existing approaches, the exponential convergence of the proposed neurodynamics to global optimal solutions can be guaranteed even with lower model complexity in terms of the number of variables. Simulation results of the proposed neurodynamic approach for 11 benchmark problems are reported to demonstrate its superiority.
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