Observer-based control for time-varying delay neural networks with nonlinear observation

• Published in: Neural computing & applications Vol. 24; no. 7-8; pp. 1639 - 1645
• Main Authors: Phat, V. N., Fernando, T., Trinh, H.
• Format: Journal Article
• Language: English
• Published: London Springer London 01.06.2014; Springer
• Subjects: Applied sciences; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer science; control theory; systems; Connectionism. Neural networks; Control system analysis; Control system synthesis; Control theory. Systems; Data Mining and Knowledge Discovery; Exact sciences and technology; Image Processing and Computer Vision; Modelling and identification; Original Article; Probability and Statistics in Computer Science; Nonlinear observation; Interval time-varying delay; Lyapunov–Krasovskii functionals; Linear matrix inequalities; Neural networks; Observer-based control; State feedback; Feedback regulation; Control synthesis; Exponential stability; Lyapunov-Krasovskii functionals; Stabilizability; Time varying system; Observer; Linear matrix inequality; Controllability; Delay time; System identification; State estimation; Lower bound; Closed loop; Delay system; Neural network; Delay dependent system; Closed feedback; Feasibility; Lyapunov function
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-013-1388-9

This paper studies the problem of designing observer-based controllers for a class of delayed neural networks with nonlinear observation. The system under consideration is subject to nonlinear observation and an interval time-varying delay. The nonlinear observation output is any nonlinear Lipschitzian function and the time-varying delay is not required to be differentiable nor its lower bound be zero. By constructing a set of appropriate Lyapunov–Krasovskii functionals and utilizing the Newton–Leibniz formula, some delay-dependent stabilizability conditions which are expressed in terms of Linear Matrix Inequalities (LMIs) are derived. The derived conditions allow simultaneous computation of two bounds that characterize the exponential stability rate of the closed-loop system. The unknown observer gain and the state feedback observer-based controller are directly obtained upon the feasibility of the derived LMIs stabilizability conditions. A simulation example is presented to verify the effectiveness of the proposed result.