access icon free Robust estimator design for periodic neural networks with polytopic uncertain weight matrices and randomly occurred sensor nonlinearities

© The Institution of Engineering and Technology
Received 17/10/2017, Accepted 12/02/2018, Revised 23/01/2018, Published 21/02/2018

This work addresses the problem of the estimator design for the periodic neural networks with polytopic uncertain connection weight matrices. The polytopic uncertainty is used to model the uncertain weight matrices. Bernoulli processes are employed to characterise the randomly occurred sensor nonlinearities, where the sensors are distributed in a large area. A Lyapunov function which depends both on the polytopic vertices and the period is constructed to improve the performance of the estimator. Sufficient conditions of the stochastic stability with performance for the augmented system are established, and the corresponding gains of the estimator are designed. Finally, an illustrative numerical example is given.

Inspec keywords: robust control; filtering theory; uncertain systems; control system synthesis; neurocontrollers; Lyapunov methods; matrix algebra

Other keywords: polytopic uncertain connection weight matrices; polytopic uncertain weight matrices; randomly occurred sensor nonlinearities; Lyapunov function; robust estimator design; polytopic vertices; sufficient conditions; periodic neural networks

Subjects: Stability in control theory; Neurocontrol; Algebra; Time-varying control systems; Control system analysis and synthesis methods

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