Estimation of Domain of Attraction for Aperiodic Sampled-Data Switched Delayed Neural Networks Subject to Actuator Saturation

• Published in: IEEE transaction on neural networks and learning systems Vol. 31; no. 5; pp. 1489 - 1503
• Main Authors: Chen, Guoliang, Sun, Jian, Xia, Jianwei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuator saturation; Actuators; aperiodic sampled-data switched delayed neural networks (ASDSDNNs); Artificial neural networks; Attraction; Computer simulation; Control theory; domain of attraction; Domains; Dwell time; Interpolation; Linear matrix inequalities; Mathematical models; Multiple objective analysis; Neural networks; Optimization; Sampling; Saturation; Segmentation; Stability criteria; Subsystems; Switches; Switching; Symmetric matrices; Synchronization; Time dependence; time-scheduled Lyapunov functionals
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2019.2920665

In this paper, for the case of the asynchronous switching caused by that subsystem's switching occuring during a sampling interval, the domain of attraction estimation problem is investigated for aperiodic sampled-data switched delayed neural networks (ASDSDNNs) subject to actuator saturation. A parameters-dependent time-scheduled Lyapunov functional consisting of a novel looped-functional is constructed using segmentation technology and linear interpolation. By employing this novel functional and using an average dwell time (ADT) approach, exponential stability criteria are proposed for polytopic uncertain ASDSDNNs subject to actuator saturation. And a relationship between ADT and sampling period is revealed for ASDSDNNs. As a corollary, exponential stability criteria are proposed for nominal ASDSDNNs subject to actuator saturation. Furthermore, by describing the domain of attraction as a time-varying ellipsoid determined by the time-scheduled Lyapunov matrix, the proposed theoretical conditions are transformed into a linear matrix inequality (LMI)-based multi-objective optimization problem. The dynamic estimates of the domain of attraction for ASDSDNNs are solved. Numerical simulation examples are provided to illustrate the effectiveness of the proposed method.