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Event-Triggered, Adaptive, Exponentially Asymptotic Tracking Control of Stochastic Nonlinear Systems
| Content Provider | MDPI |
|---|---|
| Author | He, Furong Cao, Dewen Wu, Jian Li, Jing |
| Copyright Year | 2022 |
| Description | This paper investigates the problem of event-triggered, adaptive, asymptotic tracking control for a class of non-strict feedback stochastic nonlinear systems with symmetrical structures and sensor faults. Based on the negative exponential function, the event-triggered adaptive tracking control strategy deals with the problem of exponentially asymptotic convergence for the first time. The radial basis function neural network (RBFNN) mechanism addresses uncertain factors and unknown external disturbances in the system. The developed strategy ensures that all the signals of the closed-loop system are semi-globally uniformly bounded in probability, and that the tracking error can exponentially converge to zero. Finally, a simulation example demonstrates the effectiveness of the proposed method. |
| Starting Page | 451 |
| e-ISSN | 20738994 |
| DOI | 10.3390/sym14030451 |
| Journal | Symmetry |
| Issue Number | 3 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-24 |
| Access Restriction | Open |
| Subject Keyword | Symmetry Automotive Engineering Stochastic Nonlinear Systems Event-triggered Mechanism Sensor Fault Exponentially Asymptotic Tracking |
| Content Type | Text |
| Resource Type | Article |
