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Dynamic event-triggered adaptive control for uncertain stochastic nonlinear systems

Published: 01 May 2023 Publication History

Highlights

A class of uncertain stochastic nonlinear systems with unknown parameter are firstly considered, which are more general and more practical than the nonlinear systems with the ODE form.
The dynamic event-triggered adaptive control of partially unknown stochastic nonlinear systems is are further studied. The structural properties of the system include both known and unknown information, and both parameter and function parametric and non-parametric uncertainties are contained. NNs are used to approximate the unknown nonlinear functions which are only parts of the overall nonlinear functions in the controllers and might have lower nonlinearity, less complexity and smaller magnitudes than the overall ones.
A DETM is put forward for uncertain stochastic nonlinear systems, which makes the interval between two consecutive triggered events larger and thus further saves the communication resources than SETM.

Abstract

In this paper, the dynamic event-triggered adaptive tracking control problem is investigated via backstepping technology for uncertain stochastic nonlinear systems. First, the stochastic nonlinear system with unknown parameter is considered. By introducing an additional dynamic variable, a dynamic event-triggered adaptive controller is designed such that the closed-loop signals are uniformly ultimately bounded in the sense of the fourth moment. Then, a more general partially unknown stochastic nonlinear system is further considered, and the designed adaptive neural network control scheme ensures that the closed-loop signals are fourth moment semi-globally uniformly ultimately bounded (SGUUB). The proposed dynamic event-triggering mechanism (DETM) guarantees that the lengths of time intervals between each two consecutive events are lower-bounded by a positive constant. It is necessary to point out that the DETM is better at saving resources than the static event-triggering mechanism (SETM). Finally, two simulations are conducted to show the validity of the control strategies for these two systems, respectively.

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Published In

cover image Applied Mathematics and Computation
Applied Mathematics and Computation  Volume 444, Issue C
May 2023
346 pages

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 May 2023

Author Tags

  1. Uncertain stochastic nonlinear system
  2. Dynamic event-triggering mechanism
  3. Adaptive control design
  4. Neural network
  5. Partially unknown system

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