Affiliation:
1. Faculty of Science Kunming University of Science and Technology Kunming China
2. Data Science Research Center, Faculty of Science Kunming University of Science and Technology Kunming China
Abstract
AbstractIn this article, we develop a new adaptive event‐triggered asymptotic control scheme for strict‐feedback systems with fast time‐varying parameters. To deal with time‐varying parameters with unknown variation boundaries in the feedback path and the input path, we construct three adaptive laws for parameter estimation, two for the uncertain parameters in the feedback path and one for the uncertain parameters in the input path. In particular, two sets of tuning functions are introduced to avoid over‐parametrization. Additionally, an event‐triggering mechanism is embedded in this adaptive control framework to reduce the data transmission from the controller to the actuator. We also introduce a soft sign function to handle the perturbations caused by sampling errors to achieve asymptotic stability and avoid the so‐called parameter drift. The stability analysis shows that the closed‐loop system is globally uniformly asymptotically stable and the Zeno behavior can be excluded. Simulation results verify the effectiveness and performance of the proposed adaptive scheme.
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanical Engineering,Aerospace Engineering,Biomedical Engineering,General Chemical Engineering,Control and Systems Engineering