4.7 Article

Quantized feedback control strategy for tracking performance guarantee of nonholonomic mobile robots with uncertain nonlinear dynamics

Journal

APPLIED MATHEMATICS AND COMPUTATION
Volume 407, Issue -, Pages -

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1016/j.amc.2021.126349

Keywords

State quantization; Quantized feedback design; Predesignated performance guarantee; Nonholonomic mobile robots; Uncertain nonlinear dynamics

Funding

  1. National Research Foundation of Korea (NRF) - Korea government [NRF-2019R1A2C1004898, NRF2019R1A2C1087552]

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This paper discusses the quantized feedback tracker design problem of nonholonomic mobile robots with uncertain nonlinear dynamics in a network environment, proposing low-complexity tracking and stability methodologies based on quantized states to ensure predesignated performance of tracking errors. The research provides a robust tracking scheme using quantized state variables and analyzes the closed-loop stability with a performance guarantee of tracking errors in the Lyapunov sense.
This paper discusses a quantized feedback tracker design problem of nonholonomic mobile robots with uncertain nonlinear dynamics in a network environment with state and input quantization. Quantized state feedback information of mobile robots is only used for the tacker design. Compared with existing control approaches for uncertain nonholonomic mobile robots, the primary contribution of our study is to develop quantized-states-based low-complexity tracking and stability methodologies for ensuring the predesignated performance of tracking errors. A robust tracking scheme using quantized state variables is designed without any adaptive mechanisms to compensate for nonlinear dynamic uncertainties. The boundedness of the quantization errors of the closed-loop signals is derived from a theoretical lemma. Using this lemma, the closed-loop stability is analyzed with the predesignated performance guarantee of tracking errors in the Lyapunov sense. A robot simulation verifies the resulting theoretical tracking strategy. (C) 2021 Elsevier Inc. All rights reserved.

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