1- unversity of tabriz
2- iran university of science and technology
2- iran university of science and technology
Abstract: (24 Views)
This paper presents a new control method for MEMS gyroscopes with the aim of achieving high tracking accuracy and fast convergent, eliminating the chattering phenomenon, being robust to the presence of uncertainties and external disturbances, and also not requiring direct measurement of velocity states. In order to avoid increasing the volume of calculations and the complexity of the controller structure, novel fuzzy Chebyshev neural networks based intelligent adaptive high order sliding mode observer have been used to estimate the system states and external disturbances at the same time. Fuzzy Chebyshev neural network have been used, which simultaneously take the advantages of neural network, orthogonal polynomials and fuzzy logics estimation capabilities. The central part of the controller is composed of the nonsingular fast terminal sliding mode control method that ensures finite time stability, fast convergence and high tracking accuracy. In order to eliminate the chattering phenomenon the same fuzzy Chebyshev neural network used to adjust controller gains including sign function. The simulation results have been presented and compared with the results of previous related researches, which indicate the desired performance of the proposed control method in solving the challenges raised. Various control performance evaluation criteria have also been presented to numerically evaluate the quality of the proposed control method in comparison with numerous researches, which shows the very favorable efficiency of the proposed control method and the improvement of the results of the control methods presented in previous similar studies.
Keywords: This paper presents a new control method for MEMS gyroscopes with the aim of achieving high tracking accuracy and fast convergent, eliminating the chattering phenomenon, being robust to the presence of uncertainties and external disturbances, and also not
Type of Article: Research paper |
Subject:
Special
Received: 2023/12/6 | Accepted: 2025/04/19 | ePublished ahead of print: 2025/11/24
Received: 2023/12/6 | Accepted: 2025/04/19 | ePublished ahead of print: 2025/11/24
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