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Data Driven Model-Free Adaptive Control Method for Quadrotor Trajectory Tracking Based on Improved Sliding Mode Algorithm

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Abstract

In order to solve the problems of dynamic modeling and complicated parameters identification of trajectory tracking control of the quadrotor, a data driven model-free adaptive control method based on the improved sliding mode control (ISMC) algorithm is designed, which does not depend on the precise dynamic model of the quadrotor. The design of the general sliding mode control (SMC) algorithm depends on the mathematical model of the quadrotor and has chattering problems. In this paper, according to the dynamic characteristics of the quadrotor, an adaptive update law is introduced and a saturation function is used to improve the SMC. The proposed control strategy has an inner and an outer loop control structures. The outer loop position control provides the required reference attitude angle for the inner loop. The inner loop attitude control ensures rapid convergence of the attitude angle. The effectiveness and feasibility of the algorithm are verified by mathematical simulation. The mathematical simulation results show that the designed model-free adaptive control method of the quadrotor is effective, and it can effectively realize the trajectory tracking control of the quadrotor. The design of the controller does not depend on the kinematic and dynamic models of the unmanned aerial vehicle (UAV), and has high control accuracy, stability, and robustness.

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Acknowledgement

We would like to express our sincere gratitude to Dr. WANG Jianan who made some important analysis for the article.

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Authors and Affiliations

  1. School of Aerospace Engineering, Beijing Institute of Technology, Beijing, 100081, China

    Dongdong Yuan  (袁冬冬) & Yankai Wang  (王彦恺)

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  1. Dongdong Yuan  (袁冬冬)
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  2. Yankai Wang  (王彦恺)
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Correspondence to Yankai Wang  (王彦恺).

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Yuan, D., Wang, Y. Data Driven Model-Free Adaptive Control Method for Quadrotor Trajectory Tracking Based on Improved Sliding Mode Algorithm. J. Shanghai Jiaotong Univ. (Sci.) 27, 790–798 (2022). https://doi.org/10.1007/s12204-020-2254-4

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  • DOI: https://doi.org/10.1007/s12204-020-2254-4

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