Attitude Control and Trajectory Tracking of a Shipboard Quadrotor Unmanned Aerial Vehicle Based on Self-Improvement Algorithm

With the rapid development of Industry 4.0 and urbanisation around the world, computer control technology and electromechanical systems, the development of UAVs around the world has entered a period of rapid development. As UAVs enter the rapid development stage, higher requirements are placed on the precise control rate during UAV missions. Multi-rotor UAVs have been used on ships in large scale due to their easy-to-manoeuvre characteristics. However, shipboard UAVs are affected by many factors during flight, such as crosswinds, gusts and wave effects at sea. Therefore, it is necessary to optimise the robustness and safety of the control process of shipborne UAVs through self-immunity algorithms. In the research process of UAV, modern control theory has developed very rapidly, but it is difficult to be applied in practice, and there is a certain gap. The reason for this is that advanced control theory techniques require an accurate mathematical model of the controlled object.In this study, the position and attitude equations of motion of the quadrotor UAV are firstly obtained according to the rigid body mechanics, the UAV dynamics model is deduced, and for the UAV attitude control and trajectory tracking requirements, the inner loop adopts the self-immobilisation control and the outer loop adopts the PID control, and the relevant control structure is designed. Through relevant Simulink simulation and comparison with the effect of traditional PID control, the optimisation effect of the self-immunity algorithm on the control of shipborne quadrotor UAV is verified.

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Publication Details

Journal
Science and Technology of Engineering Chemistry and Environmental Protection
Published
2024-12-31
DOI
https://doi.org/10.61173/pyz2aa50
Primary Topic
Adaptive Control of Nonlinear Systems
Type
article
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article

Attitude Control and Trajectory Tracking of a Shipboard Quadrotor Unmanned Aerial Vehicle Based on Self-Improvement Algorithm

Yu‐Kun Lai
Science and Technology of Engineering Chemistry and Environmental Protection
Adaptive Control of Nonlinear Systems
article

Attitude Control and Trajectory Tracking of a Shipboard Quadrotor Unmanned Aerial Vehicle Based on Self-Improvement Algorithm

Yu‐Kun Lai
article en

Abstract

With the rapid development of Industry 4.0 and urbanisation around the world, computer control technology and electromechanical systems, the development of UAVs around the world has entered a period of rapid development. As UAVs enter the rapid development stage, higher requirements are placed on the precise control rate during UAV missions. Multi-rotor UAVs have been used on ships in large scale due to their easy-to-manoeuvre characteristics. However, shipboard UAVs are affected by many factors during flight, such as crosswinds, gusts and wave effects at sea. Therefore, it is necessary to optimise the robustness and safety of the control process of shipborne UAVs through self-immunity algorithms. In the research process of UAV, modern control theory has developed very rapidly, but it is difficult to be applied in practice, and there is a certain gap. The reason for this is that advanced control theory techniques require an accurate mathematical model of the controlled object.In this study, the position and attitude equations of motion of the quadrotor UAV are firstly obtained according to the rigid body mechanics, the UAV dynamics model is deduced, and for the UAV attitude control and trajectory tracking requirements, the inner loop adopts the self-immobilisation control and the outer loop adopts the PID control, and the relevant control structure is designed. Through relevant Simulink simulation and comparison with the effect of traditional PID control, the optimisation effect of the self-immunity algorithm on the control of shipborne quadrotor UAV is verified.

Science and Technology of Engineering Chemistry and Environmental ProtectionVol. 1(10)
University of Shanghai for Science and Technology (CN)
Sustainable cities and communities
Openalex Percentile: Top 70%
Adaptive Control of Nonlinear Systems
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