Cascade model predictive control for enhancing UAV quadcopter stability and energy efficiency in wind turbulent mangrove forest environment.
Unmanned aerial vehicle (UAV) modelling and control, particularly in quadrotors with high position-orientation coupling, present significant challenges for practical applications, such as environmental monitoring missions in windy mangrove forests. Conventional control strategies like the PID cont...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier Ltd
2024
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/46966/3/Cascade%20model%20predictive%20control%20-%20Copy.pdf http://ir.unimas.my/id/eprint/46966/ https://www.sciencedirect.com/science/article/pii/S2772671124004169 https://doi.org/10.1016/j.prime.2024.100836 |
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| Summary: | Unmanned aerial vehicle (UAV) modelling and control, particularly in quadrotors with high position-orientation
coupling, present significant challenges for practical applications, such as environmental monitoring missions in
windy mangrove forests. Conventional control strategies like the PID controller, often employed in simulations
due to their simplicity, often underperform in real-world scenarios due to their linear assumptions. This research
proposes a novel hierarchical cascaded model predictive control system for altitude, attitude, and battery efficiency for quadrotor in mangrove area. This control system addresses computational complexity by decomposing
the overall MPC strategy into two distinct schemes, one for translational displacements and another for rotational
movements, enhancing the UAV’s resilience to wind turbulence, a significant disturbance factor in mangrove
environments. Rigorous simulation and experiment test flight involving complex trajectory tracking and windy
conditions demonstrate the proposed controller’s superior performance compared to conventional PID controller,
particularly in terms of stability, disturbance rejection, underscoring its potential for UAV applications in
challenging environments. |
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