Control Simulation of Flapping-Wing Micro Aerial Vehicle Based on Multi-level Optimization Model Predictive Control

In this paper, a control algorithm using multi-level optimization and model predictive control is proposed to solve the conflict between the computational cost and control accuracy of the flapping-wing micro aerial vehicle. First, a quasi-steady model is established to evaluate the aerodynamic force...

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Bibliographic Details
Published in:IEEE access Vol. 12; p. 1
Main Authors: Zheng, Hongyu, Chen, Wengang, Xie, Fangfang
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Aerodynamic forces
Aerodynamics
Algorithms
Atmospheric modeling
Computational efficiency
Computational modeling
Control algorithms
Control simulation
Control theory
Flapping wing vehicle
Flapping wings
Force
Kinematics
Micro air vehicles (MAV)
Model predictive control
Multi-level optimization
Optimization
Optimization models
PD control
PI control
Predictive control
Predictive models
Proportional integral derivative
Quasi-steady model
Tracking control
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:In this paper, a control algorithm using multi-level optimization and model predictive control is proposed to solve the conflict between the computational cost and control accuracy of the flapping-wing micro aerial vehicle. First, a quasi-steady model is established to evaluate the aerodynamic forces and moments of the flapping-wing vehicle, and the forces and moments are then optimized to meet the control requirements based on classical model predictive control. Then an optimization module based on the quasi-steady model is introduced to optimize the kinematic parameters to achieve the optimal forces and moments, thus it decomposes the complex optimization problem of the classical model predictive control into two sub-problems. Compared with classical PID control and model predictive control, the response speed of the multi-level optimization model predictive control is effectively improved while maintaining high accuracy. Finally, the effectiveness of the control framework is validated by the control simulations of four examples of set-point and motion tracking control.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3376646