In this paper, a linear parameter-varying (LPV)-based model and robust gainscheduled structural proportion integral and derivative (PID) control design solution are proposed and applied on a bio-inspired morphing wing unmanned aerial vehicle (UAV) for the morphing process. In the LPV model method, the authors propose an improved modeling method for LPV systems. The method combines partial linearization and function substitution. Using the proposed method, we can choose the varying parameters simply, thus creating a model that is more flexible and applicable. Then, a robust gain-scheduled structural PID control design method is given by introducing a structural matrix to design a structural PID controller, which is more consistent with the structure of the PID controller used in practice and has a simpler structure than representative ones in the existing literature. The simulation results show that the developed LPV morphing UAV model is able to catch the response of the original nonlinear model with a smaller error than the existing Jacobian linearization method and the designed controller can maintain stable flights in practice with satisfactory robustness and performance.
In micro air vehicles (MAVs), a magnetometer is usually employed for heading estimation. However, in real flight campaigns the magnetometer can easily suffer interference from external magnetic disturbances. In this paper, a simple novel method is proposed to conduct real-time calibration of the magnetometer subject to external disturbances with the aid of gravity and global navigation satellite system measurements. First, two equalities are derived from the rigid-body rotation equations. Then, the closed-form solution to the pseudo magnetometer biases is derived according to these equalities. To enhance the robustness of the solver, an optimizer based on a gradient-descent algorithm is proposed. The bifurcation analysis and convexity proofs are given to show that the proposed optimization has a unique solution and no local optimum. The execution time analysis of the proposed method also indicates that it is a very fast algorithm. Finally, the calibration parameters are determined by algebraic equations formed by pseudo biases. The proposed method is attitude-free, simple and easy-to-implement. The experiments on a real-world fixed-wing MAV demonstrate that the method is effective.
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