Quadrotor Flight System Design using Collective and Differential Morphing with SPSA and ANN
Keywords:Artificial neural network, Collective morphing, Differential morphing, Matlab, PID, Quadrotor, SPSA
Quadrotor modeling has been done with collective and differential morphing. Quadrotor initial state and morphing states are drawn in the Solidworks program. Newton-Euler approximation was used for quadrotor modeling. The mass and moment of inertia values required for modeling and simulation were obtained from the Solidworks program. Matlab / Simulink environment and state-space model approaches are used for simulations. A simultaneous perturbation stochastic approximation (SPSA) algorithm was used to determine the quadrotor morphing rates. If the morphing state obtained by SPSA is not included in the values obtained from the drawings, here it is provided to find the moments of inertia with the method based on learning by using the data obtained with the Artificial Neural Network(ANN). Proportional Integral Derivative (PID) is used as the quadrotor control algorithm. PID coefficients are also determined by SPSA.
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