Abstract:The aerial manipulator is a complex system with high coupling and instability. The motion of the robotic arm will affect the self-stabilizing accuracy of the unmanned aerial vehicles (UAVs). To enhance the stability of the aerial manipulator, a composite controller combining conventional proportion integration differentiation (PID) control, fuzzy theory, and neural network algorithm is proposed. By blurring the attitude error signal of UAV as the input of the neural network, the anti-interference ability and s… Show more
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