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 stability of UAV is improved. At the same time, a neural network model identifier based on Maxout activation function is built to realize accurate recognition of the controlled model. The simulation results show that, compared with the conventional PID controller, the composite controller combined with fuzzy neural network can improve the anti-interference ability and stability of UAV greatly.
Highlights
With the maturity of drone technology, drones are widely used in the industrial field
The composite controller is compared with the traditional proportion integration differentiation (PID) controller
The aerial manipulator is taken as the research object
Summary
With the maturity of drone technology, drones are widely used in the industrial field. A team from the Nanjing University of Aeronautics and Astronautics established an inverse system and uses the backpropagation (BP) neural network to control attitude of UAV [4]. It can make the UAV roll, pitch, and yaw (RPY) angle error within the allowable range. It has the problems of control lag and low real-time performance. Ey used a combination of H-∞robust control and acceleration feedback to control UAV and used PID to control manipulator.
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