A Compound Controller of an Aerial Manipulator Based on Maxout Fuzzy Neural Network

Author:

Qi Xinchen1ORCID,Wu Jianwei1ORCID,Pan Jiansheng1ORCID

Affiliation:

1. Institute of Ultra-Precision Photoelectric Instrument Engineering, Harbin Institute of Technology, Harbin 150001, Heilongjiang, China

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.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

Reference23 articles.

1. Design of small-scale quadrotor unmanned air vehicles using genetic algorithms

2. Stable camera position control of unmanned aerial vehicle with three degree, f-freedom manipulator for visual test of bridge inspection;T. Ikeda;Journal of Field Robotics,2019

3. Adaptive output control of a mobile manipulator hanging from a quadcopter unmanned vehicle

4. Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator

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2. Adaptive Robust Control via a Nonlinear Disturbance Observer for Cable-driven Aerial Manipulators;International Journal of Control, Automation and Systems;2023-01-30

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