Real-Time Adaptive Intelligent Control System for Quadcopter Unmanned Aerial Vehicles With Payload Uncertainties

• Published in: IEEE transactions on industrial electronics (1982) 2022-02, Vol.69 (2), p.1641-1653
• Main Authors: Muthusamy, Praveen Kumar, Garratt, Matthew, Pota, Hemanshu, Muthusamy, Rajkumar
• Format: Article
• Language: English
• Subjects: Adaptation models; Adaptive control; Algorithms; Biological neural networks; Brain; Brain emotional learning based intelligent controller (BELBIC); Brain modeling; Control systems; Controllers; flight control system; Fuzzy logic; fuzzy neural network (FNN); Machine learning; Nonlinear systems; Orbits; Payloads; proportional–integral-derivative (PID); quadrotor; Real time; reinforcement learning; six degrees-of-freedom (6DOF); suspended payload uncertainty; Tracking; UAV; Uncertainty; Unmanned aerial vehicles; wind disturbance
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2021.3055170

A novel bidirectional fuzzy brain emotional learning (BFBEL) controller is proposed to control a class of uncertain nonlinear systems such as the quadcopter unmanned aerial vehicle (QUAV). The proposed BFBEL controller is nonmodel-based and has a simplified fuzzy neural network structure and adapts with a novel bidirectional brain emotional learning algorithm. It is applied to control all six degrees-of-freedom of a QUAV for accurate trajectory tracking and to handle the payload uncertainties and disturbances in real-time. The trajectory tracking performance and the ability to handle the payload uncertainties are experimentally demonstrated on a QUAV. The experimental results show a superior performance and rapid adaptation capability of the proposed BFBEL controller. The proposed BFBEL controller can be used for the commercial drone applications.