Neural networks and genetic algorithms-based self-adjustment system for a backstepping controller of an unmanned aerial vehicle
Year: 2025
Type: article
Abstract: Backstepping control has been widely used in drones because it considers the dynamic of the system when designing the control law and is robust to parametric uncertainties. However, the typical controller has twelve gains that must be adjusted for optimal results. This process is done manually and with a fixed value, which limits the performance of the controller. This article presents a backstepping intelligent self-tuning system for a multirotor drone. The autotuning is done based on the dynamic vehicle response, optimizing energy consumption, and minimizing its rise time, but without causing an overshoot that consumes unnecessary energy. A backpropagation neural network was trained with a database that considers the dynamic response of the system to achieve this effect. The database was obtained with a metaheuristic algorithm to ensure that only combinations that meet these conditions are used. Several independent tests were carried out to test the system. The results show that the proposed method is adequately adjusted and fulfilled, with the expected dynamic response for 95% of the tests and a dynamic response with minor overshoot and settling time, compared to a PID tuned by genetic algorithm. • This research presents a controller that considers the dynamic response of the drone. • This work presents a self-tuning controller for optimizing the dynamic response. • This system allows control with minimal overshoot. • In addition, the settling time is reduced.
Source: Alexandria Engineering Journal
Institutions: Universidad de Málaga, Autonomous University of Queretaro
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,900
OpenAlex ID: W4409785314