Neural networks and genetic algorithms-based self-adjustment system for a backstepping controller of an unmanned aerial vehicle

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.