Optimal Tuning Of Integral Saturation Back-stepping Quadcopter's Controller Using Grey Wolf Optimizer

Quadcopters require affordable and efficient controls to accurately follow planned flight paths. At now, there are various techniques available for path tracking, which utilize either linear or nonlinear approaches, each with its own distinct criteria for measuring effectiveness. The Grey Wolf Optimizer (GWO) algorithm was utilized in this study to create a control system that combines the Integral Saturation Back-stepping Control (ISBSC). Firstly, a complex mathematical model of a quadcopter with six degrees of freedom was created, with the primary focus on controlling its orientation, and subsequently, its location. This method was demonstrated in both instances involving hovering and path tracking. In order to find the best gains for the planned controller technique, different artificial intelligence methods were used to conduct optimal study. The results showed that the Grey Wolf Optimizer (GWO) produced the most accurate responses and fast time convergence when it's compared to the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).