Optimal Backstepping Flight Controller Design Based a New Compact Gravitational Search Algorithm for Hexarotor Attitude Stabilization

In this paper, a hexarotor Unmanned Aerial Vehicle (UAV) flight dynamics is derived and its control system is designed. A complete mathematical model of the UAV is obtained using Newton–Euler formulation and it is used for simulation. However, a reduced model is derived for the control design purpose which is based on the backstepping approach for hexarotor attitude stabilization and altitude trajectory tracking. The main contribution of this paper consists of the introduction of a new compact Gravitational Search Algorithm (cGSA) and its application for improving the backstepping controller performances in terms of tracking errors. Indeed, using the compact optimization paradigm enhances the performances of classical Population-based Algorithms (PBAs). Moreover, the introduced cGSA is applied to compute the optimal gains of the considered controller. In addition, the cGSA is compared with two compact optimization algorithms that are compact Particle Swarm Optimization (cPSO) and compact Teaching Learning-Based Optimization (cTLBO). The proposed algorithm shows encouraging results.