Intelligent control for a drone by self-tunable Fuzzy Inference System

The work describes an automatically online self-tunable fuzzy inference system (STFIS) of a new configuration of mini-flying called XSF (X4 stationary flyer) drone. A fuzzy controller based on online optimization of a zero order Takagi-Sugeno fuzzy inference system (FIS) by a back propagation-like algorithm is successfully applied. It is used to minimize a cost function that is made up of a quadratic error term and a weight decay term that prevents an excessive growth of parameters. Thus, we carried out control for the continuation of simple trajectories such as the follow-up of straight lines, and complex (half circle, corner) by using the STFIS technique. This permits to prove the effectiveness of the proposed control law. We studied the robustness of the two controllers used in the presence of disturbances. We presented two types of disturbances, the case of a gust of wind and taking into account white noise disturbances. A comparison between the self-tunable fuzzy inference system (STFIS) and adaptive network based fuzzy inference system (ANFIS) is given.