Controlling a drone: Comparison between a based model method and a fuzzy inference system

Abstract

International audienceThe work describes an automatically on-line self-tunable fuzzy inference system (STFIS) of a new configuration of mini-flying called XSF (X4 Stationnary Flyer) drone. A fuzzy controller based on on-line 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, and helicoidal) by using the STFIS technique. This permits to prove the effectiveness of the proposed control law. Simulation results and a comparison with a static feedback linearization controller (SFL) are presented and discussed. We studied the robustness of the two controllers used in the presence of disturbances. We presented two types of disturbances, the case of a breakdown of an engine as well as a gust of wind

Monocular Visual-Inertial SLAM-Based Collision Avoidance Strategy for Fail-Safe UAV Using Fuzzy Logic Controllers

“…A robustness comparison between model-based with self-tunable fuzzy inference system (STFIS) has been studied to control a drone in presence of disturbances in [18]. Kadmiry and Driankov [16] designed an gain scheduler-based FLC for an unmanned helicopter to achieve stable and robust aggressive maneuverability.…”

Using the Cross-Entropy method for control optimization: A case study of see-and-avoid on unmanned aerial vehicles

“…The optimization of this type of controllers for UAVs is already presented in the literature. Some of the most relevant of works are [1], that shows a self-tunable fuzzy inference system (STFIS) compared with model-based control system to control a drone in presence of disturbances. The classical and multi-objective genetic algorithm (GA) based fuzzygenetic autopilot are also designed and used for a UAV [2].…”

Variable Structure-Model Reference Adaptive Control for Stabilization the Attitude of UAV

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