Estimating of UAV Battery Status with BSA Based Sugeno Type Fuzzy System

Abstract

A hybrid model based on Sugeno type fuzzy system and Back-Tracking Search Optimization Algorithm (BSA) was developed for the estimation of battery status, which is one of the most important parameters affecting the remaining endurance of a rotary wing Unmanned Aerial Vehicle (UAV), in this study. In the model, flight altitude, ground speed and current values obtained from the battery were determined as input variables; battery status was used as output variable. The data were normalized and the Sugeno type fuzzy system was modelled with different rule numbers and each model structure was optimized with BSA. The obtained simulation results show that the proposed model has high compatibility with true data and its prediction success is high. In addition, it is observed that the model performance is sensitive to the membership function type, number of rules and parameter settings. In this direction, optimizing Sugeno type fuzzy systems with BSA offers an effective and reliable approach in modelling complex and nonlinear systems such as UAV battery status.

Keywords

• UAV
• Fuzzy Logic
• Sugeno
• BSA

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