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Tham khảo tài liệu 'electric vehicles modelling and simulations part 15', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Sugeno Inference Perturbation Analysis for Electric Aerial Vehicles 409 Fig. 8. UAV EPS Model at variable exogenous conditions with Sugeno fuzzy-hybrid system and Sugeno parameter perturbations. The inputs of Figure 5 are shown next in Figure 9 top centre graphs while the resulting thruster s input electrical power is also shown lower graph . The quasi-static approach shows that the armature input electrical power does vary in order to balance the UAV flight requirements for altitude and overcome the atmospheric air moisture conditions. Clearly Figure 10 shows a realisable UAV test scenario. Initially the UAV starts at ground sea level and gradually gains altitude with a realisable climb rate. During its mission the UAV remains at a fixed altitude and then gains altitude again reaching before its 6 km requirement where it remains for a given time 25 min until it starts to descend back to sea level. Meanwhile the air moisture varies between two fuzzy logic extremes of 1 and 0.5 each representing a different condition dry air and saturated moist air respectively. The moist air affects the temperature variation as the UAV altitude varies and hence was modelled utilising the Sugeno FIS topology. Based on the chapter hypothesis the armature resistance will affect the propeller shaft angular velocity for given conditions. Therefore the next step is to observe the armature resistance during the UAV mission and compare this to the nominal sea level conditions. Figure 11 successfully demonstrates the nominal blue line armature resistance at sea level and the variable resistance due to the altitude and air moisture conditions. In Figure 11 the dotted upper and lower lines demonstrate the injected 10 perturbation in the Sugeno consequent. Both the effects of altitude air moisture and the sensor SIEB type of perturbations affect the thruster s armature resistance and therefore it is expected to observe this variation to cascade also to the thruster s variables such as the .
