This thesis contain the simulation and designing of Quadcopter using Adaptive Neuro Fuzzy Controller to control the altitude of quadcopter and obstacle detection. Now a day’s advancement in technology has made it possible to develop low power and lightweight with accurate sensors which are used with controllers for controlling, which have high processing power but small power consumption. This has been allowed for the development of complex and difficult control systems that can be implemented onboard UAV. With this combination of high precision and light weight, real-time onboard navigation or guidance and autonomous flights are now practical. This research work used a Fuzzy controller to control the pitch angle of quadcopter and avoiding obstacles. The fuzzy controller receives the sensory data and adjust the pitch accordingly until unless it finds the clear path. For detecting obstacles the IR sensors are used. For designing a fuzzy inference system used Sugeno model and used mat lab commands to design ANFIS as we have another method for designing by using a Simulink as well. ANFIS designed is based on kinematics and dynamics equation of quadcopter that will be able to control the pitch of quadcopter. Simulations results in mat lab show that by using ANFIS the performance of Quadcopter will be improved significantly

MS