Nowadays,quadcopters are presented in many life applications which require the performance of automatic takeoff,traject-ory tracking,and automatic landing.Thus,researchers are aiming to enhance the performance of these vehicles through low-cost sens-ing solutions and the design of executable and robust control techniques.Due to high nonlinearities,strong couplings and under-actu-ation,the control design process of a quadcopter is a rather challenging task.Therefore,the main objective of this work is demonstrated through two main aspects.The first is the design of an adaptive neuro-fuzzy inference system(ANFIS)controller to develop the atti-tude and altitude of a quadcopter.The second is to create a systematic framework for implementing flight controllers in embedded sys-tems.A suitable model of the quadcopter is also developed by taking into account aerodynamics effects.To show the effectiveness of the ANFIS approach,the performance of a well-trained ANFIS controller is compared to a classical proportional-derivative(PD)controller and a properly tuned fuzzy logic controller.The controllers are compared and tested under several different flight conditions including the capability to reject external disturbances.In the first stage,performance evaluation takes place in a nonlinear simulation environ-ment.Then,the ANFIS-based controllers alongside attitude and position estimators,and precision landing algorithms are implemented for executions in a real-time autopilot.In precision landing systems,an IR-camera is used to detect an IR-beacon on the ground for pre-cise positioning.Several flight tests of a quadcopter are conducted for results validation.Both simulations and experiments demon-strated superior results for quadcopter stability in different flight scenarios.