Nowadays,quadcopters are presented in many life applications which require the performance of automatic takeoff,trajectory tracking,and automatic landing.Thus,researchers are aiming to enhance the performance of these...Nowadays,quadcopters are presented in many life applications which require the performance of automatic takeoff,trajectory tracking,and automatic landing.Thus,researchers are aiming to enhance the performance of these vehicles through low-cost sensing solutions and the design of executable and robust control techniques.Due to high nonlinearities,strong couplings and under-actuation,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 attitude and altitude of a quadcopter.The second is to create a systematic framework for implementing flight controllers in embedded systems.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 environment.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 precise positioning.Several flight tests of a quadcopter are conducted for results validation.Both simulations and experiments demonstrated superior results for quadcopter stability in different flight scenarios.展开更多
文摘Nowadays,quadcopters are presented in many life applications which require the performance of automatic takeoff,trajectory tracking,and automatic landing.Thus,researchers are aiming to enhance the performance of these vehicles through low-cost sensing solutions and the design of executable and robust control techniques.Due to high nonlinearities,strong couplings and under-actuation,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 attitude and altitude of a quadcopter.The second is to create a systematic framework for implementing flight controllers in embedded systems.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 environment.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 precise positioning.Several flight tests of a quadcopter are conducted for results validation.Both simulations and experiments demonstrated superior results for quadcopter stability in different flight scenarios.
