This paper addresses an adaptive sliding mode nonlinear controller design for the attitude tracking of an autonomous quadrotor unmanned aerial vehicle (UAV). To overcome the chattering phenomenon in the controller signals, the proposed design is realized by considering the integral of the controller signal. In addition, Lyapunov-based estimations are employed, instead of bounded values of nonlinear functions and their derivatives in the controller design. Thus, the nonlinear functions that impose a computational burden on the controller signal in the classical nonlinear control method are used with their upper bounds estimated in the design. Furthermore, a singularity-free sliding mode approach is proposed to avoid the potential singularities in the adaptation mechanism. The asymptotic stability of the closed-loop dynamics is proved in the sense of Lyapunov theory. The effectiveness of the proposed control method is verified by comparative numerical simulations of the quadrotor UAV, demonstrating the robustness of the controller against external disturbances, model uncertainties, and partial actuator faults.

Model Based Design (MBD) is a development method that integrates the design, implementation, and verification of any system, and has grown in popularity in recent years. This method makes the simulation the main element of the development process, allowing continuous prototyping. In this article, the experimental verification of simulation model of small jet DEHA UAV is realized. Within the scope of the study, an aircraft simulation model is developed with model-based design approach using model-based design tools and simulation model was verified by novel experimental verification platforms. Model verification studies are carried out with experimental platforms for the aircraft-specific models “Mass Properties”, “Actuators”, “Propulsion” and “Landing Gear” in the DEHA simulation model. As a result, a simulation model verified with experimental platforms for DEHA, which is an experimental jet unmanned aerial vehicle, was obtained with a model-based design approach and tools. Thanks to the validation platforms, the accuracy of the model has been increased.