Data Dependent Stable Robust Adaptive Controller Design for Altitude Control of Quadrotor Model

This paper presents Nonlinear Auto Regressive Moving Average (NARMA) based stable robust adaptive controller design. Both the plant and the closed-loop controller systems are modelled by the proposed NARMA based input-output models. During online supervised learning for the system identification and the controller design phases, input-output data obtained from the simulated plant are evaluated in suitable parameter regions providing Schur stability for the overall closed-loop system. At the same time, ε-insentive loss function and ℓ
₁
norm are used for providing robustness for proposed system identification and adaptive controller parameters. The proposed controller design method is performed on quadrotor model which is an unmanned air vehicle benchmark plant. The performance results are compared against proportional derivative controller.