Attitude Control for an Unmanned Helicopter Using Passivity-Based Iterative Learning

This paper proposes a learning control based attitude tracking control method for a small size unmanned helicopter. The helicopter is subject to uncertain system parameters, unmodeling dynamics, and unknown external disturbances. To improve the flight control performance, a new learning based nonlinear robust control strategy is designed to let the helicopter track a desired attitude trajectory. The proposed controller accounts for the structural and unstructural uncertain dynamics via the iterative learning strategy. In order to solve the problem that iterative learning can not completely compensate for external disturbances, a sliding mode controller is also utilized to reduce the effects of unknown disturbances. The output strictly passive feature of the closed-loop system is proven by considering an iterative learning control input combined with sliding mode algorithm. Then, an adaptive update law, based on iterative learning control, is developed to guarantee the convergence of the tracking error. The asymptotic convergence of the attitude tracking errors is also proved. Numerical simulation results are presented to validate the control performance of the proposed control algorithm.