An Adaptive Attitude Tracking Control Approach for an Unmanned Helicopter with Parametric Uncertainties and Measurement Noises

The attitude control system design for an unmanned helicopter is always a challenging task because the vehicle dynamics are highly nonlinear and fully coupled and subject to parametric uncertainties. This paper presents an adaptive attitude tracking controller with dual model structure, which achieves significant tracking performance and has good capability to overcome the adverse effect of measurement noises on the convergence of adjustable parameters. Moreover, a modified controller with variable gain elements is designed to further reduce the adverse effect of measurement noises on the adjustable system output in the later stage of the adaptive learning process. A series of hardware-in-loop simulations have verified the proposed adaptive control schemes. The suggested design methodologies featured in this paper are effective control tools that can help bridge the gaps between adaptive control theory and the need of realistic applications such as high-performance unmanned helicopters.