Nonlinear Adaptive Model Following Control for a 3-DOF Model Helicopter

Abstract

Interest in designing feedback controllers for helicopters has increased over the last ten years or so due to the important potential applications of this area of research. The main difficulties in designing stable feedback controllers for helicopters arise from the nonlinearities and couplings of the dynamics of these aircraft. To date, various efforts have been directed to the development of effective nonlinear control strategies for helicopters (Sira-Ramirez et al., 1994; Kaloust et al., 1997; Kutay et al., 2005; Avila et al., 2003). Sira-Ramirez et al. applied dynamical sliding mode control to the altitude stabilization of a nonlinear helicopter model in vertical flight. Kaloust et al. developed a Lyapunov-based nonlinear robust control scheme for application to helicopters in vertical flight mode. Avila et al. derived a nonlinear 3-DOF (degree-of-freedom) model as a reduced-order model for a 7-DOF helicopter, and implemented a linearizing controller in an experimental system. Most of the existing results have concerned flight regulation. This study considers the two-input, two-output nonlinear model following control of a 3-DOF model helicopter. Since the decoupling matrix is singular, a nonlinear structure algorithm (Shima et al., 1997; Isurugi, 1990) is used to design the controller. Furthermore, since the model dynamics are described linearly by unknown system parameters, a parameter identification scheme is introduced in the closed-loop system. Two parameter identification methods are discussed: The first method is based on the differential equation model. In experiments, it is found that this model has difficulties in obtaining a good tracking control performance, due to the inaccuracy of the estimated velocity and acceleration signals. The second parameter identification method is designed on the basis of a dynamics model derived by applying integral operators to the differential equations expressing the system dynamics. Hence this identification algorithm requires neither velocity nor acceleration signals. The experimental results for this second method show that it achieves better tracking objectives, although the results still suffer from tracking errors. Finally, we introduce additional terms into the equations of motion that express model uncertainties and external disturbances. The resultant experimental data show that the method constructed with the inclusion of these additional terms produces the best control performance. 9