Nonlinear dynamic modeling and control of a small-scale helicopter

Abstract

A test bench for experimental testing of the attitude control of a small-scale helicopter is constructed. A nonlinear model with 10 states is developed for this experimental setup. The unknown model parameters are estimated using the extended Kalman filter with flight test data of the helicopter operating on the test bench. In this work, it is proved that the nonlinear helicopter dynamic model may be globally feedback linearized using the dynamic feedback linearization technique. In order to satisfy multiple closed-loop performance specifications simultaneously, a controller is proposed by applying the Convex Integrated Design (CID) method to the feedback linearized model. Finally, the controller is tested in simulation demonstrating the closed-loop performance of the proposed controller.

Inflow Prediction and First Principles Modeling of a Coaxial Rotor Unmanned Aerial Vehicle in Forward Flight

“…http://ijass.org Such as Mettler et al and Valavanis identified quasisteady derivatives and physical parameters of the single rotor unmanned aerial vehicle (UAV) by using the Comprehensive Identification from FrEquency Responses (CIFER) tool, and developed their controller on the basis of the identified state space model; Kenneth explained the parameter estimation of an aircraft [3][4][5][6][7][8][9]. Conversely, very few studies have been conducted on parameter estimation and system identification of small coaxial rotor helicopters.…”

Feedback linearization of the nonlinear model of a small-scale helicopter

“…In our previous work, we studied the nonlinear attitude system and control of the model helicopter on the test bench and proved that the attitude subsystem can be linearized by the dynamic feedback linearization technique [19,20]. Then, the key characteristics of the model helicopter were studied in [21], and we proved that the simplified attitudeheave subsystem, in which the angular velocity cross product is ignored, can be transformed to linear system by the dynamic feedback linearization technique [21].…”

Enhanced Data Driven Mode-Free Adaptive Yaw Control of UAV Helicopter

“…Because linearized models cannot guarantee the global model approximation, nonlinear control methods have been used in the control system design, such as [2,[11][12]. Furthermore, in a lot of control systems, the nonlinear model of plant dynamics is generally nonaffine in input and is commonly simplified around a trim point, that is, an operating point is dependent on the current system states [13]. Coupled with the uncertainties under the varying environment and the changing flight conditions, developing a controller to opportune compensate for the time varying uncertainties have been a more difficult task [14].…”