Adaptive control of nonlinear system based on QFT application to 3-DOF flight control system

Abstract

Research on unmanned aerial vehicle (UAV) became popular because of remote flight access and cost-effective solution. 3-degree of freedom (3-DOF) unmanned helicopters is one of the popular research UAV, because of its high load carrying capacity with a smaller number of motor and requirement of forethought motor control dynamics. Various control algorithms are investigated and designed for the motion control of the 3DOF helicopter. Three-degree-of-freedom helicopter model configuration presents the same advantages of 3-DOF helicopters along with increased payload capacity, increase stability in hover, manoeuvrability and reduced mechanical complexity. Numerous research institutes have chosen the three-degree-of-freedom as an ideal platform to develop intelligent controllers. In this research paper, we discussed about a hybrid controller that combined with Adaptive and Quantitative Feedback theory (QFT) controller for the 3-DOF helicopter model. Though research on Adaptive and QFT controller are not a new subject, the first successful single Adaptive aircraft flight control systems have been designed for the U.S. Air Force in Wright Laboratories unmanned research vehicle, Lambda [1]. Previously researcher focused on structured uncertainties associated with controller for the flight conditions theoretically. The development of simulationbased design on flight control system response, opened a new dimension for researcher to design physical flight controller for plant parameter uncertainties. At the beginning, our research was to investigates the possibility of developing the QFT combined with Adaptive controller to control a single pitch angle that meets flying quality conditions of automatic flight control. Finally, we successfully designed the hybrid controller that is QFT based adaptive controller for all the three angles.

A Laboratory Prototype Tandem Helicopter With Two Degrees of Freedom

“…Also, k i are constants used to stabilize the system. Applying the controller (17), (18) into the system (13) and simplifying, the dynamics of the closed-loop system is obtained:…”

“…The simulation is developed using the model defined in ( 13) and ( 16), the control system defined in (17), (18), and the observer defined in (21). The initial conditions for the model are:…”

Energy management and system design for fuel cell hybrid unmanned aerial vehicles

“…Advanced motion control methods can ensure that UAV can achieve various flight movements, such as adaptive and feedback theory. 13 These control methods can still be transplanted to HUAVs, but new problems after the introduction of a hybrid power system need to be considered. For the hybridization of UAVs, the first step is to optimize the size of the onboard energy sources.…”

“…One of the main objectives of this study is to correct this by presenting comprehensive energy management and sizing optimization methodologies. Advanced motion control methods can ensure that UAV can achieve various flight movements, such as adaptive and feedback theory 13 . These control methods can still be transplanted to HUAVs, but new problems after the introduction of a hybrid power system need to be considered.…”