In this paper, output tracking control of a helicopter model is investigated. The model is derived from Newton-Euler equations by assuming that the helicopter body is rigid. First, we show that for several choices of output variables exact input-output linearization fails to linearize the whole state space and results in having unstable zero dynamics. By neglecting the couplings between moments and forces, we show that the approximated system with dynamic decoupling is full state linearizable by choosing positions and heading as outputs. We prove that bounded tracltiiig is achieved by applying the approximate control. Next, we derive a diffeoniorphism showing that an approsimation of the system is differentially flat, thus state trajectory and nominal inputs can be generated from a given output trajectory. Simulation results using bot,li output tracking controllers based on exact and approximate input-output linearization are presented for coniparison. IiitroductioiiHelicopter control[l, 21 requires the ability to produce moments and forces on the vehicle for two purposes: first, to produce equilibrium and thereby hold the helicopter in a desired trim st,ate; and secondly, to produce accelerations and thereby change the helicopter velocity, position and orientation. Like aircraft control, helicopter control is accomplished primarily by producing moments about all three aircraft axes: roll, pitch and yaw. The helicopter has in addition direct control over the vertical force on the aircraft, corresponding to its VTOL capability. The engine power is controlled a rotor speed governor to automatically manage the power. The lateral and longitudinal velocities of the helicopt,er in hover must be controlled using pitch and roll inoineiits about the center of gravity. Changes in pitch or roll attitude produces longitudinal or lateral forces and finally the desired velocity of the helicopter. There usually is considerable coupling of the forces a.nd moments produced by the helicopter controls. Helicopter flight dynamics are inherently unstable, particularly in the hover mode. Feedback linearization [3]has been successfully applied in control design for highly maneuverable aircraft such as S/VTOL[4], CTOL[5] of aircraft control design. In tliis paper, we design an output tracking controller for a helicopter model based on input-output linearization. Our control design is constructed by first neglecting the coupling effect between rolling(pitching) moment and lateral(longitudina1) force, then showing that the approximate control results in bounded tracking on the exact model. The idea of using approximate inputoutput linearization on helicopter control is motivated by the control design of VSTOL in [B] and PVTOL in [7], in which full state and approximate feedback linearization are applied respectively.In this paper, we first present a helicopter model which is derived from Newton-Euler equations. In section 3 and 4, we show that exact input-output linearization and exact state-space linearization fail to linearize...

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego‐motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego‐motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real‐time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed‐loop system, conditions are provided under which exponential stability can be guaranteed. In the closed‐loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision‐in‐the‐loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.

We are developing a system for autonomous navigation of unmanned aerial vehicles (UAVs) based on computer vision. A UAV is equipped with an onboard cameras and each UAV is provided with noisy estimates of its own state, coming from GPS/INS. The mission of the UAV is low altitude navigation from an initial position to a final position in a partially known 3-D environment while avoiding obstacles and minimizing path length. We use a hierarchical approach to path planning. We distinguish between a global offline computation, based on a coarse known model of the environment and a local online computation, based on the information coming from the vision system. A UAV builds and updates a virtual 3-D model of the surrounding environment by processing image sequences and fusing them with sensor data. Based on such a model the UAV will plan a path from its current position to the terminal point. It will then follow such path, getting more data from the on-board cameras, and refining map and local path in real time.