Multi-Body System Simulation combined with System Identification was developed as a method for determining the stability characteristics of a human powered helicopter(HPH) configurations. HPH stability remains a key component for meeting competition requirements, but has not been properly treated. Traditional helicopter dynamic analysis is not suited to the HPH due to its low rotation speeds and light weight. Multi-Body System Simulation is able to generate dynamic response data for any HPH configuration. System identification and linear stability theory are used to determine the stability characteristics from the dynamic response. This thesis focuses on the method development and doesn't present any HPH analysis results.

Helicopter operations in the vicinity of small naval surface vessels often require excessive pilot workload. Because of the unsteady flow field and large mean velocity gradients, the envelope for flight operations is limited. This experimental investigation uses a 1:144 scale model of the U.S. Navy destroyer DDG-81 to explore the problem. Both active and passive flow control techniques were used to improve the flow field in the helicopter's final decent onto the flight deck. Wind tunnel data was collected at a set of grid points over the ship's flight deck using a single component hotwire. Results show that the use of porous surfaces decreases the unsteadiness of the flow field. Further improvements are found by injecting air through these porous surfaces, causing a reduction in unsteadiness in the landing region of 6.6% at 0 degrees wind-over-deck (WOD) and 8.3% at 20 degrees WOD. Other passive configurations tested include fences placed around the hangar deck edges which move the unsteady shear layer away from the flight deck. Although these devices cause an increase in unsteadiness downstream of the edge of the fence when compared to the baseline, the reticulated foam fence caused an overall decrease in unsteadiness in the landing region of 12.1% at 20 degrees WOD. / Master of Science

The overall objective of the Wallenberg Laboratory for Information Technology and Autonomous Systems (WITAS) at Linköping University is the development of an intelligent command and control system, containing vision sensors, which supports the operation of a unmanned air vehicle (UAV) in both semi- and full-autonomy modes. One of the UAV platforms of choice is the APID-MK3 unmanned helicopter, by Scandicraft Systems AB. The intended operational environment is over widely varying geographical terrain with traffic networks and vehicle interaction of variable complexity, speed, and density. The present version of APID-MK3 is capable of autonomous take-off, landing, and hovering as well as of autonomously executing pre-defined, point-to-point flight where the latter is executed at low-speed. This is enough for performing missions like site mapping and surveillance, and communications, but for the above mentioned operational environment higher speeds are desired. In this context, the goal of this thesis is to explore the possibilities for achieving stable ‘‘aggressive’’ manoeuvrability at high-speeds, and test a variety of control solutions in the APID-MK3 simulation environment. The objective of achieving ‘‘aggressive’’ manoeuvrability concerns the design of attitude/velocity/position controllers which act on much larger ranges of the body attitude angles, by utilizing the full range of the rotor attitude angles. In this context, a flight controller should achieve tracking of curvilinear trajectories at relatively high speeds in a robust, w.r.t. external disturbances, manner. Take-off and landing are not considered here since APIDMK3 has already have dedicated control modules that realize these flight modes. With this goal in mind, we present the design of two different types of flight controllers: a fuzzy controller and a gradient descent method based controller. Common to both are model based design, the use of nonlinear control approaches, and an inner- and outer-loop control scheme. The performance of these controllers is tested in simulation using the nonlinear model of APID-MK3. / <p>Report code: LiU-Tek-Lic-2002:11. The format of the electronic version of this thesis differs slightly from the printed one: this is due mainly to font compatibility. The figures and body of the thesis are remaining unchanged.</p>

The WITAS project aims to develop technologies to enable an Unmanned Airial Vehicle (UAV) to operate autonomously and intelligently, in applications such as traffic surveillance and remote photogrammetry. Many of the necessary control and reasoning tasks, e.g. state estimation, reidentification, planning and diagnosis, involve prediction as an important component. Prediction relies on models, and such models can take a variety of forms. Model design involves many choices with many alternatives for each choice, and each alternative carries advantages and disadvantages that may be far from obvious. In spite of this, and of the important role of prediction in so many areas, the problem of predictive model design is rarely studied on its own. In this thesis, we examine a range of applications involving prediction and try to extract a set of choices and alternatives for model design. As a case study, we then develop, evaluate and compare two different model designs for a specific prediction problem encountered in the WITAS UAV project. The problem is to predict the movements of a vehicle travelling in a traffic network. The main difficulty is that uncertainty in predictions is very high, du to two factors: predictions have to be made on a relatively large time scale, and we have very little information about the specific vehicle in question. To counter uncertainty, as much use as possible must be made of knowledge about traffic in general, which puts emphasis on the knowledge representation aspect of the predictive model design. The two mode design we develop differ mainly in how they represent uncertainty: the first uses coarse, schema-based representation of likelihood, while the second, a Markov model, uses probability. Preliminary experiments indicate that the second design has better computational properties, but also some drawbacks: model construction is data intensive and the resulting models are somewhat opaque. / <p>Report code: LiU-Tek-Lic-2002:15.</p>

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Complex helicopter models that include effects typically ignored in control models, such as an analytical formulation for fuselage aerodynamics, blade lead-lagging and flexibility, and tail rotor aerodynamics, are derived. The landing gear, horizontal tailplane, a fully articulated main rotor, main rotor downwash, and blade flapping are also modeled. The modeling process is motivated by the desire to build control oriented, physics based models that directly result in ordinary differential equations (ODE) models which are sufficiently rich in dynamics information. A physics based model simplification procedure, which is called new ordering scheme, is developed to reduce the number of terms in these large nonlinear ODE models, while retaining the same number of governing equations of motion. The resulting equations are trimmed and linearized around several flight conditions (i.e. straight level flight, level banked turn, and helical turn) using Maple and Matlab. The resulting trims and model modes are validated against available literature data. The linearized models are first used for the design of variance constrained controllers with inequality constraints on outputs or inputs, output variance constrained controllers (OVC) and input variance constrained controllers (IVC), respectively. The linearized helicopter models are also used for the design of online controllers which exploit the constrained model predictive control (MPC) theory. The ability of MPC to track highly constrained, heterogeneous discontinuous trajectories is examined. The performance and robustness of all these controllers (e.g. OVC, IVC, MPC) are thoroughly investigated with respect to several modeling uncertainties. Specifically, for robustness studies, variations in the flight conditions and helicopter inertial properties, as well as blade flexibility effects, are considered. Furthermore, the effectiveness of adaptive switching between controllers for the management of sensor failure during helicopter operations is studied using variance constrained controllers. Finally, the simultaneous design of the helicopter and control system is examined using simultaneous perturbation stochastic approximation in order to save active control energy. / Ph. D.

Extensive disturbance rejection methods have been established for time-invariant systems. However, the development of these techniques has not focused on application to time-periodic systems in particular until recently. The filtered-X LMS algorithm is regarded as the best disturbance rejection technique for aperiodic systems by many, as has been proven in the acoustics industry for rejecting unwanted noise. Since this is essentially a feedforward approach, we might expect its performance to be good with respect to time-periodic systems in which the disturbance frequency is already known. The work presented in this thesis is an investigation of the performance of the filtered-X LMS algorithm for disturbance rejection in time-periodic systems. Two cases are examined: a generalized linear, time-periodic system and the helicopter rotor blade in forward flight. Results for the generalized system show that the filtered-X LMS algorithm does converge for time-periodic disturbance inputs and can produce very small errors. For the helicopter rotor blade system the algorithm is shown to produce very small errors, with a 96%, or 14 dB, reduction in error from the open-loop system. The filtered-X LMS disturbance rejection technique is shown to provide a successful means of rejecting timeperiodic disturbances for time-periodic systems. / Master of Science