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The rotorcraft technology is very interesting area since last few decades due to variety of applications. One of the rotorcrafts is the quadrotor unmanned aerial vehicle (QUAV), which contains four rotors mounted on an airframe with an onboard controller. The QUAV is a highly nonlinear system and underactuated. Its controller design is very challenging task, and the need of controller is to make it autonomous based on mission planning. The purpose of this study is to design a controller for quadrotor UAV for attitude stabilization and trajectory tracking problem in presence of external environmental disturbances such as wind gust.

To address this problem, the model predictive control has been designed for attitude control and feedback linearization control for the position control using the linear parameter varying (LPV) approach. The trajectory tracking problem has been addressed using the circular trajectory and helical trajectory.

The simulation results show the efficient performance with good trajectory tracking even in presence of external disturbances in both the scenarios considered, one for circular trajectory tracking and other for helical trajectory tracking.

The novelty of the work came from using the LPV approach in controller design, which increases the robustness of the controller in presence of external disturbances.

Despite of the numerous characteristics of the multirotor unmanned aircraft systems (UASs), they have been termed as less energy-efficient compared to fixed-wing and helicopter counterparts. The purpose of this paper is to explore a more efficient multirotor configuration and to provide the robust and stable control system for it.

A heterogeneous multirotor configuration is explored in this paper, which employs a large rotor at the centre to provide majority of lift and three small tilted booms rotors to provide the control. Design provides the combined characteristics of both quadcopters and helicopters in a single UAS configuration, providing endurance of helicopters keeping the manoeuvrability, simplicity and control of quadcopters. In this paper, rotational as well as translational dynamics of the multirotor are explored. Cascade control system is designed to provide an effective solution to control the attitude, altitude and position of the rotorcraft.

One of the challenging tasks towards successful flight of such a configuration is to design a stable and robust control system as it is an underactuated system possessing complex non-linearities and coupled dynamics. Cascaded proportional integral (PI) control approach has provided an efficient solution with stable control performance. A novel motor control loop is implemented to ensure enhanced disturbance rejection, which is also validated through Dryden turbulence model and 1-cosine gust model.

Robustness and stability of the proposed control structure for such a dynamically complex UAS configuration is demonstrated with stable attitude and position performance, reference tracking and enhanced disturbance rejection.

The purpose of this paper is to design, build and test a low power high frequency (HF) transmitter that can be received by the Super Dual Auroral Radar Network (SuperDARN) radar installed at SANAE IV, the 4th South African National Antarctic Expedition Station. It is proposed that it may be possible to do propagation studies using the radar and the fixed frequency, ground-based HF transmitter beacon. Interpretation of the measurements can be used to study the ionosphere, especially Travelling Ionospheric Disturbances, which are signatures of atmospheric gravity waves.

In the absence of the actual deployment of the HF transmitter beacon in Antarctica, extensive simulations have been done to evaluate the expected performance of the transmitter in relation to the SuperDARN. A field trial has been executed between Hermanus (34.4241° S, 19.2247° E) and Pretoria (34.0558° S, 18.4589° E) in South Africa. In future, the beacon will be placed at the South Pole with its antenna radiating towards SANAE IV.

The HF transmitter conforms to the power and frequency stability requirements both during propagation tests conducted between Hermanus and Pretoria, as well as when the device was exposed to temperatures that ranged from +40°C to −45°C in a thermal chamber. Propagation in Antarctica is expected to differ from the field tests conducted due to the differences in density and dynamics of the polar ionosphere, compared to the mid-latitude ionosphere.

Space weather research, including forecasting atmospheric gravity waves and determining the expected electron density profile of the ionosphere, is of great scientific interest. The data received from the HF beacon can be used to study and characterize the ionosphere of the region between the South Pole and SANAE IV. Parameters of the ionosphere, such as electron density, geomagnetic storm effects, ionospheric motions and sky wave propagation paths will be better understood from analysing the signal received from this transmitter after it has been reflected and refracted by the ionosphere.

The purpose of this paper is to present the application of the neural-based estimation method, Neural-Gauss-Newton (NGN), using the real flight data of a small unmanned aerial vehicle (UAV).

The UAVs in general are lighter in weight and their flight is usually influenced by the atmospheric winds because of their relatively lower cruise speeds. During the presence of the atmospheric winds, the aerodynamic forces and moments get modified significantly and the accurate mathematical modelling of the same is highly challenging. This modelling inaccuracy during parameter estimation is routinely treated as the process noise. Furthermore, because of the limited dimensions of the small UAVs, the measurements are usually influenced by the disturbances caused by other subsystems. To handle these measurement and process noises, the estimation methods based on neural networks have been found reliable in the manned aircrafts.

Six sets of compatible longitudinal flight data of the designed UAV have been chosen to estimate the parameters using the NGN method. The consistency in the estimates is verified from the obtained mean and the standard deviation and the same has been validated by the proof-of-match exercise. It is evident from the results that the NGN method was able to perform on a par with the conventional maximum likelihood method.

This is a partial outcome of the research carried out in estimating parameters from the UAVs.

In operational tropical cyclone (TC) forecasting practice, there are usually many TC track guidances available from various official sources. When they do not converge, the guidances need to be ensembled by systematic approaches to formulate the best possible track as an official local TC track forecasting.

The main approach of the research is focused on finding an atmospheric environment favourable for TC survival (genesis) with the help of commonly accepted knowledge in atmospheric physics that reveals mechanism driving evolution and change of synoptic patterns in the atmosphere, using routinely available observational data, i.e. identification of TCF/TCR areas. The techniques developed are then applied to ensemble the TC track guidances available operationally to formulate an official TC track forecasting.

The results show that TC movement is very dependent on the atmospheric environment surrounding a TC. Whether the environment is favourable (TCF) or resistant (TCR) to survival of a TC system is a vital factor to determine where the TC moving. A systematic approach to the identification of the TCR/TCF areas is a key technique to ensemble available TC track guidances to formulate an official TC track forecasting.

In operational TC forecasting, TC track forecasting is the most important and difficult issue. The results indicate that the systematic approach to TC track forecasting has philosophical justification, solid scientific ground, sound logic and practical viability, and thus, make TC track forecasting easy and effective.

Project AURORA aims at the development of an unmanned airship capable of autonomous flight over user‐defined locations for aerial inspection and imagery acquisition. Presents a guidance control strategy for the trajectory path following of the AURORA airship, where the objective is to make the vehicle follow a set of pre‐defined points. The guidance strategy is based on a path tracking error generation methodology that takes into account both the distance and the angular errors of the airship with respect to the desired trajectory. The guidance system is composed of a path tracking guidance controller (as outer loop) and a heading controller (as inner loop), using the rudder deflection. Also proposes an additional roll controller, using the aileron input, in order to reduce rolling oscillations during yaw maneuvering and due to atmospheric turbulence.

This paper aims to propose output feedback-based control algorithms for the flight control system of a scaled, un-crewed helicopter in its hover flight mode.

The proposed control schemes are based on H_∞ control and composite nonlinear control. The gains of the output feedback controllers are obtained as the solution of a set of linear matrix inequalities (LMIs).

In the proposed schemes, the finite-time convergence of system states to trim condition is achieved with minimum deviation from the steady-state. As the proposed composite nonlinear output feedback design improves the transient response, it is well suited for a scaled helicopter flight. The use of measured output vector instead of the state vector or its estimate for feedback provides a simple control structure and eliminates the need for an observer in real-time application. The proposed control strategies are relevant to situations in which a simple controller is essential due to economic factors, reliability and hardware implementation constraints.

The proposed control strategies are relevant to situations in which a simple controller is essential due to economic factors, reliability and hardware implementation constraints. They also have significance in applications where the number of measurement quantities needs to be minimized such as in a fully functional rotor-craft unmanned aerial vehicle.

The developed output feedback control algorithms can be used in small-scale helicopters for numerous civilian and military applications.

This work addresses the LMI-based formulation and solution of an output feedback controller for a hovering un-crewed helicopter. The stability and robustness of the closed-loop system are proved mathematically and the performance of the proposed schemes is compared with an existing strategy via simulation studies.

In this paper, the suboptimal algorithm of adaptive control system is presented, which is specially adjusted for automatic flight control systems of general aviation and commuter aircraft, and unmanned aircraft (UMA) that conduct flights in atmospheric turbulence. At first, the method could be applied for correcting these changes in flight dynamics parameters, which cannot be compensated with the aid of an open adaptation loop. At the same time, full identification of aircraft model in real time is not required. This method is based on the estimation of most typical parameters of the aircraft mathematical model, which are most closely related to parameters, which are unmeasurable during flight, like aircraft real mass and position of center of gravity. The structure of an adaptation algorithm of aircraft flight control laws is based on the expert knowledge in the field of flight dynamics and is the result of optimization calculations. The examples which show attaining better flight comfort of the PZL M20, “Mewa” general aviation aircraft and quality improvement of the UMA, “Vector” pitch angle automatic control, have been presented.

This paper uses a historical case study, the controversy over the possibility of climatic extremes caused by hydrogen bomb tests on Pacific Ocean atolls during the 1950s, to show how, in a context of few scientific data and high uncertainty, political affiliations and public concerns shaped two types of argumentation, the “energy” and the “precautionary” arguments.

Systematic analysis of publications 1954–1956: scientific and semiscientific articles, publications of C.-N. Martin and contemporary newspaper articles, especially from the Asia–Pacific region.

First, epistemological and scientific reasoning about the likelihood of extreme natural events aligned to political convictions and pressure. Second, a geographical and social distribution of arguments: the relativizing “energy argument” prevailed in English-language scientific journals, while the “precautionary argument” dominated in popular journals and newspapers published worldwide. Third, while the “energy argument” attained general scientific consensus within two years, it lost out in the long run. The proponents of the “precautionary argument” raised relevant research questions that, though first rejected in the 1950s, later exposed the fallacies of the “energy argument” (shown for the case of the climatologist William W. Kellogg).

In contrast to the existing secondary literature, this paper presents a balanced view of the weaknesses and strengths of two lines of arguments in the 1950s. Further, this historical study sheds light on how once-discarded scientific theories may ultimately be reconsidered in a completely different political and scientific context, thus justifying the original precautionary argument.

This paper seeks to present a feedback error learning neuro‐controller for an unstable research helicopter.

Three neural‐aided flight controllers are designed to satisfy the ADS‐33 handling qualities specifications in pitch, roll and yaw axes. The proposed controller scheme is based on feedback error learning strategy in which the outer loop neural controller enhances the inner loop conventional controller by compensating for unknown non‐linearity and parameter uncertainties. The basic building block of the neuro‐controller is a nonlinear auto regressive exogenous (NARX) input neural network. For each neural controller, the parameter update rule is derived using Lyapunov‐like synthesis. An offline finite time training is used to provide asymptotic stability and on‐line learning strategy is employed to handle parameter uncertainty and nonlinearity.

The theoretical results are validated using simulation studies based on a nonlinear six degree‐of‐freedom helicopter undergoing an agile maneuver. The neural controller performs well in disturbance rejection is the presence of gust and sensor noise.

The neuro‐control approach presented in this paper is well suited to unmanned and small‐scale helicopters.

The study shows that the neuro‐controller meets the requirements of ADS‐33 handling qualities specifications of a helicopter.