A trajectory controller for kite power systems with wind gust handling capabilities

In this paper, we address the generation of electrical power using Airborne Wind Energy Systems, comprising a kite connected through a tether to a generator on the ground. We design a controller to steer the kite to follow a pre-defined periodic path, which includes a production mode, a tether retrieval mode, and a safe mode capable of handling wind gusts. Copyrigh

Airborne Wind Energy Systems: Modelling, Simulation and Trajectory Control

In this dissertation, we address the generation of electrical power using Airborne Wind Energy Systems, comprising a kite connected through a tether to a generator on the ground. We do simulations of the kite model, both in 2D and 3D. Then we design a controller to steer the kite to follow a pre-defined periodic path, which includes a production mode, a tether retrieval mode, and a safe mode capable of handling wind gusts. Finally, we desing a controller based on a nonlinear guidance logic for trajectory tracking and compare the results

Evolutionary robotics in high altitude wind energy applications

Recent years have seen the development of wind energy conversion systems that can exploit the superior wind resource that exists at altitudes above current wind turbine technology. One class of these systems incorporates a flying wing tethered to the ground which drives a winch at ground level. The wings often resemble sports kites, being composed of a combination of fabric and stiffening elements. Such wings are subject to load dependent deformation which makes them particularly difficult to model and control. Here we apply the techniques of evolutionary robotics i.e. evolution of neural network controllers using genetic algorithms, to the task of controlling a steerable kite. We introduce a multibody kite simulation that is used in an evolutionary process in which the kite is subject to deformation. We demonstrate how discrete time recurrent neural networks that are evolved to maximise line tension fly the kite in repeated looping trajectories similar to those seen using other methods. We show that these controllers are robust to limited environmental variation but show poor generalisation and occasional failure even after extended evolution. We show that continuous time recurrent neural networks (CTRNNs) can be evolved that are capable of flying appropriate repeated trajectories even when the length of the flying lines are changing. We also show that CTRNNs can be evolved that stabilise kites with a wide range of physical attributes at a given position in the sky, and systematically add noise to the simulated task in order to maximise the transferability of the behaviour to a real world system. We demonstrate how the difficulty of the task must be increased during the evolutionary process to deal with this extreme variability in small increments. We describe the development of a real world testing platform on which the evolved neurocontrollers can be tested

Design of Flight Control Laws for a Novel Stratospheric Dual-Aircraft Platform

Dual-aircraft platform (DAP) is a novel concept that features two glider-like unmanned aerial systems (UAS) tethered via a thin adjustable cable allowing them to sail back-and-forth, without propulsion, using vertical wind shear. DAP offers the potential of a low-cost atmospheric satellite. This thesis presents the results of an initiative to demonstrate this novel flight concept through modeling, simulation, and flight testing at Embry-Riddle Aeronautical University (ERAU). A realistic simulation environment, described herein, was developed to support the development and testing of flight control systems. This environment includes nonlinear aerodynamic models for the aircraft, a multi-element cable dynamics model, propeller-motor thrust model, control surface actuator models, and permits time-varying wind profiles. This simulator offers both pilot-in-the-loop control and autonomous sailing flight control, and X-Plane interface to provide visualization cues. An intensive flight test program, described herein, was conducted to support the validation of the DAP concept. MAXA Pro 4m gliders were assembled, instrumented, and flight tested in an effort to physically demonstrate the sailing mode of flight. The flight test program described here focuses on the capability to sail with one aircraft (i.e., fly without propulsion) while towing (i.e., pulling) a moving truck as an intermediate step towards the more complex scenario of sailing with two connected aircraft. Two vital elements of the flight software are implemented and analyzed herein. The accuracy of wind estimation techniques is evaluated using flight testing. The robustness of an L1 adaptive controller is evaluated within the flight simulation environment by comparing its performance with a conventional controller

Control system design using evolutionary algorithms for autonomous shipboard recovery of unmanned aerial vehicles

The capability of autonomous operation of ship-based Unmanned Aerial Vehicles (UAVs) in extreme sea conditions would greatly extend the usefulness of these aircraft for both military and civilian maritime purposes. Maritime operations are often associated with Vertical Take-Off and Landing (VTOL) procedures, even though the advantages of conventional fixed-wing aircraft over VTOL aircraft in terms of flight speed, range and endurance are well known. In this work, current methods of shipboard recovery are analysed and the problems associated with recovery in adverse weather conditions are identified. Based on this analysis, a novel recovery method is proposed. This method, named Cable Hook Recovery, is intended to recover small to medium-size fixed-wing UAVs on frigate-size vessels. It is expected to have greater operational capabilities than the Recovery Net technique, which is currently the most widely employed method of recovery for similar class of UAVs, potentially providing safe recovery even in very rough sea and allowing the choice of approach directions. The recovery method is supported by the development of a UAV controller that realises the most demanding stage of recovery, the final approach. The controller provides both flight control and guidance strategy that allow fully autonomous recovery of a fixed-wing UAV. The development process involves extensive use of specially tailored Evolutionary Algorithms and represents the major contribution of this work. The Evolutionary Design algorithm developed in this work combines the power of Evolutionary Strategies and Genetic Programming, enabling automatic evolution of both the structure and parameters of the controller. The controller is evolved using a fully coupled nonlinear six-degree-of-freedom UAV model, making linearisation and trimming of the model unnecessary. The developed algorithm is applied to both flight control and guidance problems with several variations, from optimisation of a routine PID controller to automatic control laws synthesis where no a priori data available. It is demonstrated that Evolutionary Design is capable of not only optimising, but also solving automatically the real-world problems, producing human-competitive solutions. The designed UAV controller has been tested comprehensively for both performance and robustness in a nonlinear simulation environment and has been found to allow the aircraft to be recovered in the presence of both large external disturbances and uncertainty in the simulation models

Aeronautical engineering. A continuing bibliography with indexes

This bibliography lists 326 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1982. Topics on aeronautical engineering and aerodynamics such as flight control systems, avionics, computer programs, computational fluid dynamics and composite structures are covered