Kite Generator System Periodic Motion Planning Via Virtual Constraints

International audienceThis paper presents a new control strategy for Kite Generator System (KGS). The proposed feedback strategy is based on motion planning using the virtual constraint approach and ensures exponential orbital stability of the desired trajectory. The strategy is detailed, applied and tested via numerical simulations and showed good convergence to a desired periodic motion

Control of Towing Kites for Seagoing Vessels

In this paper we present the basic features of the flight control of the SkySails towing kite system. After introduction of coordinate definitions and basic system dynamics we introduce a novel model used for controller design and justify its main dynamics with results from system identification based on numerous sea trials. We then present the controller design which we successfully use for operational flights for several years. Finally we explain the generation of dynamical flight patterns.Comment: 12 pages, 18 figures; submitted to IEEE Trans. on Control Systems Technology; revision: Fig. 15 corrected, minor text change

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

Sensors and Navigation Algorithms for Flight Control of Tethered Kites

We present the sensor setup and the basic navigation algorithm used for the flight control of the SkySails towing kite system. Starting with brief summaries on system setup and equations of motion of the tethered kite system, we subsequently give an overview of the sensor setup, present the navigation task and discuss challenges which have to be mastered. In the second part we introduce in detail the inertial navigation algorithm which has been used for operational flights for years. The functional capability of this algorithm is illustrated by experimental flight data. Finally we suggest a modification of the algorithms as further development step in order to overcome certain limitations.Comment: 6 pages, 9 figures, submitted to European Control Conference (ECC) 201

Energy production control of an experimental kite system in presence of wind gusts

International audienceThe growing need of energy, global warming and recent nuclear power plant accidents have shown that renewable energies need to be developed for tomorrow's world. Wind energy is generally harvested using wind turbines. Unfortunately, these systems have some drawbacks such as their cost, and the amount of steel and concrete used for construction. As their size grows, their complexity increases exponentially. This paper studies an alternative solution for the production of wind energy, using a kite's traction force. The aim of this paper is to control the amount of energy produced by the kite, and to be able to fly it safely in the presence of strong wind gusts. Our theoretical work has been implemented in a scale model flying autonomously in a wind tunnel. The proposed control strategy has led to control the system output power with an accuracy greater than 95%, with unknown wind speeds varying from 7.5 to 9 m/s

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field

Aerodynamic Analysis and Optimization of Gliding Locust Wing Using Nash Genetic Algorithm

Natural fliers glide and minimize wing articulation to conserve energy for endured and long range flights. Elucidating the underlying physiology of such capability could potentially address numerous challenging problems in flight engineering. This study investigates the aerodynamic characteristics of an insect species called desert locust (Schistocerca gregaria) with an extraordinary gliding skills at low Reynolds number. Here, locust tandem wings are subjected to a computational fluid dynamics (CFD) simulation using 2D and 3D Navier-Stokes equations revealing fore-hindwing interactions, and the influence of their corrugations on the aerodynamic performance. Furthermore, the obtained CFD results are mathematically parameterized using PARSEC method and optimized based on a novel fusion of Genetic Algorithms and Nash game theory to achieve Nash equilibrium being the optimized wings. It was concluded that the lift-drag (gliding) ratio of the optimized profiles were improved by at least 77% and 150% compared to the original wing and the published literature, respectively. Ultimately, the profiles are integrated and analyzed using 3D CFD simulations that demonstrated a 14% performance improvement validating the proposed wing models for further fabrication and rapid prototyping presented in the future study

Responses of a Locust Looming Sensitive Neuron, Flight Muscle Activity and Body Orientation to Changes in Object Trajectory, Background Complexity, and Flight Condition

Survival is one of the highest priorities of any animal. Interaction in the environment with conspecifics, predators, or objects, is driven by evolution of systems that can efficiently and rapidly respond to potential collision with these stimuli. Flight introduces further complexity for a collision avoidance system, requiring an animal to compute air speed, wind speed, ground speed, as well as transverse and longitudinal image flow, all within the context of detecting an approaching object. Understanding the mechanisms underlying neural control and coordination of motor systems to produce behaviours in response to the natural environment is a main goal of neuroethology. Locusts have a tractable nervous system, and a robust, reproducible collision avoidance response to looming stimuli. This tractable system allows recording from the nerve cord and flight muscles with precision and reliability, allowing us to answer important questions regarding the neuronal control of muscle coordination and, in turn, collision avoidance behaviour during flight. In flight, a collision avoidance behaviour will most often be a turn away from the approaching stimulus. I tested the hypothesis that during loosely tethered flight, synchrony between flight muscles increases just prior to the initiation of a turn and that muscle synchronization would correlate with body orientation changes during flight steering. I found that hind and forewing flight muscle synchronization events correlated strongly with forewing flight muscle latency changes, and to pitch and roll body orientation changes in response to a lateral looming visual stimulus. These findings led me to investigate further the role of the looming-sensitive descending contralateral movement detector (DCMD) neuron in flight muscle coordination and the initiation of forewing asymmetry in rigidly tethered locusts that generate a flight-like rhythm. By conducting simultaneous recordings from the nerve cord, forewing flight muscles, and visually recording the wing positions within the same flying animal, I hypothesized that DCMD burst properties would correlate with flight muscle activity changes and the initiation of wing asymmetry associated with turning behaviour. Furthermore, I accessed the effect of manipulating background complexity of the locust’s visual environment, looming object trajectory, and the putative effect of mechanosensory feedback during flight, on DCMD burst firing rate properties. DCMD burst properties were affected by changes in background complexity and object trajectory, and most interestingly during flight. This suggests that reafferent feedback from the flight motor system modulates the DCMD signal, and therefore represents a more naturalistic representation of collision avoidance behaviour. A pivotal discovery in my study was the temporal role of bursting in collision avoidance behaviour. I found that the first burst in a DCMD spike train represents the earliest detectable neuronal event correlated with muscle activity changes and the creation of wing asymmetry. I found strong correlations across all object trajectories and background complexities, between the timing of the first bursts, flight muscle activity changes and the initiation of wing asymmetry. These findings reinforce the importance of the temporal properties of DCMD bursting in collision avoidance behaviour