Model-Matching type-methods and Stability of Networks consisting of non-Identical Dynamic Agents

Many recent approaches of distributed control over networks of dynamical agents rely on the assumption of identical agent dynamics. In this paper we propose a systematic method for removing this assumption, leading to a general approach for distributed-control stabilization of networks of non-identical dynamics. Local agents are assumed to share a minimal set of structural properties, such as input dimension, state dimension and controllability indices, which are generically satisfied for parametric families of systems. Our approach relies on the solution of certain model-matching type problems using local state-feedback and input matrix transformations which map the agent dynamics to a target system, selected to minimize the joint control effort of the local feedback-control schemes. By adapting a well-established distributed LQR control design methodology to our framework, the stabilization problem for a network of non-identical dynamical agents is solved. The applicability of our approach is illustrated via a simple UAV formation control problem

Approximating linearizations for nonlinear systems

The following problem is examined: given a nonlinear control system dot-x(t) = f(x(t)) + the sum to m terms(i=1) u sub i (t)g sub i (x(t)) on R(n) and a point x(0) in R(n), approximate the system near x(0) by a linear system. One approach is to use the usual Taylor series linearization. However, the controllability properties of both the nonlinear and linear systems depend on certain Lie brackets of the vector field under consideration. This suggests that a linear approximation based on Lie bracket matching should be constructed at x(0). In general, the linearizations based on the Taylor method and the Lie bracket approach are different. However, under certain mild assumptions, it is shown that there is a coordinate system for R(n) near x(0) in which these two types of linearizations agree. The importance of this agreement is indicated by examining the time responses of the nonlinear system and its linear approximation and comparing the lower order kernels in Volterra expansions of each

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

Optically indicating surface de-icing fluids

An optically indicating de-icing solution for surfaces comprising a freezing point depressant liquid and a compound which exhibits a visually observable change as solid phase domains become present in the solution is disclosed. When applied to a surface, particularly the surface(s) of aircraft, the formation of solid phase ice domains in the liquid provides a distinct and visible change in the appearance of the solution. This allows a determination of ice formation upon the aircraft as well as a determination of the effectiveness of the de-icing solution

Autonomous three-dimensional formation flight for a swarm of unmanned aerial vehicles

This paper investigates the development of a new guidance algorithm for a formation of unmanned aerial vehicles. Using the new approach of bifurcating potential fields, it is shown that a formation of unmanned aerial vehicles can be successfully controlled such that verifiable autonomous patterns are achieved, with a simple parameter switch allowing for transitions between patterns. The key contribution that this paper presents is in the development of a new bounded bifurcating potential field that avoids saturating the vehicle actuators, which is essential for real or safety-critical applications. To demonstrate this, a guidance and control method is developed, based on a six-degreeof-freedom linearized aircraft model, showing that, in simulation, three-dimensional formation flight for a swarm of unmanned aerial vehicles can be achieved

Disassortativity in Biological and Supply Chain Networks

Network science has allowed researchers to model complex real world systems as networks in order to identify non trivial topological patterns. Degree correlations (or assortativity) is one such non trivial topological property, which indicates the extent to which nodes with similar degrees tend to pair up with each other. Biological networks have long been known to display anti-degree correlations (disassortativity), where highly connected nodes tend to avoid linking with each other. However, the mechanism underlying this structural organisation remain not well understood. Recent work has suggested that in some instances, disassortativity can be observed merely as a model artefact due to simple network representations not allowing multiple link formations between the node pairs. This phenomena is known as structural disassortativity. In this paper, we analyse datasets from two distinct classes of networks, namely; man made supply chain networks and naturally occurring biological networks. We examine whether the observed disassortativity in these networks are structurally induced or owing to some external process. Degree preserving randomisation is used to generate an ensemble of null models for each network. Comparison of the degree correlation profiles of each network, against that of their degree preserving randomised counterparts reveal whether the observed disassortativity in each network is of structural nature or not. We find that in all biological networks, the observed disassortativity is of structural nature, meaning their disassortative nature can be fully explained by their respective degree distributions, without attribution to any underlying mechanism which drives the system towards disassortativity. However, in supply chain networks, we find one case where disassortativity is structurally induced and in other cases where it is mechanistically driven. We conclude by emphasizing on ruling out structural disassortativity in future research, prior to investigating mechanisms underlying disassortativity in networks

Frostwing Co-Operation in Aircraft Icing Research

e aerodynamic effects of Cold Soaked Fuel Frost have become increasingly significant as airworthiness authorities have been asked to allow it during aircraft take-off. The Federal Aviation Administration and the Finnish Transport Safety Agency signed a Research Agreement in aircraft icing research in 2015 and started a research co-operation in frost formation studies, computational fluid dynamics for ground de/anti-icing fluids, and de/anti-icing fluids aerodynamic characteristics. The main effort has been so far on the formation and aerodynamic effects of CSFF. To investigate the effects, a generic high-lift common research wind tunnel model and DLR-F15 airfoil, representing the wing of a modern jet aircraft, was built including a wing tank cooling system. Real frost was generated on the wing in a wind tunnel test section and the frost thickness was measured with an Elcometer gauge. Frost surface geometry was measured with laser scanning and photogrammetry. The aerodynamic effect of the frost was studied in a simulated aircraft take-off sequence, in which the speed was accelerated to a typical rotation speed and the wing model was then rotated to an angle of attack used at initial climb. Time histories of the lift coefficient were measured with a force balance. The experiments showed that depending on the ambient temperature the frost may evaporate/melt during the take-off sequence. Lift losses after rotation with CSFF contamination at ambient temperatures of 4 to 7C above freezing point were measured to be 4 to 5 % for roughness values, k/c, below 10(exp -3). For comparison, lift loss tests with typical anti-icing fluids were to roughly equal lift losses. This paper gives an overview of the performed activities

On Wings of the Minimum Induced Drag: Spanload Implications for Aircraft and Birds

For nearly a century Ludwig Prandtl's lifting-line theory remains a standard tool for understanding and analyzing aircraft wings. The tool, said Prandtl, initially points to the elliptical spanload as the most efficient wing choice, and it, too, has become the standard in aviation. Having no other model, avian researchers have used the elliptical spanload virtually since its introduction. Yet over the last half-century, research in bird flight has generated increasing data incongruous with the elliptical spanload. In 1933 Prandtl published a little-known paper presenting a superior spanload: any other solution produces greater drag. We argue that this second spanload is the correct model for bird flight data. Based on research we present a unifying theory for superior efficiency and coordinated control in a single solution. Specifically, Prandtl's second spanload offers the only solution to three aspects of bird flight: how birds are able to turn and maneuver without a vertical tail; why birds fly in formation with their wingtips overlapped; and why narrow wingtips do not result in wingtip stall. We performed research using two experimental aircraft designed in accordance with the fundamentals of Prandtl's second paper, but applying recent developments, to validate the various potentials of the new spanload, to wit: as an alternative for avian researchers, to demonstrate the concept of proverse yaw, and to offer a new method of aircraft control and efficiency

Distributed leader following of an active leader for linear heterogeneous multi-agent systems

This paper considers a leader-following problem for a group of heterogeneous linear time invariant (LTI) followers that are interacting over a directed acyclic graph. Only a subset of the followers has access to the state of the leader in specific sampling times. The dynamics of the leader that generates its sampled states is unknown to the followers. For interaction topologies in which the leader is a global sink in the graph, we propose a distributed algorithm that allows the followers to arrive at the sampled state of the leader by the time the next sample arrives. Our algorithm is a practical solution for a leader-following problem when there is no information available about the state of the leader except its instantaneous value at the sampling times. Our algorithm also allows the followers to track the sampled state of the leader with a locally chosen offset that can be time-varying. When the followers are mobile agents whose state or part of their state is their position vector, the offset mechanism can be used to enable the followers to form a transnational invariant formation about the sampled state of the leader. We prove that the control input of the followers to take them from one sampled state to the next one is minimum energy. We also show in case of the homogeneous followers, after the first sampling epoch the states and inputs of all the followers are synchronized with each other. Numerical examples demonstrate our results