1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

https://users.encs.concordia.ca/~ymzhang/publications/ARC32-2-98Zhang_pp.229-252.pdf

Bibliographical review on reconfigurable fault-tolerant control systems

The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. On the other hand, UAVs can operate as flying mobile terminals within a cellular network. Such cellular-connected UAVs can enable several applications ranging from real-time video streaming to item delivery. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as 3D deployment, performance analysis, channel modeling, and energy efficiency are explored along with representative results. Then, open problems and potential research directions pertaining to UAV communications are introduced. Finally, various analytical frameworks and mathematical tools, such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.

/pdf/a-tutorial-on-uavs-for-wireless-networks-applications-2heqa4nlgr.pdf

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems

The use of flying platforms such as unmanned aerial vehicles (UAVs), popularly known as drones, is rapidly growing. In particular, with their inherent attributes such as mobility, flexibility, and adaptive altitude, UAVs admit several key potential applications in wireless systems. On the one hand, UAVs can be used as aerial base stations to enhance coverage, capacity, reliability, and energy efficiency of wireless networks. On the other hand, UAVs can operate as flying mobile terminals within a cellular network. Such cellular-connected UAVs can enable several applications ranging from real-time video streaming to item delivery. In this paper, a comprehensive tutorial on the potential benefits and applications of UAVs in wireless communications is presented. Moreover, the important challenges and the fundamental tradeoffs in UAV-enabled wireless networks are thoroughly investigated. In particular, the key UAV challenges such as three-dimensional deployment, performance analysis, channel modeling, and energy efficiency are explored along with representative results. Then, open problems and potential research directions pertaining to UAV communications are introduced. Finally, various analytical frameworks and mathematical tools such as optimization theory, machine learning, stochastic geometry, transport theory, and game theory are described. The use of such tools for addressing unique UAV problems is also presented. In a nutshell, this tutorial provides key guidelines on how to analyze, optimize, and design UAV-based wireless communication systems.

A key problem in cooperative robotics is the maintenance of a geometric configuration during movement. To address this problem, the concept of a Virtual Structure is introduced. Using this idea, a general control strategy is developed to force an ensemble of robots to behave as if they were particles embedded in a rigid structure. The method was instantiated and tested using both simulation and experimentation with a set of 3 differential drive mobile robots. Results are presented that demonstrate that this approach is capable of achieving high precision movement that is fault tolerant and exhibits graceful degradation of performance. In addition, this algorithm does not require leader selection as in other cooperative robotic strategies. Finally, the method is inherently highly flexible in the kinds of geometric formations that can be maintained.

High Precision Formation Control of Mobile Robots Using Virtual Structures

A composite nonlinear feedback tracking control law based on a nominal linear controller is proposed. The design yields nonlinear feedback laws that both increase the speed of the closed-loop system response to the command input and reduce the overshoot, while not imperilling the small signal performance achieved by the nominal linear feedback controller in the face of actuator amplitude saturation. A modern flight control application is used to demonstrate the effectiveness of the design.

Toward improvement of tracking performance nonlinear feedback for linear systems

Thetightformatione ightcontrolproblemisaddressed.Theformationconsistsofa lead andwingaircraft,where the wing e ies in tight formation with the lead, such that the lead’ s trailing vortices aerodynamically couple the lead and the wing, and a reduction in the formation’ s induced drag is achieved. A controller (i.e., a formation-hold autopilot for the wing aircraft ) is designed such that the formation’ s geometry is maintained in the face of lead aircraft maneuvers. In the formation e ight control system, the wing and lead aircraft dynamics are coupled due to kinematic effects, and, in the case of tight formations, additional aerodynamic coupling effects are introduced. These additional aerodynamic coupling effects are properly modeled. The most signie cant aerodynamic coupling effect introduced by tight formation e ight entails the coupling of the lateral/directional channel into the altitudehold autopilot channel. It is shown that formation-hold autopilots designed ignoring the aerodynamic coupling effect yield satisfactory performance in tight formation e ight. Nomenclature b = wingspan of wing CLL = lift coefe cient of the lead aircraft S = surface area of wing VSW = sidewash W = wash vector WUW = upwash

Tight Formation Flight Control

Path planning arid coordination of multiple unmanned air vehicles, so as to jointly reach a target area while minimizing the flight's exposure to radar is addressed. A hierarchical decomposition approach is pursued. The optimal path planning for a single vehicle is performed to minimize exposure while a timing constraint is imposed. Each vehicle plans it's own path. The path planning consists of a two step procedure. First, a polygonal path to the optimal path is obtained. Second, the initial path is refined to a flyable path by acknowledging maneuverability constraints. The coordination of the timing of the vehicles' arrival at the target area is performed by the higher level coordination agent. The coordination agent calculates a team estimated time of arrival from a sensitivity function that is calculated and communicated by each of the UAVs. This approach is illustrated in a simulation of a simplified scenario where three UAVs are coordinated to arrive at the target area simultaneously. Popup threats are introduced that result in dynamic inflight replanning. Simulation results show that coarse grid planning and decentralized coordinated control can rapidly respond to new threats while satisfying rigid rendezvous timing constaints.

UAV Cooperative Path Planning

This paper addresses the development of cooperative rendezvous and cooperative target classification agents in a hierarchical distributed control system for unmanned aerospace vehicles. For cooperative rendezvous, a Voronoi based polygonal path is generated to minimize exposure to radar. The rendezvous agent minimizes team exposure from Individual coordination functions while satisfying stringent timing constraints. For cooperative target classification, templates are developed, optimal trajectories are followed, and adjacent vehicles are assigned to view at complementary aspect angles. Views axe statistically combined to maximize the probability of correct target classification over various aspect angles.

UAV cooperative control

In this work, an efficient static system identification method is developed that incorporates prior information. The prior information from flight mechanics enhances the performance of the identification algorithm, yielding more accurate parameter estimates. The proposed static system identification approach is superior to dynamic system identification for on-line identification of rapidly varying plant parameters, making it suitable for adaptive and reconfigurable control. The effectiveness of the static system identification scheme is illustrated in an example problem. A derivative F-16 aircraft control surface (elevator) failure is simulated and the abruptly changing pitch channel control system's stability and control derivatives are successfully identified on-line in the presence of unmodeled dynamics, process noise (clear air turbulence), and realistic measurement noise.

Meir Pachter

Papers

Toward improvement of tracking performance nonlinear feedback for linear systems

Tight Formation Flight Control

UAV Cooperative Path Planning

UAV cooperative control

System identification for adaptive and reconfigurable control