Hybrid Formation Control of Unmanned Helicopters

Ph.DDOCTOR OF PHILOSOPH

Robust Dynamic Average Consensus for a Network of Agents with Time-varying Reference Signals

This paper presents continuous dynamic average consensus (DAC) algorithms for a group of agents to estimate the average of their time-varying reference signals cooperatively. We propose consensus algorithms that are robust to agents joining and leaving the network, at the same time, avoid the chattering phenomena and guarantee zero steady-state consensus error. Our algorithms are edge-based protocols with smooth functions in their internal structure to avoid the chattering effect. Furthermore, each agent is only capable of performing local computations and can only communicate with its local neighbors. For a balanced and strongly connected underlying communication graph, we provide the convergence analysis to determine the consensus design parameters that guarantee the agents' estimate of their average to asymptotically converge to the average of the time-varying reference signals of the agents. We provide simulation results to validate the proposed consensus algorithms and to perform a performance comparison of the proposed algorithms to existing algorithms in the literature

HIERARCHICAL HYBRID SYMBOLIC ROBOT MOTION PLANNING AND CONTROL

ABSTRACT This paper addresses the motion planning problem using hybrid symbolic techniques. The proposed approach develops a unified hierarchical hybrid control framework using a bismulation-based abstraction technique over the partitioned motion space that can be applied to autonomous aerial robots (3-D symbolic motion planning) or ground vehicles (2-D symbolic motion planning). The bisimulation relation between the abstracted model and the original continuous system guarantees that their behaviors are the same. This allows to design a discrete supervisor for the abstracted model, and then, the designed supervisor can be applied to the original system while the closed-loop behavior does not change. To apply the discrete supervisor to the original continuous system, an interface layer is developed, which on the one hand translates discrete commands of the supervisor to a continuous form applicable to the continuous plant and on the other hand, abstracts the continuous signals of the continuous low layer to discrete symbols understandable by the supervisor. The proposed algorithm is verified through implementation of a hybrid symbolic algorithm for the formation control of unmanned aerial vehicles

Leaderless Swarm Formation Control: From Global Specifications to Local Control Laws

This paper introduces a distributed leaderless swarm formation control framework to address the problem of collectively driving a swarm of robots to track a time-varying formation. The swarm's formation is captured by the trajectory of an abstract shape that circumscribes the convex hull of robots' positions and is independent of the number of robots and their ordering in the swarm. For each robot in the swarm, given global specifications in terms of the trajectory of the abstract shape parameters, the proposed framework synthesizes a control law that steers the swarm to track the desired formation using the information available at the robot's local neighbors. For this purpose, we generate a suitable local reference trajectory that the robot controller tracks by solving the input-output linearization problem. Here, we select the swarm output to be the parameters of the abstract shape. For this purpose, we design a dynamic average consensus estimator to estimate the abstract shape parameters. The abstract shape parameters are used as the swarm state feedback to generate a suitable robot trajectory. We demonstrate the effectiveness and robustness of the proposed control framework by providing the simulation of coordinated collective navigation of a group of car-like robots in the presence of robots and communication link failures

Multi-UAV trajectory planning problem using the difference of convex function programming

The trajectory planning problem for a swarm of multiple UAVs is known as a challenging nonconvex optimization problem, particularly due to a large number of collision avoidance constraints required for individual pairs of UAVs in the swarm. In this paper, we tackle this nonconvexity by leveraging the difference of convex function (DC) programming. We introduce the slack variables to relax and reformulate the collision avoidance conditions and employ the penalty function term to equivalently convert the problem into a DC form. Consequently, we construct a penalty DC algorithm in which we sequentially solve a set of convex optimization problems obtained by linearizing the collision avoidance constraint. The algorithm iteratively tightens the safety condition and reduces the objective cost of the planning problem and the additional penalty term. Numerical results demonstrate the effectiveness of the proposed approach in planning a large number of UAVs in congested space.Comment: This paper has been accepted for presentation at the 62nd IEEE Conference on Decision and Control (CDC 2023

Heterogeneous UAV Cells: An Effective Resource Allocation Scheme for Maximum Coverage Performance

This paper develops an effective approach for the 3D deployment of a heterogeneous set of unmanned aerial vehicles (UAVs) acting as aerial base stations that provide maximum wireless coverage for ground users in a given geographical area. This problem is addressed in two steps. First, in order to maximize the utilization of each UAV, its optimal flight altitude is found based on the UAV’s transmit power which provides maximum coverage radius on the ground. The UAVs are classified into separate groups based on their transmit powers and optimal flight altitudes. Next, given a repository of UAVs belonging to different classes, the proposed technique finds an optimal subset of the available UAVs along with their optimal 3D placement to provide the maximum network coverage for a given area on the ground with the minimum power consumption. This optimization problem is proved to be NP-hard, for which a novel algorithm is proposed to solve the problem. Simulation results demonstrate the effectiveness of the proposed solution and provide valuable insights into the performance of the Heterogeneous UAV-supported small cell networks

Parameter estimation of K-distributed sea clutter based on fuzzy inference and Gustafson-Kessel clustering

The detection performance of maritime radars is restricted by the unwanted sea echo or clutter. Although the number of these target-like data is small, they may cause false alarm and perturb the target detection. K-distribution is known as the best fit probability density function for the radar sea clutter. This paper proposes a novel approach to estimate the parameters of K-distribution, based on fuzzy Gustafson–Kessel clustering and fuzzy Takagi–Sugeno Kang modelling. The main contribution of the proposed method is the ability to estimate the parameters, given a small number of data which will usually be the case in practical applications. This is achieved by a pre-estimation using fuzzy clustering that provides a prior knowledge and forms a rough model to be fine tuned using the least square method. The algorithm also improves the calculations of shape and width of membership functions by means of clustering in order to improve the accuracy. The resultant estimator then acts to overcome the bottleneck of the existing methods in which it achieves a higher performance and accuracy in spite of small number of data

UAV Selection Methodology and Performance Evaluation to Support UAV-Enabled Bridge Inspection

This project performed preliminary work to support use of Unmanned Aerial Vehicles (UAV)-based for bridge inspections, providing an economical and safer alternative to conventional inspection practices. The main challenge is that most existing technologies rely on general-purpose UAV platforms and there is no verified methodology for UAV-enabled bridge inspection principles and relevant considerations to reliably obtain inspection data. There have been some efforts to use general-purpose commercially available UAVs for bridge inspection. However, the turbulent environment that often exists around bridges requires customized and enhanced UAV platforms with a higher level of robustness, taking into account the bridge type and structure as well as the weather conditions around the bridge. Additionally, the data-acquisition capabilities of commercially available UAVs need to be compared to those required for bridge inspection. Previously, there has not been a study to quantify the gap between the performance of the commercially available UAVs and ideal desired performances. In this multidisciplinary project, a comprehensive set of experiments were developed for selection, testing, and evaluation techniques of candidate UAVs, the complex nature of flying UAVs in close proximity to bridges was explored, and the limitations of UAV flight due to turbulent flows around bridge components and nearby terrain was assessed. Commercially available platforms for bridge inspection were selected, tested, and evaluated. Deliverables from this project include: (1) measurable metrics to evaluate the performance of UAVs for bridge inspection, (2) experiments to test the suitability of UAVs for bridge inspection, and (3) a comprehensive analysis near-bridge environment flow field. Computational analysis of air flow patterns near bridge elements shows that the bridge geometry creates areas of turbulence and flow variation which impact the control requirements of the UAV. Local weather conditions can amplify these areas. Test flights were performed at selected structures to provide additional insight into the flight and data collection capabilities of the UAVs under consideration. Findings and deliverables from this project will help NCDOT justify capital purchases made to support UAV-assisted inspection, as well as additional research needed to integrate UAVs into their current bridge inspection processes. Ultimately, this work supports a follow-up project to develop workflows and implementation tools for efficient UAV-enabled bridge inspection