Virtual Structures Based Autonomous Formation Flying Control for Small Satellites

Many space organizations have a growing need to fly several small satellites close together in order to collect and correlate data from different satellite sensors. To do this requires teams of engineers monitoring the satellites orbits and planning maneuvers for the satellites every time the satellite leaves its desired trajectory or formation. This task of maintaining the satellites orbits quickly becomes an arduous and expensive feat for satellite operations centers. This research develops and analyzes algorithms that allow satellites to autonomously control their orbit and formation without human intervention. This goal is accomplished by developing and evaluating a decentralized, optimization-based control that can be used for autonomous formation flight of small satellites. To do this, virtual structures, model predictive control, and switching surfaces are used. An optimized guidance trajectory is also develop to reduce fuel usage of the system. The Hill-Clohessy-Wiltshire equations and the D\u27Amico relative orbital elements are used to describe the relative motion of the satellites. And a performance comparison of the L1, L2, and L∞ norms is completed as part of this work. The virtual structure, MPC based framework combined with the switching surfaces enables a scalable method that allows satellites to maneuver safely within their formation, while also minimizing fuel usage

Self-triggered Consensus Control of Multi-agent Systems from Data

This paper considers self-triggered consensus control of unknown linear multi-agent systems (MASs). Self-triggering mechanisms (STMs) are widely used in MASs, thanks to their advantages in avoiding continuous monitoring and saving computing and communication resources. However, existing results require the knowledge of system matrices, which are difficult to obtain in real-world settings. To address this challenge, we present a data-driven approach to designing STMs for unknown MASs building upon the model-based solutions. Our approach leverages a system lifting method, which allows us to derive a data-driven representation for the MAS. Subsequently, a data-driven self-triggered consensus control (STC) scheme is designed, which combines a data-driven STM with a state feedback control law. We establish a data-based stability criterion for asymptotic consensus of the closed-loop MAS in terms of linear matrix inequalities, whose solution provides a matrix for the STM as well as a stabilizing controller gain. In the presence of external disturbances, a model-based STC scheme is put forth for $\mathcal{H}_{\infty}$-consensus of MASs, serving as a baseline for the data-driven STC. Numerical tests are conducted to validate the correctness of the data- and model-based STC approaches. Our data-driven approach demonstrates a superior trade-off between control performance and communication efficiency from finite, noisy data relative to the system identification-based one

Coordinated multi-robot formation control

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Distributed Tracking Control Design for Leader-Follower Multi-Agent Systems

Multi-agent systems (MASs) have been widely recognized as a key way to model, analyze, and engineer numerous kinds of complex systems composed of distributed agents. The aim of this dissertation is to study control design for leader-follower MASs such that a group of followers can track a specified leader via distributed decision making based on distributed information. We identify and consider several critical problems that have stood in the way of distributed tracking control synthesis and analysis. Specifically, they include: 1) limited information access by the followers to the leader, 2) effects of external disturbances, 3) complicated dynamics of agents, and 4) energy efficiency. To overcome the first three problems, we take a lead with the design of distributed-observer-based control, with the insight that distributed observers can enable agents to recover unknown quantities in a collective manner for the purpose of control. To deal with the fourth problem, we propose the first study of MAS tracking control conscious of nonlinear battery dynamics to increase operation time and range. The dissertation will present the following research contributions. First, we propose the notion of designing distributed observers to make all the followers aware of the leader's state and driving input, regardless of the network communication topology, and perform tracking controller design based on the observers. Second, we further develop distributed disturbance observers and observer-based robust tracking control to handle the scenario when all the leader and followers are affected by unknown disturbances only bounded in rates of change. The third contribution lies in treating a leader-follower MAS with high-order, nonlinear dynamics. Assuming the availability of very limited measurement data, we substantively expand the idea of observer-based control to develop a catalog of distributed observers such that the followers can reconstruct large amounts of information necessary for effective tracking control. Finally, we propose a distributed predictive optimization method to integrate onboard battery management with tracking control for long-endurance operation of an electric-powered MAS. The proposed dissertation research offers new insights and a set of novel tools to enhance the control performance of leader-follower MASs. The results also have a promise to find potential applications in other types of MASs

Data-driven Polytopic Output Synchronization of Heterogeneous Multi-agent Systems from Noisy Data

This paper proposes a novel approach to addressing the output synchronization problem in unknown heterogeneous multi-agent systems (MASs) using noisy data. Unlike existing studies that focus on noiseless data, we introduce a distributed data-driven controller that enables all heterogeneous followers to synchronize with a leader's trajectory. To handle the noise in the state-input-output data, we develop a data-based polytopic representation for the MAS. We tackle the issue of infeasibility in the set of output regulator equations caused by the noise by seeking approximate solutions via constrained fitting error minimization. This method utilizes measured data and a noise-matrix polytope to ensure near-optimal output synchronization. Stability conditions in the form of data-dependent semidefinite programs are derived, providing stabilizing controller gains for each follower. The proposed distributed data-driven control protocol achieves near-optimal output synchronization by ensuring the convergence of the tracking error to a bounded polytope, with the polytope size positively correlated with the noise bound. Numerical tests validate the practical merits of the proposed data-driven design and theory

A Review of Consensus-based Multi-agent UAV Applications

In this paper, a review of distributed control for multi-agent systems is proposed, focusing on consensus-based applications. Both rotary-wing and fixed-wing Unmanned Aerial Vehicles (UAVs) are considered. On one side, methodologies and implementations based on collision and obstacle avoidance through consensus are analyzed for multirotor UAVs. On the other hand, a target tracking through consensus is considered for fixed-wing UAVs. This novel approach to classify the literature could help researchers to assess the outcomes achieved in these two directions in view of potential practical implementations of consensus-based methodologies

Cooperative Control of Multiple Wheeled Mobile Robots: Normal and Faulty Situations

Recently, cooperative control of multiple unmanned vehicles has attracted a great deal of attention from scientific, industrial, and military aspects. Groups of unmanned ground, aerial, or marine vehicles working cooperatively lead to many advantages in a variety of applications such as: surveillance, search and exploration, cooperative reconnaissance, environmental monitoring, and cooperative manipulation, respectively. During mission execution, unmanned systems should travel autonomously between different locations, maintain a pre-defined formation shape, avoid collisions of obstacles and also other team members, and accommodate occurred faults and mitigate their negative effect on mission execution. The main objectives of this dissertation are to design novel algorithms for single wheeled mobile robots (WMRs) trajectory tracking, cooperative control and obstacle avoidance of WMRs in fault-free situations. In addition, novel algorithms are developed for fault-tolerant cooperative control (FTCC) with integration of fault detection and diagnosis (FDD) scheme. In normal/fault-free cases, an integrated approach combining input-output feedback linearization and distributed model predictive control (MPC) techniques is designed and implemented on a team of WMRs to accomplish the trajectory tracking as well as the cooperative task. An obstacle avoidance algorithm based on mechanical impedance principle is proposed to avoid potential collisions of surrounding obstacles. Moreover, the proposed control algorithm is implemented to a team of WMRs for pairing with a team of unmanned aerial vehicles (UAVs) for forest monitoring and fire detection applications. When actuator faults occur in one of the robots, two cases are explicitly considered: i) if the faulty robot cannot complete its assigned task due to a severe fault, then the faulty robot has to get out from the formation mission, and an FTCC strategy is designed such that the tasks of the WMRs team are re-assigned to the remaining healthy robots to complete the mission with graceful performance degradation. Two methods are used to investigate this case: the Graph Theory, and formulating the FTCC problem as an optimal assignment problem; and ii) if the faulty robot can continue the mission with degraded performance, then the other team members reconfigure the controllers considering the capability of the faulty robot. Thus, the FTCC strategy is designed to re-coordinate the motion of each robot in the team. Within the proposed scheme, an FDD unit using a two-stage Kalman filter (TSKF) to detect and diagnose actuator faults is presented. In case of using any other nonlinear controller in fault-free case rather than MPC, and in case of severe fault occurrence, another FTCC strategy is presented. First, the new reconfiguration is formulated by an optimal assignment problem where each healthy WMR is assigned to a unique place. Second, the new formation can be reconfigured, while the objective is to minimize the time to achieve the new formation within the constraints of the WMRs' dynamics and collision avoidance. A hybrid approach of control parametrization and time discretization (CPTD) and particle swarm optimization (PSO) is proposed to address this problem. Since PSO cannot solve the continuous control inputs, CPTD is adopted to provide an approximate piecewise linearization of the control inputs. Therefore, PSO can be adopted to find the global optimum solution. In all cases, formation operation of the robot team is based on a leader-follower approach, whilst the control algorithm is implemented in a distributed manner. The results of the numerical simulations and real experiments demonstrate the effectiveness of the proposed algorithms in various scenarios