High-Order Leader-Follower Tracking Control under Limited Information Availability

Limited information availability represents a fundamental challenge for control of multi-agent systems, since an agent often lacks sensing capabilities to measure certain states of its own and can exchange data only with its neighbors. The challenge becomes even greater when agents are governed by high-order dynamics. The present work is motivated to conduct control design for linear and nonlinear high-order leader-follower multi-agent systems in a context where only the first state of an agent is measured. To address this open challenge, we develop novel distributed observers to enable followers to reconstruct unmeasured or unknown quantities about themselves and the leader and on such a basis, build observer-based tracking control approaches. We analyze the convergence properties of the proposed approaches and validate their performance through simulation

Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

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

Adaptive Fuzzy Tracking Control with Global Prescribed-Time Prescribed Performance for Uncertain Strict-Feedback Nonlinear Systems

Adaptive fuzzy control strategies are established to achieve global prescribed performance with prescribed-time convergence for strict-feedback systems with mismatched uncertainties and unknown nonlinearities. Firstly, to quantify the transient and steady performance constraints of the tracking error, a class of prescribed-time prescribed performance functions are designed, and a novel error transformation function is introduced to remove the initial value constraints and solve the singularity problem in existing works. Secondly, based on dynamic surface control methods, controllers with or without approximating structures are established to guarantee that the tracking error achieves prescribed transient performance and converges into a prescribed bounded set within prescribed time. In particular, the settling time and initial value of the prescribed performance function are completely independent of initial conditions of the tracking error and system parameters, which improves existing results. Moreover, with a novel Lyapunov-like energy function, not only the differential explosion problem frequently occurring in backstepping techniques is solved, but the drawback of the semi-global boundedness of tracking error induced by dynamic surface control can be overcome. The validity and effectiveness of the main results are verified by numerical simulations on practical examples

Advances in the Theory of Fixed-time Stability with Applications in Constrained Control and Optimization

Driving the state of dynamical systems to a desired point or set is a problem of crucial practical importance. Various constraints are present in real-world applications due to structural and operational requirements. Spatial constraints, i.e., constraints requiring the system trajectories to evolve in some textit{safe} set, while visiting some goal set(s), are typical in safety-critical applications. Furthermore, temporal constraints, i.e., constraints pertaining to the time of convergence, appear in time-critical applications, for instance, when a task must complete within a fixed time due to an internal or an external deadline. Moreover, imperfect knowledge of the operational environment and/or system dynamics, and the presence of external disturbances render offline control policies impractical and make it essential to develop methods for online control synthesis. Thus, from the implementation point-of-view, it is desired to design fast optimization algorithms so that an optimal control input, e.g., min-norm control input, can be computed online. As compared to exponential stability, the notion of fixed-time stability is stronger, with the time of convergence being finite and is bounded for all initial conditions. This dissertation studies the theory of fixed-time stability with applications in multi-agent control design under spatiotemporal and input constraints, and in the field of continuous-time optimization. First, multi-agent control design problems under spatiotemporal constraints are studied. A vector-field-based controller is presented for distributed control of multi-agent systems for a class of agents modeled under double-integrator dynamics. A finite-time controller that utilizes the state estimates obtained from a finite-time state observer is designed to guarantee that each agent reaches its goal location within a finite time while maintaining safety with respect to other agents as well as dynamic obstacles. Next, new conditions for fixed-time stability are developed to use fixed-time stability along with input constraints. It is shown that these new conditions capture the relationship between the time of convergence, the domain of attraction, and the input constraints for fixed-time stability. Additionally, the new conditions establish the robustness of fixed-time stable systems with respect to a class of vanishing and non-vanishing additive disturbances. Utilizing these new fixed-time stability results, a control design method using convex optimization is presented for a general class of systems having nonlinear, control-affine dynamics. Control barrier and control Lyapunov function conditions are used as linear constraints in the optimization problem for set-invariance and goal-reachability requirements. Various practical issues, such as input constraints, additive disturbance, and state-estimation error, are considered. Next, new results on finite-time stability for a class of hybrid and switched systems are proposed using a multiple-Lyapunov-functions framework. The presented framework allows the system to have unstable modes. Finally, novel continuous-time optimization methods are studied with guarantees for fixed-time convergence to an optimal point. Fixed-time stable gradient flows are developed for unconstrained convex optimization problems under conditions such as strict convexity and gradient dominance of the objective function, which is a relaxation of strong convexity. Furthermore, min-max problems are considered and modifications of saddle-point dynamics are proposed with fixed-time stability guarantees under various conditions on the objective function.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168071/1/kgarg_1.pd

Cooperative Control Reconfiguration in Networked Multi-Agent Systems

Development of a network of autonomous cooperating vehicles has attracted significant attention during the past few years due to its broad range of applications in areas such as autonomous underwater vehicles for exploring deep sea oceans, satellite formations for space missions, and mobile robots in industrial sites where human involvement is impossible or restricted, to name a few. Motivated by the stringent specifications and requirements for depth, speed, position or attitude of the team and the possibility of having unexpected actuators and sensors faults in missions for these vehicles have led to the proposed research in this thesis on cooperative fault-tolerant control design of autonomous networked vehicles. First, a multi-agent system under a fixed and undirected network topology and subject to actuator faults is studied. A reconfigurable control law is proposed and the so-called distributed Hamilton-Jacobi-Bellman equations for the faulty agents are derived. Then, the reconfigured controller gains are designed by solving these equations subject to the faulty agent dynamics as well as the network structural constraints to ensure that the agents can reach a consensus even in presence of a fault while simultaneously the team performance index is minimized. Next, a multi-agent network subject to simultaneous as well as subsequent actuator faults and under directed fixed topology and subject to bounded energy disturbances is considered. An H∞ performance fault recovery control strategy is proposed that guarantees: the state consensus errors remain bounded, the output of the faulty system behaves exactly the same as that of the healthy system, and the specified H∞ performance bound is guaranteed to be minimized. Towards this end, the reconfigured control law gains are selected first by employing a geometric control approach where a set of controllers guarantees that the output of the faulty agent imitates that of the healthy agent and the consensus achievement objectives are satisfied. Then, the remaining degrees of freedom in the selection of the control law gains are used to minimize the bound on a specified H∞ performance index. Then, control reconfiguration problem in a team subject to directed switching topology networks as well as actuator faults and their severity estimation uncertainties is considered. The consensus achievement of the faulty network is transformed into two stability problems, in which one can be solved offline while the other should be solved online and by utilizing information that each agent has received from the fault detection and identification module. Using quadratic and convex hull Lyapunov functions the control gains are designed and selected such that the team consensus achievement is guaranteed while the upper bound of the team cost performance index is minimized. Finally, a team of non-identical agents subject to actuator faults is considered. A distributed output feedback control strategy is proposed which guarantees that agents outputs’ follow the outputs of the exo-system and the agents states remains stable even when agents are subject to different actuator faults

Advances in Theoretical and Computational Energy Optimization Processes

The paradigm in the design of all human activity that requires energy for its development must change from the past. We must change the processes of product manufacturing and functional services. This is necessary in order to mitigate the ecological footprint of man on the Earth, which cannot be considered as a resource with infinite capacities. To do this, every single process must be analyzed and modified, with the aim of decarbonising each production sector. This collection of articles has been assembled to provide ideas and new broad-spectrum contributions for these purposes

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

Onboard Robust Nonlinear Control for Multiple Multirotor UAVs

In this thesis, we focus on developing and validating onboard robust nonlinear control approaches for multiple multirotor unmanned aerial vehicles (UAVs), for the promise of achieving nontrivial tasks, such as path following with aggressive maneuvers, navigation in complex environments with obstacles, and formation in group. To fulfill these challenging missions, the first concern comes with the stability of flight control for the aggressive UAV maneuvers with large tilted angles. In addition, robustness of control is highly desired in order to lead the multirotor UAVs to safe and accurate performance under disturbances. Furthermore, efficiency of control algorithm is a crucial element for the onboard implementation with limited computational capability. Finally, the potential to simultaneously control a group of UAVs in a stable fashion is required. All of these concerns motivate our work in this thesis in the following aspects. We first propose the problem of robust control for the nontrivial maneuvers of a multirotor UAV under disturbances. A complete framework is developed to enable the UAV to achieve the challenging tasks, which consists of a nonlinear attitude controller based on the solution of global output regulation problems for the rigid body rotations SO(3), a backstepping-like position controller, a six-dimensional (6D) wrench observer to estimate the unknown force and torque disturbances, and an online trajectory planner based on a model predictive control (MPC) method. We prove the strong convergence properties of the proposed method both in theory and via intensive real-robot experiments of aggressive waypoint navigation and large-tilted path following tasks in the presence of external disturbances, e.g. wind gusts. Secondly, we propose the problem of autonomous navigation of a multirotor UAV in complex scenarios. We present an effective and robust control approach, namely a fast MPC method with the inclusion of nonlinear obstacle avoiding constraints, and implement it onboard the UAV at 50Hz. The developed approach enables the navigation for a multirotor UAV in 3D environments with multiple obstacles, by autonomously deciding to fly over or around the randomly located obstacles. The third problem that is addressed in our work is formation control for a group of multirotor UAVs. We solve this problem by proposing a distributed formation control algorithm for multiple UAVs based on the solution of retraction balancing problem. The algorithm brings the whole group of UAVs simultaneously to a prescribed submanifold that determines the formation shape in an asymptotically stable fashion in 2D and 3D environments. We validate our proposed algorithm via a series of hardware-in-the-loop simulations and real-robot experiments in various formation cases of arbitrary time-varying (e.g. expanding, shrinking or moving) shapes. In the actual experiments, up to 4 multirotors have been implemented to form arbitrary triangular, rectangular and circular shapes drawn by the operator via a human-robot-interaction device. We have also carried out virtual tests using up to 6 onboard computers to achieve a spherical formation and a formation moving through obstacles.In dieser Arbeit konzentrieren wir uns auf die Entwicklung und Validierung von robusten nichtlinearen On-Bord Steuerungsansatzen für mehrere unbemannte Multirotor-Luftfahrzeuge (UAVs), mit dem Ziel, nicht triviale Aufgaben zu erledigen wie z.B. Wegfolge mit aggressiven Manovern, Navigation in komplexen Umgebungen mit Hindernissen und Formationsflug in einer Gruppe. Um diese anspruchsvollen Missionen zu erfullen liegt unser Hauptaugenmerk bei der Stabilität der Flugsteuerung für aggressive UAV Manöver mit steilen Lagewinkeln. Des weiteren ist Kontroll-robustheit sehr wünschenswert, um die Multirotor-UAVs unter Beeinflussung sicher und genau zu steuern. Daruber hinaus ist die Effizienz des Kontrollalgorithmus ein wichtiges Element für die Onboard-Implementierung mit eingeschrankter Rechenfähigkeit. Abschliessend ist das Potenzial, gleichzeitig eine Gruppe von UAVs in stabiler Weise zu kontrollieren, erforderlich. All dies motiviert uns zur Arbeit an den folgenden Aspekten: Zuerst behandeln wir das Problem der robusten Steuerung nichttrivialer Manöver eines Multirotor UAV unter Störeinfluss. Ein komplettes Framework wird entwickelt, welches dem UAV ermöglicht diese anspruchsvollen Aufgaben zu bewältigen. Es beinhaltet einem nichtlinearen Lageregler, basierend auf der Lösung von globalen Ausgangsrege lungsproblemen für Starrkörperrotationen SO(3), einem backstepping basierten Positionsregler, einen sechsdimensionalen (6D) wrench observer um die unbekannten Kraftund Drehmomenteinflusse zu schätzen, sowie einem Online-Trajektorienplaner basierend auf Model Predictive Control (MPC). Wir weisen die starken Konvergenzcharakteristiken der vorgeschlagenen Methode nach, sowohl in der Theorie als auchmittels intensiver Real-roboter-Experimente, mit aggressiver Wegpunktnavigation und Wegfindungsaufgaben in extremer Fluglage in Gegenwart externer Einflüsse, z.B. Windböen. Als nächstes bearbeiten wir das Problem der autonomen Navigation eines Multirotor UAV in komplexen Szenarien. Wir stellen einen effektiven und robusten Steuerungsansatz dar, nämlich eine schnelle MPC-Methode mit der Einbeziehung von nichtlinearer Einschränkungen zur Hindernisvermeidung, und implmenetieren diese an Bord des UAV mit 50Hz. Der entwickelte Ansatz ermöglicht die Navigation eines Multirotor UAVs in 3D-Umgebungen mit mehreren Hindernissen, wobei autonom entschieden wir, über oder um die zufällig gelegenen Hindernisse zu fliegen. Das dritte Problem, das in unserer Arbeit angesprochen wird, ist die Bildungssteuerung für eine Gruppe von Multirotor UAVs. Wir lösen dieses Problem, indem wir einen verteilten Formationskontrollalgorithmus für mehrere UAVs auf der Grundlage der Lösung des Retraction Balancing Problems vorschlagen. Der Algorithmus bringt die ganze Gruppe von UAVs gleichzeitig auf eine vorgeschriebene Untermanigfaltigkeit, welche die Formation in asymtotisch stabiler Weise in 2D- und 3D-Umgebungen bestimmt. Wir validieren unseren vorgeschlagenen Algorithmus uber eine Reihe von Hardware-in-the- ¨ Loop-Simulationen und Real-Roboter-Experimente mit verschiedenen Formationsvarianten in beliebigen zeitveränderlichen (z. B. expandierenden, schrumpfenden oder bewegten) Formen. In den eigentlichen Experimenten wurden bis zu 4 Multirotoren eingesetzt, um beliebige dreieckige, rechteckige und kreisförmige Formen zu bilden, die vom Bediener über eine Mensch-Roboter-Interaktionsvorrichtung vorgezeichnet wurden. Wir haben auch virtuelle Tests mit bis zu 6 Onboard-Computern durchgeführt, um eine sphärische Formation und eine Formation zu erreichen, die sich durch Hindernisse. bewegt