Cooperative control of multi-agent systems (MASs) has been intensively investigated in the past decade. The task is always complicated for an individual agent, but can be achieved by collectively operating a group of agents in a reliable, economic and efficient way. Although a lot of efforts are being spent on improving MAS performances, much progress has yet to be developed on different aspects. This thesis aims to solve problems in the consensus control of multiple quadrotors and/or mobile robots considering irregular sampling controls, heterogeneous agent dynamics and the presence of model uncertainties and disturbances. The thesis proceeds with Chapter 1 by providing the literature review of the state-of-the-art development in the consensus control of MASs. Chapter 2 introduces experimental setups of the laboratory involving two-wheeled mobile robots (2WMRs), quadrotors, positioning systems and inter-vehicle communications. All of the developed theoretical results in Chapters 3-6 are experimentally verified on the platform. Then it is followed by two main parts: Irregular sampling consensus control methods (Chapter 3 and 4) and cooperative control of heterogeneous MASs (Chapter 5 and 6). Chapter 3 focuses on the non-uniform sampling consensus control for a group of 2WMRs, and Chapter 4 studies the event-based rendezvous control for a group of asynchronous robots with time-varying communication delays. Chapter 5 concentrates on cooperative control methods for a heterogeneous MAS consisting of quadrotors and 2WMRs. Chapter 6 focuses on the design of a quadrotor flight controller which is robust to various adverse factors such as model uncertainties and external disturbances. The developed controller is further applied to the consensus control of the heterogeneous MAS. Specifically, Chapter 3 studies synchronized and non-periodical sampling consensus control methods for a group of 2WMRs. The directed and switching communication topologies among the network are considered in the controller design. The 2WMR is an underactuated system, which implies that it can not generate independent x and y accelerations in the two-dimensional plane. The rendezvous control methods are proposed for 2WMRs. The algebraic graph theory and stochastic matrix analysis are employed to conduct the convergence analysis. Although the samplings in the work of Chapter 3 are aperiodic, one feature is that local clocks of agents are required to be synchronized. Challenges arise in the practical control of distributed MASs, especially in the scenario that the global clock is lacking. Moreover, frequent samplings can result in redundant information transmissions when the communication bandwidth is limited. To address these problems, Chapter 4 investigates an event-based rendezvous control method for a group of asynchronous MAS with time-varying communication delays. Integral-type triggering conditions for each robot are adopted to be checked periodically. If the triggering condition is satisfied at one checking instant, the agent samples and broadcasts the state to the neighbors with a bounded communication delay. Then an algorithm is provided for driving 2WMRs to asymptotically reach rendezvous. The convergence analysis is conducted through Lyapunov approaches. Most of the theoretical works on cooperative control are focused on controlling agents with identical dynamics. However, in certain realistic scenarios, some complex missions require the cooperation of different types of agent dynamics such as surveillance, search and rescue, etc. Tasks can be carried out with higher efficiency by employing both the autonomous ground vehicles and unmanned aerial vehicles. To achieve better performance for MASs, in Chapter 5, distributed cooperative control methods for a heterogeneous MAS consisting of quadrotors and 2WMRs are developed. Consensus conditions are provided, and the theoretical results are experimentally verified. Many existing quadrotor control methods need exact model parameters of the quadrotor. In reality, when a quadrotor is conducting some tasks with extra payloads or with unexpected damages to the model structure, errors in parameters could result in the failure of the flight. External disturbances also inevitably affect the flight performance. To move a step further towards practical applications, in Chapter 6, a robust quadrotor flight controller using Integral Sliding Mode Control (ISMC) technique is investigated. In experiments, an extra payload with the position and mass unknown, is attached to destroy the accuracy of the model and to add disturbances. The designed controller significantly rejects negative effects caused by the payload during the flight. This controller is also successfully applied to an MAS consisting of a quadrotor and 2WMRs. / Graduate

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A cooperative multi-agent system is a collection of interacting agents deployed in a mission space where each agent is allowed to control its local state so that the fleet of agents collectively optimizes a common global objective. While optimization problems associated with multi-agent systems intend to determine the fixed set of globally optimal agent states, control problems aim to obtain the set of globally optimal agent controls. Associated non-convexities in these problems result in multiple local optima. This dissertation explores systematic techniques that can be deployed to either escape or avoid poor local optima while in search of provably better (still local) optima. First, for multi-agent optimization problems with iterative gradient-based solutions, a distributed approach to escape local optima is proposed based on the concept of boosting functions. These functions temporarily transform gradient components at a local optimum into a set of boosted non-zero gradient components in a systematic manner so that it is more effective compared to the methods where gradient components are randomly perturbed. A novel variable step size adjustment scheme is also proposed to establish the convergence of this distributed boosting process. Developed boosting concepts are successfully applied to the class of coverage problems. Second, as a means of avoiding convergence to poor local optima in multi-agent optimization, the use of greedy algorithms in generating effective initial conditions is explored. Such greedy methods are computationally cheap and can often exploit submodularity properties of the problem to provide performance bound guarantees to the obtained solutions. For the class of submodular maximization problems, two new performance bounds are proposed and their effectiveness is illustrated using the class of coverage problems. Third, a class of multi-agent control problems termed Persistent Monitoring on Networks (PMN) is considered where a team of agents is traversing a set of nodes (targets) interconnected according to a network topology aiming to minimize a measure of overall node state. For this class of problems, a gradient-based parametric control solution developed in a prior work relies heavily on the initial selection of its `parameters' which often leads to poor local optima. To overcome this initialization challenge, the PMN system's asymptotic behavior is analyzed, and an off-line greedy algorithm is proposed to systematically generate an effective set of initial parameters. Finally, for the same class of PMN problems, a computationally efficient distributed on-line Event-Driven Receding Horizon Control (RHC) solution is proposed as an alternative. This RHC solution is parameter-free as it automatically optimizes its planning horizon length and gradient-free as it uses explicitly derived solutions for each RHC problem invoked at each agent upon each event of interest. Hence, unlike the gradient-based parametric control solutions, the proposed RHC solution does not force the agents to converge to one particular behavior that is likely to be a poor local optimum. Instead, it keeps the agents actively searching for the optimum behavior. In each of these four parts of the thesis, an interactive simulation platform is developed (and made available online) to generate extensive numerical examples that highlight the respective contributions made compared to the state of the art.

This dissertation focuses on the study of decentralized coordination algorithms of multiple autonomous vehicles. Here, the term decentralized coordination is used to refer to the behavior that a group of vehicles reaches the desired group behavior via local interaction. Research is conducted towards designing and analyzing distributed coordination algorithms to achieve desired group behavior in the presence of none, one, and multiple group reference states. Decentralized coordination in the absence of any group reference state is a very active research topic in the systems and controls society. We first focus on studying decentralized coordination problems for both single-integrator kinematics and double-integrator dynamics in a sampled-data setting because real systems are more appropriate to be modeled in a sampled-data setting rather than a continuous setting. Two sampled-data consensus algorithms are proposed and the conditions to guarantee consensus are presented for both fixed and switching network topologies. Because a number of coordination algorithms can be employed to guarantee coordination, it is important to study the optimal coordination problems. We further study the optimal consensus problems in both continuous-time and discrete-time settings via an linear-quadratic regulator (LQR)-based approach. Noting that fractional-order dynamics can better represent the dynamics of certain systems, especially when the systems evolve under complicated environment, the existing integer-order coordination algorithms are extended to the fractional-order case. Decentralized coordination in the presence of one group reference state is also called coordinated tracking, including both consensus tracking and swarm tracking. Consensus tracking refers to the behavior that the followers track the group reference state. Swarm tracking refers to the behavior that the followers move cohesively with the external leader while avoiding inter-vehicle collisions. In this part, consensus tracking is studied in both discrete-time setting and continuous-time settings while swarm tracking is studied in a continuous-time setting. Decentralized coordination in the presence of multiple group reference states is also called containment control, where the followers will converge to the convex hull, i.e., the minimal geometric space, formed by the group references states via local interaction. In this part, the containment control problem is studied for both single-integrator kinematics and double-integrator dynamics. In addition, experimental results are provided to validate some theoretical results.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第19124号 / 情博第570号 / 新制||情||100(附属図書館) / 32075 / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 杉江 俊治, 教授 太田 快人, 教授 大塚 敏之, 准教授 東 俊一 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

The development of connected vehicle (CV) technologies enables advanced management of individual vehicles and traffic signals to improve urban mobility and energy efficiency. In this thesis, a cooperative vehicle-signal control system will be developed to integrate an Eco-driving system and a proactive signal control system under a mixed connected environment with both connected vehicles (CVs) and human-driven vehicles (HDVs). The system utilizes CVs to conduct an accurate prediction of queue length and delay at different approaches of intersections. Then, a queue-based optimal control strategy is established to minimize the fuel usage of individual CVs and the travel time delay of entire intersections. The system applies the model predictive control to search for the optimal signal timing plan for each intersection and the most-fuel efficient speed profiles for each CV to gain the global optimum of the intersection. In this thesis, a simulation platform is designed to verify the effectiveness of the proposed system under different traffic scenarios. The comparison with the eco-driving only and signal control only algorithms verifies that the cooperative system has a much more extensive reduction range of the trip delay in the case of medium and high saturation. At low saturation, the effect of the system is not much different from that of the eco-driving algorithm, but it is still better than the signal control. Results show that the benefits of CVs are significant at all different market penetration rates of CVs. It also demonstrates the drawback of the system at high congestion levels. / Thesis / Master of Applied Science (MASc)

Recent rapid advances in computing, communication, sensing, and actuation, together with miniaturization technologies, have offered unprecedented opportunities to employ large numbers of autonomous vehicles (air, ground, and water) working cooperatively to accomplish a mission. Cooperative control of such multi-agent dynamical systems has potential impact on numerous civilian, homeland security, and military applications. Compared with single-agent control problems, new theoretical and practical challenges emerge and need to be addressed in cooperative control of multiagent dynamical systems, including but not limited to problem size, task coupling, limited computational resources at individual agent level, communication constraints, and the need for real-time obstacle/collision avoidance. In order to address these challenges, a unified optimal multi-agent cooperative control strategy is proposed to formulate the multi-objective cooperative control problem into one unified optimal control framework. Many cooperative behaviors, such as consensus, cooperative tracking, formation, obstacle/collision avoidance, or flocking with cohesion and repulsion, can be treated in one optimization process. An innovative inverse optimal control approach is utilized to include these cooperative objectives in derived cost functions such that a closedorm cooperative control law can be obtained. In addition, the control law is distributed and only depends on the local neighboring agents’ information. Therefore, this new method does not demand intensive computational load and is easy for real-time onboard implementation. Furthermore, it is very scalable to large multi-agent cooperative dynamical systems. The closed-loop asymptotic stability and optimality are theoretically proved. Simulations based on MATLAB are conducted to validate the cooperative behaviors including consensus, Rendezvous, formation flying, and flocking, as well as the obstacle avoidance performance.

Smart grid is more than just the smart meters. The future smart grids are expected to include a high penetration of distributed generations (DGs), most of which will consist of renewable energy sources, such as solar or wind energy. It is believed that the high penetration of DGs will result in the reduction of power losses, voltage profile improvement, meeting future load demand, and optimizing the use of non-conventional energy sources. However, more serious problems will arise if a decent control mechanism is not exploited. An improperly managed high PV penetration may cause voltage profile disturbance, conflict with conventional network protection devices, interfere with transformer tap changers, and as a result, cause network instability. Indeed, it is feasible to organize DGs in a microgrid structure which will be connected to the main grid through a point of common coupling (PCC). Microgrids are natural innovation zones for the smart grid because of their scalability and flexibility. A proper organization and control of the interaction between the microgrid and the smartgrid is a challenge. Cooperative control makes it possible to organize different agents in a networked system to act as a group and realize the designated objectives. Cooperative control has been already applied to the autonomous vehicles and this work investigates its application in controlling the DGs in a micro grid. The microgrid power objectives are set by a higher level control and the application of the cooperative control makes it possible for the DGs to utilize a low bandwidth communication network and realize the objectives. Initially, the basics of the application of the DGs cooperative control are formulated. This includes organizing all the DGs of a microgrid to satisfy an active and a reactive power objective. Then, the cooperative control is further developed by the introduction of clustering DGs into several groups to satisfy multiple power objectives. Then, the cooperative distribution optimization is introduced iii to optimally dispatch the reactive power of the DGs to realize a unified microgrid voltage profile and minimize the losses. This distributed optimization is a gradient based technique and it is shown that when the communication is down, it reduces to a form of droop. However, this gradient based droop exhibits a superior performance in the transient response, by eliminating the overshoots caused by the conventional droop. Meanwhile, the interaction between each microgrid and the main grid can be formulated as a Stackelberg game. The main grid as the leader, by offering proper energy price to the micro grid, minimizes its cost and secures the power. This not only optimizes the economical interests of both sides, the microgrids and the main grid, but also yields an improved power flow and shaves the peak power. As such, a smartgrid may treat microgrids as individually dispatchable loads or generators.

With the recent trend for systems to be more and more autonomous, there is a growing need for cooperative trajectory planning. Applications that can be considered as cooperative systems such as surveying, formation flight, and traffic control need a method that can rapidly produce trajectories while considering all of the constraints on the system. Currently most of the existing methods to handle cooperative control are based around either simple dynamics and/or on the assumption that all vehicles have homogeneous properties. In reality, typical autonomous systems will have heterogeneous, nonlinear dynamics while also being subject to extreme constraints on certain state and control variables. In this thesis, a new approach to the cooperative control problem is presented based on the bio-inspired motion strategy known as local pursuit. In this framework, decision making about the group trajectory and formation are handled at a cooperative level while individual trajectory planning is considered in a local sense. An example is presented for a case of an autonomous farming system (e.g. scouting) utilizing nonlinear vehicles to cooperatively accomplish various farming task with minimal energy consumption or minimum time. The decision making and trajectory generation is handled very quickly while being able to consider changing environments laden with obstacles

Many real-world electrical and mechanical systems have velocity-dependent constraints in their dynamic models. For example, car-like robots, unmanned aerial vehicles, autonomous underwater vehicles and hopping robots, etc. Most of these systems can be transformed into a chained form, which is considered as a canonical form of these nonholonomic systems. Hence, study of chained systems ensure their wide applicability. This thesis studied the problem of continuous feed-back control of the chained systems while pursuing inverse optimality and exponential convergence rates, as well as the feed-back stabilization problem under input saturation constraints. These studies are based on global singularity-free state transformations and controls are synthesized from resulting linear systems. Then, the application of optimal motion planning and dynamic tracking control of nonholonomic autonomous underwater vehicles is considered. The obtained trajectories satisfy the boundary conditions and the vehicles' kinematic model, hence it is smooth and feasible. A collision avoidance criteria is set up to handle the dynamic environments. The resulting controls are in closed forms and suitable for real-time implementations. Further, dynamic tracking controls are developed through the Lyapunov second method and back-stepping technique based on a NPS AUV II model. In what follows, the application of cooperative surveillance and formation control of a group of nonholonomic robots is investigated. A designing scheme is proposed to achieves a rigid formation along a circular trajectory or any arbitrary trajectories. The controllers are decentralized and are able to avoid internal and external collisions. Computer simulations are provided to verify the effectiveness of these designs.