Resilient Distributed Energy Management for Systems of Interconnected Microgrids

In this paper, distributed energy management of interconnected microgrids, which is stated as a dynamic economic dispatch problem, is studied. Since the distributed approach requires cooperation of all local controllers, when some of them do not comply with the distributed algorithm that is applied to the system, the performance of the system might be compromised. Specifically, it is considered that adversarial agents (microgrids with their controllers) might implement control inputs that are different than the ones obtained from the distributed algorithm. By performing such behavior, these agents might have better performance at the expense of deteriorating the performance of the regular agents. This paper proposes a methodology to deal with this type of adversarial agents such that we can still guarantee that the regular agents can still obtain feasible, though suboptimal, control inputs in the presence of adversarial behaviors. The methodology consists of two steps: (i) the robustification of the underlying optimization problem and (ii) the identification of adversarial agents, which uses hypothesis testing with Bayesian inference and requires to solve a local mixed-integer optimization problem. Furthermore, the proposed methodology also prevents the regular agents to be affected by the adversaries once the adversarial agents are identified. In addition, we also provide a sub-optimality certificate of the proposed methodology.Comment: 8 pages, Conference on Decision and Control (CDC) 201

Formation control of mobile robots and unmanned aerial vehicles

In this dissertation, the nonlinear control of nonholonomic mobile robot formations and unmanned aerial vehicle (UAV) formations is undertaken and presented in six papers. In the first paper, an asymptotically stable combined kinematic/torque control law is developed for leader-follower based formation control of mobile robots using backstepping. A neural network (NN) is introduced along with robust integral of the sign of the error (RISE) feedback to approximate the dynamics of the follower as well as its leader using online weight tuning. Subsequently, in the second paper, a novel NN observer is designed to estimate the linear and angular velocities of both the follower and its leader robot and a NN output feedback control law is developed. On the other hand, in the third paper, a NN-based output feedback control law is presented for the control of an underactuated quad rotor UAV, and a NN virtual control input scheme is proposed which allows all six degrees of freedom to be controlled using only four control inputs. The results of this paper are extended to include the control of quadrotor UAV formations, and a novel three-dimensional leader-follower framework is proposed in the fourth paper. Next, in the fifth paper, the discrete-time nonlinear optimal control is undertaken using two online approximators (OLA\u27s) to solve the infinite horizon Hamilton-Jacobi-Bellman (HJB) equation forward-in-time to achieve nearly optimal regulation and tracking control. In contrast, paper six utilizes a single OLA to solve the infinite horizon HJB and Hamilton-Jacobi-Isaacs (HJI) equations forward-intime for the near optimal regulation and tracking control of continuous affine nonlinear systems. The effectiveness of the optimal tracking controllers proposed in the fifth and sixth papers are then demonstrated using nonholonomic mobile robot formation control --Abstract, page iv

Event-triggered near optimal adaptive control of interconnected systems

Increased interest in complex interconnected systems like smart-grid, cyber manufacturing have attracted researchers to develop optimal adaptive control schemes to elicit a desired performance when the complex system dynamics are uncertain. In this dissertation, motivated by the fact that aperiodic event sampling saves network resources while ensuring system stability, a suite of novel event-sampled distributed near-optimal adaptive control schemes are introduced for uncertain linear and affine nonlinear interconnected systems in a forward-in-time and online manner. First, a novel stochastic hybrid Q-learning scheme is proposed to generate optimal adaptive control law and to accelerate the learning process in the presence of random delays and packet losses resulting from the communication network for an uncertain linear interconnected system. Subsequently, a novel online reinforcement learning (RL) approach is proposed to solve the Hamilton-Jacobi-Bellman (HJB) equation by using neural networks (NNs) for generating distributed optimal control of nonlinear interconnected systems using state and output feedback. To relax the state vector measurements, distributed observers are introduced. Next, using RL, an improved NN learning rule is derived to solve the HJB equation for uncertain nonlinear interconnected systems with event-triggered feedback. Distributed NN identifiers are introduced both for approximating the uncertain nonlinear dynamics and to serve as a model for online exploration. Next, the control policy and the event-sampling errors are considered as non-cooperative players and a min-max optimization problem is formulated for linear and affine nonlinear systems by using zero-sum game approach for simultaneous optimization of both the control policy and the event based sampling instants. The net result is the development of optimal adaptive event-triggered control of uncertain dynamic systems --Abstract, page iv

Decentralized adaptive neural network control of interconnected nonlinear dynamical systems with application to power system

Traditional nonlinear techniques cannot be directly applicable to control large scale interconnected nonlinear dynamic systems due their sheer size and unavailability of system dynamics. Therefore, in this dissertation, the decentralized adaptive neural network (NN) control of a class of nonlinear interconnected dynamic systems is introduced and its application to power systems is presented in the form of six papers. In the first paper, a new nonlinear dynamical representation in the form of a large scale interconnected system for a power network free of algebraic equations with multiple UPFCs as nonlinear controllers is presented. Then, oscillation damping for UPFCs using adaptive NN control is discussed by assuming that the system dynamics are known. Subsequently, the dynamic surface control (DSC) framework is proposed in continuous-time not only to overcome the need for the subsystem dynamics and interconnection terms, but also to relax the explosion of complexity problem normally observed in traditional backstepping. The application of DSC-based decentralized control of power system with excitation control is shown in the third paper. On the other hand, a novel adaptive NN-based decentralized controller for a class of interconnected discrete-time systems with unknown subsystem and interconnection dynamics is introduced since discrete-time is preferred for implementation. The application of the decentralized controller is shown on a power network. Next, a near optimal decentralized discrete-time controller is introduced in the fifth paper for such systems in affine form whereas the sixth paper proposes a method for obtaining the L2-gain near optimal control while keeping a tradeoff between accuracy and computational complexity. Lyapunov theory is employed to assess the stability of the controllers --Abstract, page iv

Game-Theoretic Safety Assurance for Human-Centered Robotic Systems

In order for autonomous systems like robots, drones, and self-driving cars to be reliably introduced into our society, they must have the ability to actively account for safety during their operation. While safety analysis has traditionally been conducted offline for controlled environments like cages on factory floors, the much higher complexity of open, human-populated spaces like our homes, cities, and roads makes it unviable to rely on common design-time assumptions, since these may be violated once the system is deployed. Instead, the next generation of robotic technologies will need to reason about safety online, constructing high-confidence assurances informed by ongoing observations of the environment and other agents, in spite of models of them being necessarily fallible.This dissertation aims to lay down the necessary foundations to enable autonomous systems to ensure their own safety in complex, changing, and uncertain environments, by explicitly reasoning about the gap between their models and the real world. It first introduces a suite of novel robust optimal control formulations and algorithmic tools that permit tractable safety analysis in time-varying, multi-agent systems, as well as safe real-time robotic navigation in partially unknown environments; these approaches are demonstrated on large-scale unmanned air traffic simulation and physical quadrotor platforms. After this, it draws on Bayesian machine learning methods to translate model-based guarantees into high-confidence assurances, monitoring the reliability of predictive models in light of changing evidence about the physical system and surrounding agents. This principle is first applied to a general safety framework allowing the use of learning-based control (e.g. reinforcement learning) for safety-critical robotic systems such as drones, and then combined with insights from cognitive science and dynamic game theory to enable safe human-centered navigation and interaction; these techniques are showcased on physical quadrotors—flying in unmodeled wind and among human pedestrians—and simulated highway driving. The dissertation ends with a discussion of challenges and opportunities ahead, including the bridging of safety analysis and reinforcement learning and the need to ``close the loop'' around learning and adaptation in order to deploy increasingly advanced autonomous systems with confidence

구조로봇을 위한 강건한 계층적 동작 계획 및 제어

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 박종우.Over the last several years, robotics has experienced a striking development, and a new generation of robots has emerged that shows great promise in being able to accomplish complex tasks associated with human behavior. Nowadays the objectives of the robots are no longer restricted to the automaton in the industrial process but are changing into explorers for hazardous, harsh, uncooperative, and extreme environments. As these robots usually operate in dynamic and unstructured environments, they should be robust, adaptive, and reactive under various changing operation conditions. We propose online hierarchical optimization-based planning and control methodologies for a rescue robot to execute a given mission in such a highly unstructured environment. A large number of degrees of freedom is provided to robots in order to achieve diverse kinematic and dynamic tasks. However, accomplishing such multiple objectives renders on-line reactive motion planning and control problems more difficult to solve due to the incompatible tasks. To address this problem, we exploit a hierarchical structure to precisely resolve conflicts by creating a priority in which every task is achieved as much as possible according to the levels. In particular, we concentrate on the reasoning about the task regularization to ensure the convergence and robustness of a solution in the face of singularity. As robotic systems with real-time motion planners or controllers often execute unrehearsed missions, a desired task cannot always be driven to a singularity free configuration. We develop a generic solver for regularized hierarchical quadratic programming without resorting to any off-the-shelf QP solver to take advantage of the null-space projections for computational efficiency. Therefore, the underlying principles are thoroughly investigated. The robust optimal solution is obtained under both equality and inequality tasks or constraints while addressing all problems resulting from the regularization. Especially as a singular value decomposition centric approach is leveraged, all hierarchical solutions and Lagrange multipliers for properly handling the inequality constraints are analytically acquired in a recursive procedure. The proposed algorithm works fast enough to be used as a practical means of real-time control system, so that it can be used for online motion planning, motion control, and interaction force control in a single hierarchical optimization. Core system design concepts of the rescue robot are presented. The goals of the robot are to safely extract a patient and to dispose a dangerous object instead of humans. The upper body is designed humanoid in form with replaceable modularized dual arms. The lower body is featured with a hybrid tracked and legged mobile platform to simultaneously acquire versatile manipulability and all-terrain mobility. Thus, the robot can successfully execute a driving task, dangerous object manipulation, and casualty extraction missions by changing the pose and modularized equipments in an optimized manner. Throughout the dissertation, all proposed methods are validated through extensive numerical simulations and experimental tests. We highlight precisely how the rescue robot can execute a casualty extraction and a dangerous object disposal mission both in indoor and outdoor environments that none of the existing robots has performed.최근에 등장한 새로운 세대의 로봇은 기존에는 인간만이 할 수 있었던 복잡한 일을 로봇 또한 수행할 수 있음을 보여주었다. 특히 DARPA Robotics Challenge를 통해 이러한 사실을 잘 확인할 수 있으며, 이 로봇들은 공장과 같은 정형화된 환경에서 자동화된 일을 반복적으로 수행하던 임무에서 더 나아가 극한의 환경에서 인간을 대신하여 위험한 임무를 수행할 수 있는 방향으로 발전하고 있다. 그래서 사람들은 재난환경에서 안전하고 시의 적절하게 대응할 수 있는 여러 가지 대안 중에서 실현 가능성이 높은 대처 방안으로 로봇을 생각하게 되었다. 하지만 이러한 로봇은 동적으로 변화하는 비정형 환경에서 임무를 수행할 수 있어야 하기 때문에 불확실성에 대해 강건해야하고, 다양한 환경 조건에서 능동적으로 반응을 할 수 있어야 한다. 본 학위논문에서는 로봇이 비정형 환경에서 강건하면서도 적응적으로 동작할 수 있는 실시간 최적화 기반의 동작 계획 및 제어 방법과 구조 로봇의 설계 개념을 제안하고자 한다. 인간은 많은 자유도를 가지고 있으며, 하나의 전신 동작을 생성할 때 다양한 기구학 혹은 동역학적 특성을 가지는 세부 동작 혹은 작업을 정의하고, 이를 효과적으로 종합할 수 있다. 그리고 학습을 통해 각 동작 요소들을 최적화할 뿐만 아니라 상황 에 따라 각 동작 요소에 우선순위를 부여하여 이를 효과적으로 결합하거나 분리하여 실시간으로 최적의 동작을 생성하고 제어한다. 즉, 상황에 따라 중요한 동작요소를 우선적으로 수행하고 우선순위가 낮은 동작요소는 부분 혹은 전체적으로 포기하기도 하면서 매우 유연하게 전체 동작을 생성하고 최적화 한다. 인간과 같이 다자유도를 보유한 로봇 또한 기구학과 동역학적 특성을 가지는 다양한 세부 동작 혹은 작업을 작업공간(task space) 혹은 관절공간(configuration space)에서 정의할 수 있으며, 우선순위에 따라 이를 효과적으로 결합하여 전체 동작을 생 성하고 제어할 수 있다. 서로 양립하기 어려운 로봇의 동작 문제를 해결하기 위해 동작들 사이에 우선순위를 부여하여 계층을 생성하고, 이에 따라 로봇의 전신 동작을 구현하는 방법은 오랫동안 연구가 진행되어 왔다. 이러한 계층적 최적화를 이용하면 우선순위가 높은 동작부터 순차적으로 실행하지만, 우선순위가 낮은 동작요소들도 가능한 만족시키는 최적의 해를 찾을 수 있다. 하지만 관절의 구동 범위와 같은 부등식의 조건이 포함된 계층적 최적화 문제에서 특이점에 대한 강건성까지 확보할 수 있는 방법에 대해서는 아직까지 많은 부분이 밝 혀진 바가 없다. 따라서 본 학위논문에서는 등식과 부등식으로 표현되는 구속조건 혹은 동작요소를 계층적 최적화에 동시에 포함시키고, 특이점이 존재하더라도 강건성과 수렴성을 보장하는 관절공간에서의 최적해를 확보하는데 집중한다. 왜나하면 비정형 임무를 수행하는 로봇은 사전에 계획된 동작을 수행하는 것이 아닌 변화하는 환경조건에 따라 실시간으로 동작을 계획하고 제어해야 하기 때문에 특이점이 없는 자세로 로봇을 항상 제어하기가 어렵다. 그리고 이렇게 특이점을 회피하는 방향으로 로봇을 제어하는 것은 로봇의 운용성을 심각하게 저해시킬 수 있다. 특이점 근방에서의 해의 강건성이 보장되지 않으면 로봇 관절에 과도한 속도 혹은 토크가 발생하여 로봇의 임무 수행이 불가능하거나 환경과 로봇의 손상을 초래할 수 있으며, 나아가 로봇과 함께 임무를 수행하는 사람에게 상해를 가할 수도 있다. 특이점에 대한 강건성을 확보하기 위해 우선순위 기반의 계층적 최적화와 정규화 (regularization)를 통합하여 정규화된 계층적 최적화 (RHQP: Regularized Hierarchical Quadratic Program) 문제를 다룬다. 부등식이 포함된 계층적 최적화에 정규화를 동시에 고려함으로써 야기되는 많은 문제점들을 해결하고 해의 최적성과 강건성을 확보할 수 있는 방법을 제안한다. 특히 외부의 최적화 프로그램을 사용하지 않고 수치적 최적화 (numerical optimization) 이론과 우선순위에 기반을 두는 여유자유도 로봇의 해석 기법을 이용하여 계산의 효율성을 극대화할 수 있는 이차 프로그램(quadratic programming)을 제안한다. 또한 이와 동시에 정규화된 계층적 최적화 문제의 이론적 구조를 철저하게 분석한다. 특히 특이값 분해 (singular value decomposition)를 통해 최적해와 부등식 조건을 처리하는데 필요한 라그랑지 승수를 재귀적인 방법으로 해석적 형태로 구함으로써 계산의 효율성을 증대시키고 동시에 부등식의 조건을 오류 없이 정확하게 처리할 수 있도록 하였다. 그리고 정규화된 계층적 최적화를 힘제어까지 확장하여 환경과 로봇의 안전한 상호작용을 보장하여 로봇이 적절한 힘으로 환경과 접촉할 수 있도록 하였다. 불확실성이 존재하는 비정형 환경에서 비정형 임무를 수행할 수 있는 구조로봇의 핵심 설계 개념을 제시한다. 비정형 환경에서의 조작 성능과 이동 성능을 동시에 확보할 수 있는 형상으로 로봇을 설계하여 구조 로봇으로 하여금 최종 목적으로 설정된 인간을 대신하여 부상자를 구조하고 위험물을 처리하는 임무를 효과적으로 수행할 수 있도록 한다. 구조 로봇에 필요한 매니퓰레이터는 부상자 구조 임무와 위험물 처리 임무에 따라 교체 가능한 모듈형으로 설계하여 각각의 임무에 따라 최적화된 매니퓰 레이터를 장착하여 임무를 수행할 수 있다. 하체는 트랙과 관절이 결합된 하이브리드 형태를 취하고 있으며, 주행 임무와 조작임무에 따라 형상을 변경할 수 있다. 형상 변경과 모듈화된 매니퓰레이터를 통해서조작 성능과 험한 지형에서 이동할 수 있는 주행 성능을 동시에 확보하였다. 최종적으로 구조로봇의 설계와 실시간 계층적 제어를 이용하여 비정형 실내외 환경에서 구조로봇이 주행임무, 위험물 조작임무, 부상자 구조 임무를 성공적으로 수 행할 수 있음을 해석과 실험을 통하여 입증함으로써 본 학위논문에서 제안한 설계와 정규화된 계층적 최적화 기반의 제어 전략의 유용성을 검증하였다.1 Introduction 1 1.1 Motivations 1 1.2 Related Works and Research Problems for Hierarchical Control 3 1.2.1 Classical Approaches 3 1.2.2 State-of-the-Art Strategies 4 1.2.3 Research Problems 7 1.3 Robust Rescue Robots 9 1.4 Research Goals 12 1.5 Contributions of ThisThesis 13 1.5.1 Robust Hierarchical Task-Priority Control 13 1.5.2 Design Concepts of Robust Rescue Robot 16 1.5.3 Hierarchical Motion and ForceControl 17 1.6 Dissertation Preview 18 2 Preliminaries for Task-Priority Control Framework 21 2.1 Introduction 21 2.2 Task-Priority Inverse Kinematics 23 2.3 Recursive Formulation of Null Space Projector 28 2.4 Conclusion 31 3 Robust Hierarchical Task-Priority Control 33 3.1 Introduction 33 3.1.1 Motivations 35 3.1.2 Objectives 36 3.2 Task Function Approach 37 3.3 Regularized Hierarchical Optimization with Equality Tasks 41 3.3.1 Regularized Hierarchical Optimization 41 3.3.2 Optimal Solution 45 3.3.3 Task Error and Hierarchical Matrix Decomposition 49 3.3.4 Illustrative Examples for Regularized Hierarchical Optimization 56 3.4 Regularized Hierarchical Optimization with Inequality Constraints 60 3.4.1 Lagrange Multipliers 61 3.4.2 Modified Active Set Method 66 3.4.3 Illustrative Examples of Modified Active Set Method 70 3.4.4 Examples for Hierarchical Optimization with Inequality Constraint 72 3.5 DLS-HQP Algorithm 79 3.6 Concluding Remarks 80 4 Rescue Robot Design and Experimental Results 83 4.1 Introduction 83 4.2 Rescue Robot Design 85 4.2.1 System Design 86 4.2.2 Variable Configuration Mobile Platform 92 4.2.3 Dual Arm Manipulators 95 4.2.4 Software Architecture 97 4.3 Performance Verification for Hierarchical Motion Control 99 4.3.1 Real-Time Motion Generation 99 4.3.2 Task Specifications 103 4.3.3 Singularity Robust Task Priority 106 4.3.4 Inequality Constraint Handling and Computation Time 111 4.4 Singularity Robustness and Inequality Handling for Rescue Mission 117 4.5 Field Tests 122 4.6 Concluding Remarks 126 5 Hierarchical Motion and Force Control 129 5.1 Introduction 129 5.2 Operational Space Control 132 5.3 Acceleration-Based Hierarchical Motion Control 134 5.4 Force Control 137 5.4.1 Force Control with Inner Position Loop 141 5.4.2 Force Control with Inner Velocity Loop 144 5.5 Motion and Force Control 145 5.6 Numerical Results for Acceleration-Based Motion and Force Control 148 5.6.1 Task Specifications 150 5.6.2 Force Control Performance 151 5.6.3 Singularity Robustness and Inequality Constraint Handling 155 5.7 Velocity Resolved Motion and Force Control 160 5.7.1 Velocity-Based Motion and Force Control 161 5.7.2 Experimental Results 163 5.8 Concluding Remarks 167 6 Conclusion 169 6.1 Summary 169 6.2 Concluding Remarks 173 A Appendix 175 A.1 Introduction to PID Control 175 A.2 Inverse Optimal Control 176 A.3 Experimental Results and Conclusion 181 Bibliography 183 Abstract 207박

Nonlinear Model Predictive Control for Motion Generation of Humanoids

Das Ziel dieser Arbeit ist die Untersuchung und Entwicklung numerischer Methoden zur Bewegungserzeugung von humanoiden Robotern basierend auf nichtlinearer modell-prädiktiver Regelung. Ausgehend von der Modellierung der Humanoiden als komplexe Mehrkörpermodelle, die sowohl durch unilaterale Kontaktbedingungen beschränkt als auch durch die Formulierung unteraktuiert sind, wird die Bewegungserzeugung als Optimalsteuerungsproblem formuliert. In dieser Arbeit werden numerische Erweiterungen basierend auf den Prinzipien der Automatischen Differentiation für rekursive Algorithmen, die eine effiziente Auswertung der dynamischen Größen der oben genannten Mehrkörperformulierung erlauben, hergeleitet, sodass sowohl die nominellen Größen als auch deren ersten Ableitungen effizient ausgewertet werden können. Basierend auf diesen Ideen werden Erweiterungen für die Auswertung der Kontaktdynamik und der Berechnung des Kontaktimpulses vorgeschlagen. Die Echtzeitfähigkeit der Berechnung von Regelantworten hängt stark von der Komplexität der für die Bewegungerzeugung gewählten Mehrkörperformulierung und der zur Verfügung stehenden Rechenleistung ab. Um einen optimalen Trade-Off zu ermöglichen, untersucht diese Arbeit einerseits die mögliche Reduktion der Mehrkörperdynamik und andererseits werden maßgeschneiderte numerische Methoden entwickelt, um die Echtzeitfähigkeit der Regelung zu realisieren. Im Rahmen dieser Arbeit werden hierfür zwei reduzierte Modelle hergeleitet: eine nichtlineare Erweiterung des linearen inversen Pendelmodells sowie eine reduzierte Modellvariante basierend auf der centroidalen Mehrkörperdynamik. Ferner wird ein Regelaufbau zur GanzkörperBewegungserzeugung vorgestellt, deren Hauptbestandteil jeweils aus einem speziell diskretisierten Problem der nichtlinearen modell-prädiktiven Regelung sowie einer maßgeschneiderter Optimierungsmethode besteht. Die Echtzeitfähigkeit des Ansatzes wird durch Experimente mit den Robotern HRP-2 und HeiCub verifiziert. Diese Arbeit schlägt eine Methode der nichtlinear modell-prädiktiven Regelung vor, die trotz der Komplexität der vollen Mehrkörperformulierung eine Berechnung der Regelungsantwort in Echtzeit ermöglicht. Dies wird durch die geschickte Kombination von linearer und nichtlinearer modell-prädiktiver Regelung auf der aktuellen beziehungsweise der letzten Linearisierung des Problems in einer parallelen Regelstrategie realisiert. Experimente mit dem humanoiden Roboter Leo zeigen, dass, im Vergleich zur nominellen Strategie, erst durch den Einsatz dieser Methode eine Bewegungserzeugung auf dem Roboter möglich ist. Neben Methoden der modell-basierten Optimalsteuerung werden auch modell-freie Methoden des verstärkenden Lernens (Reinforcement Learning) für die Bewegungserzeugung untersucht, mit dem Fokus auf den schwierig zu modellierenden Modellunsicherheiten der Roboter. Im Rahmen dieser Arbeit werden eine allgemeine vergleichende Studie sowie Leistungskennzahlen entwickelt, die es erlauben, modell-basierte und -freie Methoden quantitativ bezüglich ihres Lösungsverhaltens zu vergleichen. Die Anwendung der Studie auf ein akademisches Beispiel zeigt Unterschiede und Kompromisse sowie Break-Even-Punkte zwischen den Problemformulierungen. Diese Arbeit schlägt basierend auf dieser Grundlage zwei mögliche Kombinationen vor, deren Eigenschaften bewiesen und in Simulation untersucht werden. Außerdem wird die besser abschneidende Variante auf dem humanoiden Roboter Leo implementiert und mit einem nominellen modell-basierten Regler verglichen

CONTROLLER SYNTHESIS AND FORMAL BEHAVIOR INFERENCE IN AUTONOMOUS SYSTEMS

Autonomous systems are widely used in crucial applications such as surveillance,defense, reghting, and search & rescue operations. Many of these application require systems to satisfy user-dened requirements describing the desired system behavior. Given high-level requirements, we are interested in the design of controllers that guarantee the compliance of these requirements by the system. However, ensuring that these systems satisfy a given set of requirements is challenging for many reasons, one of which is the large computational cost incurred by having to account for all possible system behaviors and environment conditions. These computational diculties are exacerbated when systems are required to satisfy requirements involving large numbers of tasks emerging from dynamic environments. In addition to computational diculties, scalability issues also arise when dealing with multi-agent applications, in which agents require coordination and communication to satisfy mission requirements. This dissertation is an eort towards addressing the computational and scalability challenges of designing controllers from highlevel requirements by employing reactive synthesis, a formal methods approach, and combining it with other decision-making processes that handle coordination among agents to alleviate the load on reactive synthesis. The proposed framework results in a more scalable solution with lower computational costs while guaranteeing that high-level requirements are met. The practicality of the proposed framework is demonstrated through various types of multi-agent applications including reghting, re monitoring, rescue, search & rescue and ship protection scenarios. Our approach incorporates methodology from computer science and control, including reactive synthesis of discrete systems, metareasoning, reachability analysis and inverse reinforcement learning. This thesis consists of two key parts: reactive synthesis from linear temporal logic specications and specication inference from demonstrations of formal behavior. First, we introduce the reactive synthesis problem for which the desired system behavior species the method by which a multi-agent system solves the problem of decentralized task allocation depending on communication availability conditions. Second, we present the synthesis problem formulated to obtain a high-level mission planner and controller for managing a team of agents ghting a wildre. Third, we present a framework for inferring linear temporal logic specications that succinctly convey and explain the observed behavior. The gained knowledge is leveraged to improve motion prediction for agents behaving according to the learned specication. The eectiveness of the inference process and motion prediction framework are demonstrated through a scenario in which humans practice social norms commonly seen in pedestrian settings