Showing papers in "IEEE Control Systems Magazine in 2016"
TL;DR: Fossen as discussed by the authors provides a comprehensive manuscript encompassing two separable texts on hydrodynamics and control of marine vehicles, including a detailed treatment of the subject matter, written from a more generalist perspective.
Abstract: This book offers a comprehensive manuscript encompassing two separable texts on hydrodynamics and control of marine vehicles. In contrast to the aerospace industry, where dynamicists are often control experts as well, in the maritime world (due to the significant effect environmental loads have on the dynamics of the system) the more common pairing is dynamics with hydrodynamics. As such, Fossen is one of few in the maritime industry who could write such a complete treatment of dynamics, hydrodynamics, and control issues related to surface and subsurface marine vehicles. The author provides an excellent treatment of the subject matter, written from a more generalist perspective, at a substantially lower cost. There are excellent texts covering marine hydrodynamics, vessel dynamics, and ship motion control. However, for some time a void has existed in the literature covering the intersection of these related fields, with the exception of specialist texts such as the treatment of high-speed craft control. Contents This book is presented as two volumes. The first volume is devoted to marine craft hydrodynamics and the second volume of the book is devoted to motion control.
1,018 citations
TL;DR: In this article, a model predictive control (MPC) approach is proposed to solve an open-loop constrained optimal control problem (OCP) repeatedly in a receding-horizon manner, where the OCP is solved over a finite sequence of control actions at every sampling time instant that the current state of the system is measured.
Abstract: Model predictive control (MPC) has demonstrated exceptional success for the high-performance control of complex systems [1], [2]. The conceptual simplicity of MPC as well as its ability to effectively cope with the complex dynamics of systems with multiple inputs and outputs, input and state/output constraints, and conflicting control objectives have made it an attractive multivariable constrained control approach [1]. MPC (a.k.a. receding-horizon control) solves an open-loop constrained optimal control problem (OCP) repeatedly in a receding-horizon manner [3]. The OCP is solved over a finite sequence of control actions {u0,u1,f,uN- 1} at every sampling time instant that the current state of the system is measured. The first element of the sequence of optimal control actions is applied to the system, and the computations are then repeated at the next sampling time. Thus, MPC replaces a feedback control law p(m), which can have formidable offline computation, with the repeated solution of an open-loop OCP [2]. In fact, repeated solution of the OCP confers an "implicit" feedback action to MPC to cope with system uncertainties and disturbances. Alternatively, explicit MPC approaches circumvent the need to solve an OCP online by deriving relationships for the optimal control actions in terms of an "explicit" function of the state and reference vectors. However, explicit MPC is not typically intended to replace standard MPC but, rather, to extend its area of application [4]-[6].
657 citations
TL;DR: A review of the development, analysis, and control of epidemic models can be found in this paper, where the authors present various solved and open problems in the development and analysis of epidemiological models.
Abstract: This article reviews and presents various solved and open problems in the development, analysis, and control of epidemic models. The proper modeling and analysis of spreading processes has been a long-standing area of research among many different fields, including mathematical biology, physics, computer science, engineering, economics, and the social sciences. One of the earliest epidemic models conceived was by Daniel Bernoulli in 1760, which was motivated by studying the spread of smallpox [1]. In addition to Bernoulli, there were many different researchers also working on mathematical epidemic models around this time [2]. These initial models were quite simplistic, and the further development and study of such models dates back to the 1900s [3]-[6], where still-simple models were studied to provide insight into how various diseases can spread through a population. In recent years, there has been a resurgence of interest in these problems as the concept of "networks" becomes increasingly prevalent in modeling many different aspects of the world today. A more comprehensive review of the history of mathematical epidemiology can be found in [7] and [8].
619 citations
TL;DR: Multiagent systems have been a major area of research for the last 15 years motivated by tasks that can be executed more rapidly in a collaborative manner or that are nearly impossible to carry out otherwise.
Abstract: Multiagent systems have been a major area of research for the last 15 years. This interest has been motivated by tasks that can be executed more rapidly in a collaborative manner or that are nearly impossible to carry out otherwise. To be effective, the agents need to have the notion of a common goal shared by the entire network (for instance, a desired formation) and individual control laws to realize the goal. The common goal is typically centralized, in the sense that it involves the state of all the agents at the same time. On the other hand, it is often desirable to have individual control laws that are distributed, in the sense that the desired action of an agent depends only on the measurements and states available at the node and at a small number of neighbors. This is an attractive quality because it implies an overall system that is modular and intrinsically more robust to communication delays and node failures.
122 citations
TL;DR: The artificial pancreas is a long-awaited goal for the management of type 1 diabetes, and its development was recently triggered by the development of continuous glucose sensors.
Abstract: In healthy individuals, blood glucose concentrations are tightly controlled by the pancreas through the secretion of two hormones, insulin and glucagon. Insulin is secreted by pancreatic beta cells and reduces blood glucose concentrations, and glucagon is secreted by pancreatic alpha cells and increases blood glucose concentrations [1], [2]. In type 1 diabetes, insulin secretion is lost due to the autoimmunedestruction of the pancreatic beta cells [3]. Type 1 diabetes accounts for 5-15% of approximately 366 million worldwide patients with diabetes [4], and its incidence is increasing at a rate of 3.9% per year [5].
118 citations
TL;DR: Existing and emerging simulation-based approaches offer improved means of testing and, in some cases, verifying the correctness of control system designs.
Abstract: Designers of industrial embedded control systems, such as automotive, aerospace, and medical-device control systems, use verification and testing activities to increase their confidence that performance requirements and safety standards are met. Since testing and verification tasks account for a significant portion of the development effort, increasing the efficiency of testing and verification will have a significant impact on the total development cost. Existing and emerging simulation-based approaches offer improved means of testing and, in some cases, verifying the correctness of control system designs.
112 citations
TL;DR: In this paper, a linear dynamic time-invariant model is identified to describe the relationship between the reference signal and the output of the system, and the power spectrum of the unmodeled disturbances are identified to generate uncertainty bounds on the estimated model.
Abstract: Linear system identification [1]?[4] is a basic step in modern control design approaches. Starting from experimental data, a linear dynamic time-invariant model is identified to describe the relationship between the reference signal and the output of the system. At the same time, the power spectrum of the unmodeled disturbances is identified to generate uncertainty bounds on the estimated model.
83 citations
TL;DR: Cooperative localization is emerging as an alternative localization technique that can be employed in large numbers of mobile agents to perform surveillance, search and rescue, transport, and delivery tasks in aerial, underwater, space, and land environments.
Abstract: Technological advances in ad hoc networking and the miniaturization of electromechanical systems are making possible the use of large numbers of mobile agents (for example, mobile robots, human agents, and unmanned vehicles) to perform surveillance, search and rescue, transport, and delivery tasks in aerial, underwater, space, and land environments. However, the successful execution of such tasks often hinges upon accurate position information, which is needed in lower-level locomotion and path-planning algorithms. Common techniques for the localization of mobile robots are the classical preinstalled beacon-based localization algorithms, fixed feature-based simultaneous localization and mapping (SLAM) algorithms, and Global Positioning System (GPS) navigation. However, these localization techniques work based on assumptions such as the existence of distinct and static features that can be revisited often or line of sight to GPS satellites, which may not be feasible for operations such as search and rescue, environment monitoring, and oceanic exploration. In the case of GPS navigation, there is also a current concern about signal jamming for outdoor navigation, especially for unmanned aerial vehicle coordination and control. Instead, cooperative localization is emerging as an alternative localization technique that can be employed in such scenarios.
79 citations
TL;DR: The residential and commercial building sector is known to use around 40% of the total end-use energy and is considered to be the largest energy consumer sector in the world as mentioned in this paper.
Abstract: The residential and commercial building sector is known to use around 40% of the total end-use energy and, hence, is considered to be the largest energy consumer sector in the world [1]. Approximately half of this energy is used for heating/cooling, ventilation, and air-conditioning (HVAC), and this usage is increasing 0.5?5% per year in developed countries [2]. The distribution of energy use percentages within the building for the United States is shown in Figure 1. This trend is similar for the rest of the world.
77 citations
TL;DR: DDF applications are many and include cooperative robots mapping a room, cooperative unmanned aerial vehicles (UAVs) geolocating a moving object on the ground, a distributed formation of space telescopes, and a group of people discussing an interesting issue, either in person or online.
Abstract: Distributed data fusion (DDF) is the process whereby a group of agents sense their local environment, communicate with other agents, and collectively try to infer knowledge about a particular process. The applications are many and include cooperative robots mapping a room, cooperative unmanned aerial vehicles (UAVs) geolocating a moving object on the ground, a distributed formation of space telescopes, and a group of people discussing an interesting issue, either in person or online.
45 citations
TL;DR: In this article, the authors propose an approach for optimizing the agent density functions such that their mutual goals are optimized, such as maintaining a desired con guration or a star formation.
Abstract: Many complex systems, ranging from renewable resources [1] to very-large-scale robotic systems (VLRS) [2], can be described as multiscale dynamical systems comprising many interactive agents. In recent years, signi cant progress has been made in the formation control and stability analysis of teams of agents, such as robots, or autonomous vehicles. In these systems, the mutual goals of the agents are, for example, to maintain a desired con guration, such as a triangle or a star formation, or to perform a desired behavior, such as translating as a group (schooling) or maintaining the center of mass of the group (flocking) [2]-[7]. While this literature has successfully illustrated that the behavior of large networks of interacting agents can be conveniently described and controlled by density functions, it has yet to provide an approach for optimizing the agent density functions such that their mutual goals are optimized.
TL;DR: Our Robots, Ourselves as mentioned in this paper is a history of human-robot interaction in the early stages of autonomous engineering systems, focusing on the interplay between humans and machines and, from this viewpoint, developing theses.
Abstract: David Mindell is one of us—a human, an engineer, a control-systems adept, a roboticist—but he is also a historian as well as a chronicler of technology and the interaction between humans and their technologies. He delivered a public lecture at the 2013 American Control Conference in Washington, D.C., “How We Interact with Robots, Feedback Loops and Autonomous Systems: Historical Perspectives and a Look Forward.” His first book [1] explores the emergence of the then-undifferentiated trio of control, computing, and communications from the common primordial technological and intellectual soup of the period between the ends of the two World Wars. The fact that Harold Black, Henrik Bode, and Harry Nyquist all worked for the telephone company is explained, as are the formative roles of Vannevar Bush, Gordon Brown, and others. In Our Robots, Ourselves, Mindell turns his attention to autonomy and our technological inclination toward constructing machines capable of navigating among us better than—or at least as well as—ourselves. His subtitle reveals his skepticism, and I am very relieved to find an ally capable of articulating the core issues. He has considerable experience in the foundational stages of autonomous engineering systems and, with his historian’s eye for evidence, is able to develop unifying themes from his observation of people interacting with these systems. Invariably, the replacement of humans in the loop winds up enhancing the performance of the human operators, since the decision support needed for autonomy improves the information available to the operator, without the attendant need for blind trust of the machine. Mindell explores examples from submersible systems, lunar landers, commercial aircraft, and military drones. He draws on experience, both personal and vicarious, such as the IEEE Control Systems Magazine’s editor-in-chief’s prang in the DARPA Grand Challenge for autonomous vehicles [5], spanning from his own doctoral studies in the early 1990s to very recent press releases from Google and Amazon and his own involvement in optionally piloted aircraft. Throughout, Mindell concentrates on the interplay between humans and machines and, from this viewpoint, develops his theses. Without spoiling the story, his three central myths of autonomy are 1) the myth of linear progress, which is that the path to full autonomy will proceed through the orderly surmounting of successive technical obstacles toward the complete solution 2) the myth of human replacement, which is that the eventual roles of humans will be reduced to passengers, observers, beneficiaries, or worse 3) the myth of complete autonomy, which is that machines will operate entirely by themselves in decision making and will learn and adapt without the requirement for human direction. While he provides a panoply of convincing examples from technology over the years, he takes great pains to establish the roles played by humans in such systems. He asks the question as to whether we would ultimately accept an armed drone on the battlefield or a surgical robot with “a mind of its own.” Being a Bostonian of many years, he also draws on examples from everyday experience to make his points. These include questions of driving in heavy snow with impaired visibility in a rapidly altering environment. Surprisingly, he omits the unwritten “count to seven after a green light before driving forward” rule, which seems to govern these particular denizens. Although Mindell is attuned to the role of humans in the operation of increasingly sophisticated and enabling machines, he is no Luddite. He draws on various systems, I EEE Control Systems Magazine welcomes suggestions for books to be reviewed in this column. Please contact either Scott R. Ploen, Hong Yue, or Hesuan Hu, the associate editors for books reviews.
TL;DR: Most of the relevant automatic control concepts, such as systems interconnection, frequency response, stability analysis, time response, and/or root locus, have been displayed graphically.
Abstract: Modern engineering projects are multidisciplinary and involve the integration of diverse elements. Within this context, automatic control plays a crucial role. Automatic control is an area of knowledge with significant mathematical content, including differential equations, linear algebra, differential geometry, and/or complex variable among others [1]. Consequently, in many cases, automatic control is difficult for most students to grasp, especially in those cases where students have only an introductory control course in their engineering curriculum. Thus, teachers look for ways to introduce and make automatic control attractive for the students [2]. From the very beginning, graphical representation has been used as support in automatic control teaching. Looking back to the pioneering books in automatic control [3]-[11], it can be seen that there are plenty of schematics and figures. Graphical abstraction was also used to contribute to theoretical understanding [12]. Most of the relevant automatic control concepts, such as systems interconnection, frequency response, stability analysis, time response, and/or root locus, have been displayed graphically [13]. This type of representation has been considered for years as an excellent way to introduce automatic control concepts.
TL;DR: In this paper, the authors proposed a command augmentation system for multi-rotor UAVs, where the vehicles cooperate and jointly execute a mission in constrained complex environments such as crowd monitoring and utility line inspection.
Abstract: Multirotor unmanned aerial vehicles (UAVs) have experienced a very fast-paced technological development over the past years. Flight control systems have evolved from simple stability augmentation systems, barely enabling an external pilot to remotely fly a multirotor UAV, to full-fledged command augmentation systems, opening up the possibilities of autonomous operations of multirotors. Due to their small size, low cost, and high agility, multirotors draw a plethora of applications, including multiple vehicles operations, where the vehicles cooperate and jointly execute a mission, in constrained complex environments such as crowd monitoring and utility line inspection.
TL;DR: High-accuracy localization is a fundamental capability that is essential for autonomous reliable operation in numerous applications, including autonomous driving, monitoring of an environmental phenomena, mapping, and tracking, thus requiring computationally efficient inference methods.
Abstract: High-accuracy localization is a fundamental capability that is essential for autonomous reliable operation in numerous applications, including autonomous driving, monitoring of an environmental phenomena, mapping, and tracking. The problem can be formulated as inference over the robot's state and possibly additional variables of interest based on incoming sensor measurements and a priori information, if such information exists. Moreover, in numerous applications, this inference problem has to be solved in real time, thus requiring computationally efficient inference methods.
TL;DR: The relationship between control systems and the Internet of Things is discussed, including hybrid systems, embedded systems, cyberphysical systems (CPS), and systems of systems.
Abstract: Discusses the relationship between control systems and the Internet of Things. Buzzwords are the drumbeat of technological progress. Casting an eye back over the last couple of decades in control, we are reminded of “hot topics” such as hybrid systems, embedded systems, cyberphysical systems (CPS), and systems of systems. The buzz today is about the Internet of Things or IoT.
TL;DR: For the purposes of this tutorial, there are two main paradigms for solving this multiagent coordination problem: (1) centralized coordination, where a single control center receives all relevant information necessary to define the motion and actions of every agent in the team, and (2) decentralized coordination,where individual agents make their own decisions about motion and action with the use of local communication and a priori information about the planning environment.
Abstract: A single robotic agent can perform cost-effective work in many domains, especially those that are dirty, dangerous, or dull for humans. Forming multiagent teams of autonomous robots can even further improve their capabilities. A major reason for this increase in performance is that teams of agents can take measurements and act in many places at once, and thus can react much faster than a single agent to a dynamic environment. Additionally, multiple individual agents with overlapping capabilities can provide mission completion robustness in the event of an individual agent failure. However, the benefits of multiagent teams come at the expense of added complexity and resource consumption required to coordinate team behavior. For the purposes of this tutorial, there are two main paradigms for solving this multiagent coordination problem: 1) centralized coordination, where a single control center receives all relevant information necessary to define the motion and actions of every agent in the team, and 2) decentralized coordination, where individual agents make their own decisions about motion and action with the use of local communication and a priori information about the planning environment.
TL;DR: This work states that the growing demand for autonomous multivehicle networks has stimulated a broad interest in distributed control and estimation strategies that support cooperative and coordinated vehicle autonomy.
Abstract: Many applications, such as environmental monitoring, security and surveillance, scientific exploration, and intelligent transportation, share a fundamental need to accomplish multiple tasks across space and time that are beyond the capabilities of a single autonomous platform. The growing demand for autonomous multivehicle networks has stimulated a broad interest in distributed control and estimation (DCE) strategies that support cooperative and coordinated vehicle autonomy. Ideally, distributed approaches would not only perform as well as centralized methods but also lead to better scalability, naturally parallelized computation, and resilience to communication loss and hardware failures.
TL;DR: The understanding of some advanced automatic control concepts are made more accessible from the mathematical point of view using graphical or tabular concepts that can be handled in any basic control course.
Abstract: For years, one of the main challenges for teachers of the automatic control courses at both the undergraduate and postgraduate levels has been breaking the barrier between the strong mathematical content of these courses and the practice of applying such knowledge to real systems. Some students envision automatic control courses as a set of abstract mathematics, and even when they understand how to solve textbook problems, few students assimilate how to apply this knowledge to real systems or grasp the true importance of such courses in various branches of engineering [1], [2]. To try to address these issues, some researchers have exerted considerable effort to make the understanding of some advanced automatic control concepts more accessible from the mathematical point of view using graphical or tabular concepts that can be handled in any basic control course [3]?[5]. Recently, with advances in computer technology, numerical simulation tools have also been widely used to facilitate the teaching-learning process in the automatic control area [6]?[8].
TL;DR: In this paper, the authors provide historical information on the origin of stability analysis in Maxwell's paper and derive his key equations using illustrative figures to improve the readability of that paper.
Abstract: In 1868, James C. Maxwell published a paper, "On Governors," in Proceedings of the Royal Society of London [1]. This paper was overlooked for a long time because it was deemed by many to be difficult to comprehend. However, since Norbert Wiener drew attention to this paper in 1948, it has been recognized as the first significant paper on control theory; as a result, Maxwell has been regarded as the "father of control theory" [2]. The purpose of this article is to provide historical information on the origin of stability analysis in Maxwell's paper and to rederive his key equations using illustrative figures to improve the readability of that paper.
TL;DR: A robotics prototyping platform, called measurable augmented reality for prototyping cyberphysical systems (MAR-CPS), is outlined, allowing for the real-time visualization of latent state information to aid hardware prototyping and performance testing of algorithms.
Abstract: Planning, control, perception, and learning are current research challenges in multirobot systems. The transition dynamics of the robots may be unknown or stochastic, making it difficult to select the best action each robot must take at a given time. The observation model, a function of the robots' sensor systems, may be noisy or partial, meaning that deterministic knowledge of the team's state is often impossible to attain. Moreover, the actions each robot can take may have an associated success rate and/or a probabilistic completion time. Robots designed for real-world applications require careful consideration of such sources of uncertainty, regardless of the control scheme or planning or learning algorithms used for a specific problem. Understanding the underlying mechanisms of planning algorithms can be challenging due to the latent variables they often operate on. When performance testing such algorithms on hardware, the simultaneous use of the debugging and visualization tools available on a workstation can be difficult. This transition from experimentation to implementation becomes especially challenging when the experiments need to replicate some feature of the software tool set in hardware, such as simulation of visually complex environments. This article details a robotics prototyping platform, called measurable augmented reality for prototyping cyberphysical systems (MAR-CPS), that directly addresses this problem, allowing for the real-time visualization of latent state information to aid hardware prototyping and performance testing of algorithms.
TL;DR: In this article, an identifier is added to generate the parameter estimates, and these estimates are directly applied in the aforementioned control law; see [1]-[3] for more details.
Abstract: The basic premise upon which adaptive control is based is the existence of a parameterized controller that achieves the control objective. Moreover, it is assumed that these parameters are not known but that they can be estimated online from measurements of the plant signals. Toward this end, an identifier is added to generate the parameter estimates. Then, applying a certainty equivalence principle, these estimates are directly applied in the aforementioned control law; see [1]-[3].
TL;DR: In some situations, advanced engineered drive techniques can improve response times by as much as a thousand fold, significantly opening up the application space for MEMS/NEMS solutions.
Abstract: Micro/nanoelectromechanical systems (MEMS/NEMS) provide the engineer with a powerful set of solutions to a wide variety of technical challenges. However, because they are mechanical systems, response times can be a limitation. In some situations, advanced engineered drive techniques can improve response times by as much as a thousand fold, significantly opening up the application space for MEMS/NEMS solutions.
TL;DR: Model predictive control (MPC) has become a dominant advanced control framework that has made a tremendous impact on both the academic and industrial control communities as discussed by the authors, and particularly notable progress has been made in the fields of robust and stochastic MPC which are now almost as well understood as their conventional MPC counterpart.
Abstract: Model predictive control (MPC) has become a dominant advanced control framework that has made a tremendous impact on both the academic and industrial control communities. Although the roots of MPC go back to the early 1960s, a remarkable surge in its popularity has taken place over the last two decades. Research in MPC has led to a plethora of advances addressing fundamental aspects, numerical considerations, and practical implementations. Particularly notable progress has been made in the fields of robust and stochastic MPC, which are now almost as well understood as their conventional MPC counterpart. Oxford’s contributions to MPC can be traced back to the early work of David W. Clarke on generalized predictive control. The Oxford control group, as well as theauthors of this book, have been very active in MPC and have developed a variety of specific approaches to MPC. This book offers a simplified and appealing overview of classical, robust, and stochastic MPC. These three areas are of paramount importance in MPC due to their fundamental nature and key role as the building blocks of more complex classical and contemporary MPC formulations. The book highlights a selection of works in which the authors of the book have contributed but also makes extensive use of methods originated and developed by other researchers in the area of control/MPC.
TL;DR: Cooperation between multiple robots or sensing devices, such as cell phones or head-mounted displays, can have great benefits in many different situations, but for coordination to be effective, they need to understand the structure of their environment.
Abstract: Cooperation between multiple robots or sensing devices, such as cell phones or head-mounted displays, can have great benefits in many different situations. Cooperating robots can maneuver through tight spaces, perform tasks in parallel, and increase redundancy and robustness to failure, while humans using intelligently connected devices can share augmented reality experiences. For coordination to be effective, though, coordinating devices and robots need to understand the structure of their environment, their locations within that environment, and their locations relative to each other.
TL;DR: In this paper, the unscented Kalman filter (UKF) is used to tune the parameters of the unsented transform (UT) to achieve better estimation performance than the EKF.
Abstract: The extended Kalman filter (EKF) [1] has been a widely used nonlinear estimation tool for more than four decades [2], but it may perform poorly when the dynamic system is not almost linear on the time scale of the update intervals [3]?[5]. In such cases, the unscented Kalman filter (UKF) [6] has the potential to achieve better estimation performance than the EKF, while having computational complexity of the same order of magnitude [6]. To work properly, the UKF requires values for the three parameters of the (scaled) unscented transform (UT) [7]. The user is thus faced with a difficult task, for which there is little theoretical guidance. This fact has given rise to numerous heuristics for tuning of the UT parameters (see "Related Work" for a brief review of the relevant literature).
TL;DR: The topic of distributed control and estimation (DCE) is of increasing relevance to robotic vehicle systems that share a fundamental need to accomplish multiple tasks across space and time that are beyond the capabilities of a single platform.
Abstract: The topic of distributed control and estimation (DCE) is of increasing relevance to robotic vehicle systems that share a fundamental need to accomplish multiple tasks across space and time that are beyond the capabilities of a single platform. The growing demand for autonomous mobile multirobot networks has stimulated broad interest in DCE strategies that naturally support cooperative, collaborative, and coordinated vehicle autonomy. When designed and implemented properly, the performance of distributed approaches can approach that of centralized methods, but they also provide better scalability, naturally parallelized computation, and resilience to communication loss and hardware failures.
TL;DR: The pioneering work on variable structure and sliding-mode control (VS&SMC) was started in the Soviet Union in the 1950s as mentioned in this paper, and the robustness properties of variable structure control attracted the attention of the international control community.
Abstract: The pioneering work on variable structure and sliding-mode control (VS&SMC) was started in the Soviet Union in the 1950s. In the late 1970s, after the publication of the books [1], [2] and the tutorial [3], the robustness properties of variable-structure control attracted the attention of the international control community.
TL;DR: This article presents a set of three interactive software tools for time series analysis education (ITTSAE) written in Sysquake, a language similar to that used in Matlab, and that generates tools with interactive graphics that have simple interfaces.
Abstract: A time series is a set of observations that are generated sequentially in time. This article considers discrete-time series, where observations are made at some fixed interval h. A time series can also be considered as a possible realization of a stochastic process, which is a statistical phenomenon that evolves in time according to probabilistic laws.
TL;DR: In this article, the authors provide a comprehensive view of Lyapunov stability that should be accessible to mathematically inclined graduate students and to many researchers in the control field, which is a welcome addition to the published literature.
Abstract: The contents of the book are a review of dynamical systems,the principal stability and boundedness results on metric spaces, specialized stability and boundedness results on metric spaces, applications to discrete-event systems, stability results for finite-dimensional dynamical systems, applications to the stability of finite-dimensional dynamical systems, and stability results for infinite-dimensional systems. Each chapter ends with three final sections: Notes and References, Problems, and Bibliography; these sections are welcome additions that provide insights and perspectives beyond the main content of the chapters. This second edition provides more emphasis on the stability of discrete-event and hybrid systems than the first edition [1] (see [2] for an earlier review), and it emphasizes the use of non-monotonic Lyapunov functions, which the authors developed in their recent research publications. In the Preface, the authors motivate the introduction of non-monotonic Lyapunov functions; this generalization is used to derive a general form of Lyapunov stability results. Further, in the case that the Lyapunov functions are monotonic, standard Lyapunov stability results are obtained. In practice, this extension is primarily motivated by discrete event and hybrid dynamical systems. These new features add significant material to the first edition. This new edition of the book provides a scholarly and comprehensive view of Lyapunov stability that should be accessible to mathematically inclined graduate students and to many researchers in the control field. It is a welcome addition to the published literature.