Mismatched disturbances generally appear in vehicular platoons, which may cause severe deterioration for the platooning control. We are, therefore, motivated to consider the problem of platooning control with mismatched disturbances, for which we propose a distributed adaptive backstepping control approach. The main challenges are 1) ensuring the internal and string stability of the platoon, 2) maintaining the compositionality and the distributed nature of the controller, and 3) ensuring the robustness of the controller with respect to general types of disturbances. To address these challenges, we propose a novel notion namedVector String Lyapunov Functions, whose existence implies$l_{2}$weak string stability. This notion is based on the vector Lyapunov function-based stability analysis, which depends on the input-to-state stability of a comparison system. Using this notion, we propose a novel distributed adaptive backstepping controller for the platoon to ensure the controller’s compositionality and distributed nature. At the same time, the closed-loop system’s internal stability and string stability are formally guaranteed. Finally, simulation studies are conducted to illustrate the effectiveness of the proposed control method.

The main goal of this study is to extract a set of brain networks in multiple time-resolutions to analyze the connectivity patterns among the anatomic regions for a given cognitive task. We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolutions. The suggested architecture is tested on task data set of Human Connectome Project (HCP) where we extract multi-resolution networks, each of which corresponds to a cognitive task. At the first level of this architecture, we decompose the fMRI signal into multiple sub-bands using wavelet decompositions. At the second level, for each sub-band, we estimate a brain network extracted from short time windows of the fMRI signal. At the third level, we feed the adjacency matrices of each mesh network at each time-resolution into an unsupervised deep learning algorithm, namely, a Stacked De- noising Auto-Encoder (SDAE). The outputs of the SDAE provide a compact connectivity representation for each time window at each sub-band of the fMRI signal. We concatenate the learned representations of all sub-bands at each window and cluster them by a hierarchical algorithm to find the natural groupings among the windows. We observe that each cluster represents a cognitive task with a performance of 93% Rand Index and 71% Adjusted Rand Index. We visualize the mean values and the precisions of the networks at each component of the cluster mixture. The mean brain networks at cluster centers show the variations among cognitive tasks and the precision of each cluster shows the within cluster variability of networks, across the subjects.

Passivity theory is a well-established tool for analysis and synthesis of dynamical systems. Recently, this work has been extended to switched and hybrid systems where passivity and stability results of single systems as well as interconnected systems are derived. However, the results may no longer hold when quantization is present as is the case with digital controllers or communication channels. The contribution in this paper is to introduce a control framework under which passivity for switched and non-switched systems can be maintained. This framework centers on the use of an input-output coordinate transformation to recover the passivity property. In order to present these results, background material is provided on passive quantization and output strict passivity for switched and non-switched systems. The proposed framework is first presented for non-switched systems and then generalized to switched systems.

System integration is the elephant in the china store of large-scale cyber-physical system (CPS) design. It would be hard to find any other technology that is more undervalued scientifically and at the same time has bigger impact on the presence and future of engineered systems. The unique challenges in CPS integration emerge from the heterogeneity of components and interactions. This heterogeneity drives the need for modeling and analyzing cross-domain interactions among physical and computational/networking domains and demands deep understanding of the effects of heterogeneous abstraction layers in the design flow. To address the challenges of CPS integration, significant progress needs to be made toward a new science and technology foundation that is model based, precise, and predictable. This paper presents a theory of composition for heterogeneous systems focusing on stability. Specifically, the paper presents a passivity-based design approach that decouples stability from timing uncertainties caused by networking and computation. In addition, the paper describes cross-domain abstractions that provide effective solution for model-based fully automated software synthesis and high-fidelity performance analysis. The design objectives demonstrated using the techniques presented in the paper are group coordination for networked unmanned air vehicles (UAVs) and high-confidence embedded control software design for a quadrotor UAV. Open problems in the area are also discussed, including the extension of the theory of compositional design to guarantee properties beyond stability, such as safety and performance.

This paper extends some prior work by the authors to address general robustness of solutions in multiagent coordination control problems. In particular, it focuses on fault-tolerant formation control. In our prior work, symmetries in the system were exploited to simplify the nonlinear Lyapunov stability analysis for symmetric systems. The type of symmetry considered is a discrete symmetry where the system is composed of many repeated instances of interacting identical agents. The results are based on Lyapunov methods, and hence are of general applicability and specifically these results are applicable to both distributed as well as non-distributed coordination methods. This paper considers the same types of systems and extends the stability results to the cases of robustness of formation stability under failure of individual agents.

Real-life cyber-physical systems, such as automotive vehicles, building automation systems, and groups of unmanned vehicles are monitored and controlled by networked control systems. The overall system dynamics emerges from the interaction among physical dynamics, computational dynamics, and communication networks. Network uncertainties such as time-varying delay and packet loss cause significant challenges. This paper proposes a passive control architecture for designing wireless networked control systems that are insensitive to network uncertainties. We describe the architecture for a system consisting of a robotic manipulator controlled by a digital controller over a wireless network and we show that the system is stable even in the presence of time-varying delays. We present simulation results that demonstrate the advantages of the architecture with respect to stability and performance and show that the system is insensitive to network uncertainties.

A current survey of the emerging field of networked control systems is provided. The aim is to introduce the fundamental issues involved in designing successful networked control systems, to provide a snapshot assessment of the current state of research in the field, to suggest useful future research directions, and to provide a broad perspective on recent fundamental results. Reflecting the goals of the Special Issue itself, this paper surveys relevant work from the areas of systems and control, signal processing, detection and estimation, data fusion, and distributed systems. We discuss appropriate network architectures, topics such as coding for robustly stable control in the presence of time-varying channel capacity, channels with fixed versus adaptively variable data width, issues in data rate problems in nonlinear feedback problems, and problems in routing for stability and performance. In surveying current research on networked control systems, we find that recent theoretical advances and target applications are intimately intertwined. The common goal of papers in the Special Issue which follows is to describe key aspects of this relationship. We also aim to provide a bridge between networked control systems and closely related contemporary work dealing with sensor networks and wireless communication protocols

Given an arbitrary Petri net (PN) structure, which may have uncontrollable and unobservable transitions, the deadlock prevention procedure presented here determines a set of linear inequalities on the PN markings. When the PN is supervised so that its markings satisfy these inequalities, the supervised net is proved to be deadlock-free for all initial markings that satisfy the supervision constraints. Deadlock-freedom implies that there will always be at least one transition that is enabled in the closed-loop (supervised) system. The method is not guaranteed to ensure liveness, as it can be applied to systems that cannot be made live under any circumstances. However, for controllable and observable PNs, it is shown that, when the method ensures liveness as well, the liveness-ensuring supervisor is least restrictive. Moreover, it is shown that the method is not restrictive even for PNs in which not all transitions can be made live. The procedure allows automated synthesis of the supervisors.

This paper examines congestion control for explicit rate data networks. The available bit rate (ABR) service category of asynchronous transfer mode (ATM) networks serves as an example system for this paper, however the results of this paper are applicable to other explicit rate systems as well. After a plant model is established, an adaptive control strategy is presented and compared to other strategies. Two algorithm enhancements are then introduced. These enhancements reduce the convergence time and improve queue depth management. This work differentiates itself from the other contributions in the area of rate-based congestion control in its balanced approach of retaining enough complexity as to afford attractive performance properties, but not so much complexity as to make implementation prohibitively expensive.

In this paper, the supervisory control of hybrid systems is introduced and discussed at length. Such control systems typically arise in the computer control of continuous processes, for example, in manufacturing and chemical processes, in transportation systems, and in communication networks. A functional architecture of hybrid control systems consisting of a continuous plant, a discrete-event controller, and an interface is used to introduce and describe analysis and synthesis concepts and approaches. Our approach highlights the interaction between the continuous and discrete dynamics, which is the cornerstone of any hybrid system study. Discrete abstractions are used to approximate the continuous plant. Properties of the discrete abstractions to be appropriate representations of the continuous plant are presented, and important concepts such as determinism and controllability are discussed. Supervisory control design methodologies are presented to satisfy control specifications described by formal languages. Several examples are used throughout the paper to illustrate our approach.

An algorithm for constructing and training multilayer neural networks, dependence identification, is presented in this paper. Its distinctive features are that (i) it transforms the training problem into a set of quadratic optimization problems that are solved by a number of linear equations, (ii) it constructs an appropriate network to meet the training specifications, and (iii) the resulting network architecture and weights can be further refined with standard training algorithms, like backpropagation, giving a significant speedup in the development time of the neural network and decreasing the amount of trial and error usually associated with network development.

Hybrid control systems consist of a discrete event (DES) controller supervising a continuous-state (CSS) plant. A controller can be synthesized by obtaining a DES controller for an equivalent DES representation (DES plant) of the CSS plant. An important issue concerns the logical invariance (stability) of DES plant transitions to variations in the initial CSS plant state. This paper provides a set of sufficient conditions for the existence of stable transitions in the DES plant. For CSS plants which are affine in their control policies, these conditions form a system of linear inequalities over the space of control vectors used by the CSS plant. Feasible points to this inequality system are inductively determined using a method of centers algorithm known as the ellipsoid method.

Given a graph with arcs that have costs, the A* algorithm is designed to find the shortest path from a single node to a set of nodes. While the A* algorithm is well understood, it is somewhat limited in its application due to the fact that it is often difficult to specify the "heuristic function" so that A* exhibits desirable computational properties. In this paper a metric space approach to the specification of the heuristic function is introduced. It is shown how to specify an admissible and monotone heuristic function for a wide class of problem domains. In addition, when the cost structure for the underlying graph is specified via a metric, it is shown that admissible and monotone heuristic functions are easy to specify and further computational advantages can be obtained. Applications to an optimal parts distribution problem in flexible manufacturing systems and artificial intelligence planning problems are provided.>

In rule-based artificial intelligence (AI) planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some working memory. Normally, the intent is to determine which rules are relevant to the current situation (i.e., to find the conflict set). A technique using a multilayer perceptron to solve the match phase problem for rule-based AI systems is presented. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is presented.>

article Stability and stabilizability of discrete event dynamic systems Share on Authors: Cüneyt M. Özveren Massachusetts Institute of Technology, Cambridge Massachusetts Institute of Technology, CambridgeView Profile , Alan S. Willsky Massachusetts Institute of Technology, Cambridge Massachusetts Institute of Technology, CambridgeView Profile , Panos J. Antsaklis Univ. of Notre Dame, Notre Dame, IN Univ. of Notre Dame, Notre Dame, INView Profile Authors Info & Claims Journal of the ACMVolume 38Issue 3July 1991 pp 729–751https://doi.org/10.1145/116825.116855Online:01 July 1991Publication History 92citation799DownloadsMetricsTotal Citations92Total Downloads799Last 12 Months6Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

A new derivation is presented for the bounds on the size of a multilayer neural network to exactly implement an arbitrary training set; namely the training set can be implemented with zero error with two layers and with the number of the hidden-layer neurons equal to #1>/= p-1. The derivation does not require the separation of the input space by particular hyperplanes, as in previous derivations. The weights for the hidden layer can be chosen almost arbitrarily, and the weights for the output layer can be found by solving #1+1 linear equations. The method presented exactly solves (M), the multilayer neural network training problem, for any arbitrary training set.

A description is given of 11 papers from the April 1990 special issue on neural networks in control systems of IEEE Control Systems Magazine. The emphasis was on presenting as varied and current a picture as possible of the use of neural networks in control. The papers described cover: the design of associative memories using feedback neural networks; a method to use neural networks to control highly nonlinear systems; the modeling of nonlinear chemical systems using neural networks; the identification of dynamical systems; the comparison of conventional adaptive controllers and neural-network-based controllers; a method to provide adaptive control for nonlinear systems; neural networks and back-propagation; the back-propagation algorithm; the use of trained neural networks to regulate the pitch attitude of an underwater telerobot; the control of mobile robots; and the issues involved in integrating neural networks and knowledge-based systems.