We design an adaptive full-state feedback controller to stabilize a one-dimensional reaction–diffusion equation with unknown boundary input delay. An infinite-dimensional representation of the actuator delay is utilized to transform the system into a transport PDE cascading with a reaction–diffusion PDE. A suitable parameter update law is designed to establish local boundedness of the system trajectories

We study stability of multivariable control-affine nonlinear systems under sparsification of feedback controllers. Given a stabilizing feedback, sparsification in our context refers to the scheduling of the individual control inputs one at a time in rapid periodic sweeps over the set of control inputs, which corresponds to round-robin scheduling. We prove that if a locally asymptotically stabilizing

Agglomeration benefits firms because it minimizes consumers' search costs and information asymmetry. However, the effects of agglomeration may change due to the considerable amount of information available in online platforms. Thus, drawing upon the theoretical lens of information transparency and agglomeration theory, we investigate how online reviews change the effect of agglomeration on firm performance

Due to their relevance in systems analysis and (robust) controller design, we consider the problem of determining control-theoretic system properties of an a priori unknown system from data only. More specifically, we introduce a necessary and sufficient condition for a discrete-time linear time-invariant system to satisfy a given integral quadratic constraint (IQC) over a finite time horizon using

Automatic clustering is a challenging problem, especially when the decision-maker has little or no information about the nature of the dataset and the criteria of interest. There is a lack of generalizability in the current validity indexes (VI) for automatic clustering algorithms, as each considers a limited number of objectives and mostly ignores the other aspects of clustering validation. The proposed

This paper describes the foundations of interpretable data science for decision making and serves as an editorial to the corresponding special issue. Interpretable data science analyzes data that summarizes domain relationships to produce knowledge that is readily understandable by human decision makers. To this end, we contextualize the current role of interpretable data science for improved business

In this paper, an asymptotic stability proof for a class of methods for inexact nonlinear model predictive control is presented. General Q-linearly convergent online optimization methods are considered and an asymptotic stability result is derived for the setting where a limited number of iterations of the optimizer are carried out per sampling time. Under the assumption of Lipschitz continuity of

This paper develops a method for obtaining guaranteed outer approximations for global attractors of continuous and discrete time nonlinear dynamical systems. The method is based on a hierarchy of semidefinite programming problems of increasing size with guaranteed convergence to the global attractor. The approach taken follows an established line of reasoning, where we first characterize the global

This article is the first to test whether the attraction effect can be replicated among recommended options in a real digital marketplace, namely – an online flight aggregator. For this purpose, we conducted a large-scale field experiment on an existing flight aggregator, in which we varied the number of the options recommended at the top of the result list. More precisely, we investigated whether

This paper compares the performance, in terms of expected opportunity loss of mistakes, of group decision making with fallible members, under different organization structures. These structures result from combining the decision mechanisms of authority and consensus with the communication networks of star, tree, and full network. Simulation results from Agent Based Modeling show that the authority

We design an event-triggered boundary controller for a continuum model of highly re-entrant manufacturing systems for which the influx rate of products is the controlled quantity. Mathematically, the governing equation of the process is a nonlinear hyperbolic partial differential equation with a nonlinearity arising from the nonlocal propagation speed and the positivity state constraint. The nonlinearity

This paper presents a new method for bounding the set of trajectories consistent with a system of nonlinear ordinary differential equations, a compact set of admissible time-invariant uncertainties, and a set of state constraints. Such reachability bounds are important in set-based state estimation, fault detection, robust control, etc. Interval reachability methods based on differential inequalities

Thermodynamics has been the subject of intense scientific debate throughout its long history (see "Summary"). The following famous quote from Albert Einstein's autobiographical notes expresses his admiration for the theory of classical, macroscopic thermodynamics [1]

The processes of information diffusion across social networks (for example, the spread of opinions and the formation of beliefs) are attracting substantial interest in disciplines ranging from behavioral sciences to mathematics and engineering (see "Summary"). Since the opinions and behaviors of each individual are infl uenced by interactions with others, understanding the structure of interpersonal

The objective of navigation is to determine the instantaneous location of a moving vehicle. Historically, sailors estimated the speed of a ship by dropping a log into the water and observing the time for it to float from the bow to the stern [1, pp. 21-38]. Combining this speed information with directional data from a compass yields a velocity estimate. Finally, integrating the velocity estimate with

We consider the problem of reconstructing a function from a finite set of noise-corrupted samples. Two kernel algorithms are analyzed, namely kernel ridge regression and ɛ-support vector regression. By assuming the ground-truth function belongs to the reproducing kernel Hilbert space of the chosen kernel, and the measurement noise affecting the dataset is bounded, we adopt an approximation theory viewpoint

Sensor and actuator selection problems (SASPs) are some of the core problems in dynamic systems design and control. These problems correspond to determining the optimal selection of sensors (measurements) or actuators (control nodes) such that certain estimation/control objectives can be achieved. While the literature on SASPs are indeed inveterate, the vast majority of the work focuses on linear(ized)

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In this issue, “25 Years Ago” revisits the article “Anti-Windup, Bumpless, and Conditioned Transfer Techniques for PID Controllers,” by Youbin Peng, Damir Vrancic, and Raymond Hanus, in IEEE Control Systems Magazine, vol. 16, no. 4, pp. 48–57, 1996. Below is an excerpt from the article.

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Reports on the activities of hte CS society Technical Committee on Manufacturing Automation and Robotic Control.

Reports on the technical activities of the CS society Technical Committee on Smart Grid.

Presents an interview conducted with Diana Yanakiev.

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Presents an interview conducted with Maruthi R. Akella.

In recent decades, there has been a rapid development of theory and methods for optimal transport, both within systems and control as well as in other areas such as signal processing, medical imaging, statistics, and machine learning. These advancements have developed optimal transport into an extensive framework with a large set of theoretical and computational tools that can be used to address problems

How data became one of the most powerful tools to fight an epidemic is a question that a recent (10 June 2020) The New York Times article poses in its title. Indeed, the spread of COVID-19 involves dynamically evolving hidden data (for example, the number of infected people, the number of asymptomatic people) that must be deduced from noisy and partially observed data (for example, the number of daily

A driving force in the development of technology, and hence society as a whole, is the automation of processes that were previously executed manually. For many tasks, this comes with an intrinsic need for sensing, estimation, and actuation-concepts that are well known in this community. Moreover, an increasing number of such applications involve large networks containing ensembles of agents that interact

Have you ever thought about how to ship a collection of packages from their original locations to fi nal destinations, how to construct a wall using a pile of bricks, or how to pair customers with providers in an industrial supply chain? If your answer is yes to any of these questions, you thought about optimal transport. If your answer is no, you still must have encountered problems like how to drive

Our account begins in 1931/1932, when one of the fathers of quantum theory, Erwin Schrödinger, proposed a Gedankenexperiment to comprehend the propagation of a cloud of Brownian particles. He proposed [1], [2] the inference problem to identify the most likely evolution of a large collection of particles as they traverse between two endpoints in time— their distribution being measured and available

This book is an accessible and comprehensive introduction to the field of data-driven science and engineering. It is unique in the sense that it brings together interdisciplinary concepts from machine learning, dynamical systems, and feedback control and applies them to physical systems arising in science and engineering. The book provides a broad overview of these concepts and develops tools for data-driven

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Recounts the career and contributions of Dr. Radhakishan Sohanlal Baheti.

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In the digital economy, firms' IT resource investments represent a significant portion of their capital investments. IT governance processes are critical for companies to decide what and how to deploy IT resources and measure and achieve the expected business benefits. This study introduces and measures the concept of IT governance process capability, which is defined as the firm's ability to identify

The problem of exactly differentiating a signal with bounded second derivative is considered. A class of differentiators is proposed, which converge to the derivative of such a signal within a fixed, i.e., a finite and uniformly bounded convergence time. A tuning procedure is derived that allows to assign an arbitrary, predefined upper bound for this convergence time. It is furthermore shown that this

In this paper we address data-driven control design with regular language specifications for plants described as abstract systems, i.e. as collections of input-state functions. The abstract system is assumed to be suffix and concatenation closed, causal, deterministic and time-invariant. The system is unknown, apart from a collected finite set of experiments. Given a specification expressed as a regular

This note extends a recently proposed algorithm for model identification and robust model predictive control (MPC) of asymptotically stable, linear time-invariant systems subject to process and measurement disturbances. Independent output predictors for different horizon values are estimated with Set Membership methods. It is shown that the corresponding prediction error bounds are the least conservative

In this paper, a class of optimization problems is investigated, where the objective function is the sum of N convex functions viewed as local functions and the constraints are N nonidentical closed convex sets. Additionally, it is aimed to solve the considered optimization problem in a distributed manner and thus a sequence of time-varying unbalanced directed graphs is introduced first to depict the

It is well-known that terminal state constraints play an instrumental role in ensuring the feasibility and stability of the nonlinear model predictive control (MPC). Yet, they inherently and largely limit the size of the feasible region and restrict the use of MPC to practical applications. In this paper, a terminal cost characterized by an implicit sliding mode control (SMC) law is proposed for developing

This work shows an approach to achieve output consensus among heterogeneous agents in a multi-agent environment where each agent is subject to input constraints. The communication among agents is described by a time-varying directed/undirected graph. The approach is based on the well-known Internal Model Principle which uses a specific class of unstable reference systems. One contribution of this work

In this paper, the finite-time control problem for a class of double integrator systems under state constraints is investigated. Different from the traditional finite-time control methods with constant gains, an improved finite-time controller with state-dependent gains is designed. The main challenges lie in the finite-time stability analysis of the resultant closed-loop system caused by state-dependent

Decision-making in everyday life has an essential role in effectively completing personal tasks and processes. The complexity of these processes and the resulting cognitive load of managing them may vary significantly. To decrease the cognitive load created by such decision-making efforts and to obtain better outcomes, recommendation systems carry significant potential. In order to investigate the

Many online question-and-answer (Q&A) communities have tried to motivate knowledge contribution through the implementation of gamification mechanisms. We know little, though, about the effectiveness of specific hierarchical privilege levels (a.k.a. “leveling up”) and non-hierarchical incentives (e.g., independent reputation gains and achievement badges) in promoting knowledge contribution. Drawing

Ticket-switching is the process by which the price information on a relatively expensive item is switched with one from a cheap item. This allows the customer to pay the cheap item price for the expensive item. Evidence suggests that ticket-switching events in retail supply chains may not be rare and therefore need to be addressed. We consider ticket-switching in RFID-based systems and develop four

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This paper presents the output feedback control design for a tracking problem of a small-scale helicopter in the presence of uncertainties. The dynamics of the helicopter is an underactuated mechanical system and the form of control inputs is nonaffine. A time-scale approach is suggested to cope with underactuated mechanical systems to overcome lack of the number of inputs. A newly developed dynamic

This paper describes an optimization framework to control a distributed parameter system (DPS) using a team of mobile actuators. The framework simultaneously seeks optimal control of the DPS and optimal guidance of the mobile actuators such that a cost function associated with both the DPS and the mobile actuators is minimized subject to the dynamics of each. The cost incurred from controlling the

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In this paper, we propose a two-stage algorithm utilizing the Cauchy integral and the matrix-valued adaptive Fourier decomposition (abbreviated as matrix AFD) to identify transfer functions of linear time-invariant (LTI) multi-input multi-output (MIMO) systems in the continuous time case. In recent work of Alpay et al. (2017), a theory of adaptive rational approximation to matrix-valued Hardy space