Showing papers in "Annual Reviews in Control in 2021"

TL;DR: In this article, the authors investigate adaptive strategies to robustly and optimally control the COVID-19 pandemic via social distancing measures based on the example of Germany and propose a robust MPC-based feedback policy using interval arithmetic.

TL;DR: A framework for decentralized and integrated decision-making for re-scheduling of a cyber-physical production system is presented, and the validation and proof-of-concept of the proposed method in an Industry 4.0 pilot line of assembly process demonstrates that the proposed framework is capable to detect changes in the manufacturing process and to make appropriate decisions for re -scheduled the process.

TL;DR: This survey article explores insights from the development and experimental deployment of control systems for airborne wind energy platforms over approximately the past two decades, highlighting both the optimal control approaches that have been used to extract the maximal amount of power from tethered systems and the robust modal control approaches used to achieve reliable launch, landing, and extreme wind operation.

TL;DR: A new version of the well-known epidemic mathematical SEIR model is used to analyze the pandemic course of COVID-19 in eight different countries, and one of the proposed model’s improvements is to reflect the societal feedback on the disease and confinement features.

TL;DR: This review presents and classify PID tuning methods till date and presents a proposal to minimize the dilemma of complexity and cost that has become associated with tuning the three main parameters of the PID control law.

TL;DR: In this article, the authors present a survey of different models proposed in the literature, assembling a list of 36 model structures and assessing their ability to provide reliable information using the control theoretic concepts of structural identifiability and observability.

TL;DR: Data-driven analysis, signal processing, and control methods as mentioned in this paper can be broadly classified as implicit and explicit approaches, with the implicit approach being more robust to uncertainty and robustness to noise.

TL;DR: A review of past and present results and approaches in the area of motion planning using MIP (Mixed-integer Programming) and an emphasis is laid on the existing alternatives for implementation and on various experimental validations documented in the literature.

TL;DR: A two-bladed downwind morphing rotor concept is overview that is expected to lower the cost of energy at wind turbine sizes beyond 13 megawatts (MW) compared with continued upscaling of traditional three-blade upwind rotor designs.

TL;DR: In this paper, a literature review deals with the research conducted over the past decades on the topic of semi-active and active suspension controllers with road preview, and discusses the effect of the road preview time on the resulting system performance, and identifies control development trends.

TL;DR: Commonalities and differences of those distributed strategies that exploit the degree of interaction between control agents to boost the mentioned properties are reviewed, frequently leading to control structures where the communication network becomes a decision variable that may evolve dynamically.

TL;DR: In this paper, the authors derived a linear relationship among the optimal start time and duration of a single interval of social distancing from an approximation of the classic epidemic SIR model.

TL;DR: An overview of the main factors of control-oriented models and control strategies for OCIS is presented and the acceptability of the reported modeling and control techniques is established.

TL;DR: In this article, a review on the development and application of model predictive control (MPC) for autonomous intelligent mechatronic systems (AIMS) is presented, starting from the conceptual analysis of "mechatronics" and analyzing the characteristics and control system design requirements of AIMS.

TL;DR: In this paper, the Koopman operator theory has long-standing connections to known system-theoretic and dynamical system notions that are not universally recognized, and the authors aim to bridge the gap between various concepts regarding both theory and tractable realizations.

TL;DR: A detailed overview of recent advances, successes, and promising directions for optimization-based space vehicle control can be found in this paper, where the primary focus is on the last ten years, which have seen a veritable rise in the number of applications using three core technologies: lossless convexification, sequential convex programming, and model predictive control.

TL;DR: In this article, a historical perspective of the field of adaptive control over the past seven decades and its intersection with learning is provided, along with a chronology of key events over this large time-span, problem statements that the field has focused on, and key solutions are presented.

TL;DR: Contraction theory is an analytical tool to study differential dynamics of a non-autonomous (i.e., time-varying) nonlinear system under a contraction metric defined with a uniformly positive definite matrix, the existence of which results in a necessary and sufficient characterization of incremental exponential stability of multiple solution trajectories with respect to each other as mentioned in this paper.

TL;DR: It is concluded that low-cost monitoring can be feasible given the right application and operating environment.

TL;DR: The paper identifies the need for interoperability in system-of-systems in contrast to integration in a single system and issues due to insufficient support for physical aspects of systems.

TL;DR: In this paper, the advantages of reinforcement learning over traditional model-based optimal control methods and how it can be tailored to better address the characteristics of industrial batch process control problems are examined.

TL;DR: The main point of this review paper is to classify hyperspectral-LiDAR and hyperspectRAL-SAR data fusion with approaches and recent achievements about the fusion are highlighted.

TL;DR: In this article, the role of data-driven methodologies for pandemic modelling and control is discussed, and a roadmap from the access to epidemiological data sources to the control of epidemic phenomena is provided.

TL;DR: A concept of DES control system architecture based on predictive models based on data mining based model generation is offered for estimating and predicting the state of resources in production processes.

TL;DR: It is seen that both the formulation of corresponding stochastic control problems and the tools to solve them may differ considerably from their deterministic/finite-dimensional counterparts.

TL;DR: In this article, a policy to smartly select the individuals to be tested was proposed based on the observation that, during Covid-19 epidemic, the choice of which individuals should be tested has an important impact on the effectiveness of selective confinement measures.

TL;DR: In this article, the authors use a model-based approach based on a stochastic susceptible-infections-removed (SIR) model with time-varying parameters, which captures the evolution of the disease dynamics in response to changes in social behavior, non-pharmaceutical interventions, and testing rates.

TL;DR: In this paper, a stochastic model of epidemic temporal growth and mitigation based on a time-modulated Hawkes process is proposed to capture the impact of undetected, asymptomatic and super-diffusive individuals.

TL;DR: This paper is a literature review on ICC, and focuses on the topics that are left uncovered by the most recent surveys on the subject, or that are dealt with only by old surveys, namely: a) the systematic categorisation of the available ICC architectures, with the critical analysis of their strengths and weaknesses.

TL;DR: In this article, the authors present a solution for crowd management based on a POI recommendation system that suggests the nearest safe options upon request of a particular POI to visit by the user.