Showing papers in "Annual Reviews in Control in 2020"

TL;DR: This paper provides a unified framework for model predictive building control technology with focus on the real-world applications and presents the essential components of a practical implementation of MPC such as different control architectures and nuances of communication infrastructures within supervisory control and data acquisition (SCADA) systems.

TL;DR: A quantitative comprehension of SARS-CoV-2 dynamics and the estimation of standard parameters of viral infections is the key contribution of this pioneering work.

TL;DR: The use of reinforcement learning in sustainable energy and electric systems will certainly change the traditional energy utilization mode and bring more intelligence into the system, and future challenges and opportunities will be explicitly addressed.

TL;DR: This paper summarizes the vision, challenges and opportunities in this research field, based on the experience of the authors, members of the Working Group 7 “Human factors and ergonomics in industrial and logistic system design and management” of the IFAC Technical Committee (TC) 5.2.

TL;DR: A modified Susceptible-Infected-Recovered-Deceased (SIRD) model for the contagion is developed, and official data of the pandemic is used for identifying the parameters of this model.

TL;DR: A Model Predictive Control policy to mitigate the COVID-19 contagion in Brazil, designed as optimal On-Off social isolation strategy, and results indicate a possible second peak of infections, which has a forecast to the beginning of October.

TL;DR: A variety of mathematical models that have been proposed to capture the dynamic behavior of epidemic processes are proposed, including traditional group models for which no underlying graph structures are assumed, thus implying that instantaneous mixing between all members of a population occurs.

TL;DR: Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.

TL;DR: A comprehensive literature review on the strategies and methodologies applied for formation control of multiple unmanned ground vehicles in both normal and faulty situations is presented.

TL;DR: An optimal control approach is proposed that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario and supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model.

TL;DR: This paper reviews the available works considering LPV MPC design, ranging from the sub-optimal, simplified, yet Quadratic Programming (QP) algorithms, the tube-based tools, the set-constrained procedures, the Nonlinear Programming procedures and the robust ones; the main features of the recent research body on this topic are examined.

TL;DR: This survey paper comprehensively reviews the state-of-the-art development of collaborative robotic manipulation from the perspective of modelling, control and optimization.

TL;DR: A new dimension reduction expression of state space framework is presented to unify dynamic latent variable analytics for process data, dynamic factor models for econometrics, subspace identification of multivariate dynamic systems, and machine learning algorithms for dynamic feature analysis.

TL;DR: This paper provides a unified framework for the analysis and design of parameter estimators and shows that they lie at the core of some modified schemes recently proposed in the literature, and uses this framework to propose some new schemes with relaxed conditions for convergence and improved transient performance.

TL;DR: The future research directions include the design of models and methods to optimize HRC systems in terms of ergonomics, safety, and throughput, as well as studying flexibility and reconfigurability in hybrid systems.

TL;DR: The history of the IOC and Inverse Reinforcement Learning approaches is reviewed and the connections and differences between them are described to cover the research gap in the existing literature and the general formulation of IOC/IRL is described.

TL;DR: It is shown that “herd immunity” can be achieved when the total of infected, symptomatic and asymptomatic persons is as low as 25% of the population, and “lockdown” as a way of greatly reducing inter-personal contact has been very effective in checking the progress of the disease.

TL;DR: This effort focuses on recent trends in autonomous maintenance before moving on to discuss digital twin as a vehicle for decision making from the viewpoint of requirements, whilst the role of AI in assisting with this process is also explored.

TL;DR: The objective of this work is to classify, scrutinize and review the SMC techniques used to extract maximum power from PVSs in both off-grid and grid connected applications and provides important insight into MPPT control of the PV systems.

TL;DR: Using a control theoretical approach, validated mathematical models of SARS-CoV-2 in humans are characterized and a complete analysis of the main dynamic characteristic is developed based on the reproduction number.

TL;DR: This vision paper aims to address distributed decision-making in digital and integrated manufacturing networks under a socio-cyber-physical systems perspective by presenting a narrative review of current research literature, followed by a research framework proposition.

TL;DR: This paper proposes a direct way of extending nonlinear modes to mechanical systems with a non Euclidean metrics (e.g. robots) using a language familiar to the control theorist.

TL;DR: Ten “messages,” intended primarily for researchers interested in the practical impact of their work, that are hoped shed insight on the industry mindset, are presented.

TL;DR: Adaptive control of linear stochastic systems, in particular, the global stability and optimality of the well-known self-tuning regulators, designed by combining the least-squares estimator with the minimum variance controller are considered.

TL;DR: It is indicated that approximate models can considerably influence practical performance of optimally tuned FOPID control systems and ignorance of limitations of approximation methods in optimal tuning solutions can significantly affect real world performances.

TL;DR: The paper concludes with the most recent development trends, and ideas for future work, including possible applications of model predictive control as well as integration of anti-jerk controllers with autonomous driving systems and other vehicle control functions.

TL;DR: An extension of the classical pandemic SIRD model is considered for the regional spread of COVID-19 in France under lockdown strategies and a new model for the network pandemic model of each age class is provided.

TL;DR: This paper provides an overview on the distinct methods to test the parameter identifiability, classified into the so-called output equality approaches, local state isomorphism approaches and differential algebra approaches.

TL;DR: This paper collects the recent work and classifies them according to the use of YK parametrization, Dual Y kucera or both, providing the latest advances with main applications in different control fields.

TL;DR: A thorough review of the current literature and several study trends for the future in this area are presented including data quality problems, small object detection, embedded application, and evaluation baseline.