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About 10,000 papers for “Control Systems in Engineering”

Competitive control in systems engineering.

Van Voorhees, Franklin David., Van Voorhees, Franklin David. · UA Campus Repository (The University of Arizona) · 1995

The three main difficulties of the product development process are non-convexity, uncertainty, and multiobjectivity. Because of these problems, the product development process is characterized using two metaphors. The first metaphor is navigating in uncharted waters. The second is the development process as a competition among the various objectives that must be satisfied, including the need to minimize costs and maximize performance. Both of these metaphors lead to the concept of 'good directions' for guiding the development process. Methods of obtaining 'good directions' are described, and fuzziness is recruited to deal with the uncertainties in the direction-finding process. The two main approaches to product development are the optimizing approach and the combinative approach. Experimental designs and genetic algorithms are suggested for efficiently investigating the design space through the correct use of models and prototypes. Experimental designs can be used as part of either the optimizing or the combinative approaches, while the genetic algorithm is more effective for the combinative approach. An innovative approach to robust design is introduced which emphasizes three-level factorial experimental designs. In addition, models of competition are examined as a means of understanding the concepts of competition which are applicable to system theory and systems engineering. Case studies describing the Manhattan Project and the Polaris Program help to illustrate the role of competition in product development.

Cited by 0

AI in control Systems engineering

D.A. Linkens · The Knowledge Engineering Review · 1990

Abstract Decision-making is an integral part of any consideration of the discipline of control Systems. This implies feedback of knowledge in some format, whether it is mediated via human or machineoriented methods. The application of AI methodologies to control Systems engineering is described in this review under two broad categories of design and implementation. The design of control strategies is itself divided into phases of modelling and simulation, identification, and algorithm selection and tuning. The aspects of implementation which are covered include real-time considerations such as knowledge-based control and fuzzy logic, multi-sensor data fusion, fault detection and Human–Computer Interaction (HCI).

Cited by 9

Scale models in control systems engineering

P.E. Wellstead · Transactions of the Institute of Measurement and Control · 1980

The article describes the role played by scale models in dynamical systems studies and control engineering. As a tangible example, the development at the Control Systems Centre, UMIST, of a series of scale models for control systems studies is described. The models form a comprehensive set of equipment which is used for laboratory investigations of process systems, material transport problems, electromechanical control, and the like. The paper describes how such a range of models is used to illustrate the essential features and methods of mathematical modelling, system identification, analysis and control

Cited by 16

Social Justice in Control Systems Engineering

Kathryn Johnson, Jon A. Leydens, Barbara Moskal et al. · 2015

Abstract Social Justice in Control Systems EngineeringThe research "Social Justice in Engineering with a Focus on Control Systems" integrates socialjustice into a control systems class and examines the impacts of such integration on studentlearning and motivation. The concept of social justice defies a universal definition, but is funda-mentally about human rights. The vision implied in social justice is that people and communitieshave the right to equality (in various senses), to health, to dignity, and to opportunities.Problem Statement Social elements such as social justice may not be perceived as“engineering” by faculty and students, leading to an engineering workforce not well-trained insocial elements of sustainable engineering solutions. Engineers work within an existing systemwithout significant effort to change the social constructs of that system [1]. Social justice isclosely tied to care for the natural environment and is a key element of sustainability. [2]However, whereas economics has long been considered an important part of engineeringeducation and the environment has gained significant ground in recent years, social justicereceives very little attention within typical engineering curricula. Therefore, most engineeringgraduates are unprepared to perform thorough assessments of the social justice implications oftheir engineering decisions and designs [2]. Our work seeks to remedy this shortcoming.Research Approach Using a mixed-methods approach, we begin by assessing the state of socialjustice awareness of the students entering the “Introduction to Feedback Control Systems”(IFCS) class. We then provide various social justice interventions to help students learn about theinherent connections between social justice inherent and control systems engineering. Ourresearch questions are: (1) What exposure to social justice concepts have students had in theiruniversity courses prior to taking IFCS?, (2) How do students report their perceptions of controlsystems classes that explicitly include engineering and social justice units compared to similarclasses that do not include social justice?, and (3) How does targeted social justice instructionimpact student performance on control systems course outcomes, student plans to pursueadditional control systems courses, and student ability to recognize the social justice dimensionsof control systems? A survey offered to all enrolled students is followed by focus groups andsemi-structured interviews for a smaller subset of students. Two sections of the course areoffered each semester, and students from both sections are invited to participate in surveys, focusgroups, and interviews. One of the two sections includes social justice interventions and the othersection follows a more traditional approach so that comparisons can be made between the two.Expected Results We will assess student learning outcomes in terms of traditional technicalcriteria and engagement. The research will help to expand understanding of social justiceelements of control systems engineering specifically and to collect baseline information of theseelectrical and mechanical engineering students’ prior experiences with engineering and socialjustice. This baseline information will provide important background for future curricularchanges required to teach social justice. Finally, this research includes an evaluation of theimpact of social justice interventions within a required control systems course. Ideally, thestudents exposed to the social justice interventions will become ambassadors for social justicewithin their future careers. We will share intervention materials with the control systems andsocial justice teaching communities, thereby increasing the opportunity for additional students tolearn about this important area.Note: This paper is one of four in the session, “Pushing the Boundaries of the Liberal Arts andEngineering: Integrating Social Justice in Engineering Science Courses”References [1] Riley, Donna, Engineering and Social Justice, San Rafael, CA: Morgan and Claypool Publishers, 2008. [2] Cech, Erin, “The (Mis)framing of social justice: Why ideologies of depoliticization and meritocracy hinder engineers’ ability to think about social injustices,” in Engineering education for social justice: Critical explorations and opportunities, (ed.) Juan Lucena, New York: Springer, pp. 67-84, 2013.

Cited by 21Open access

Genetic algorithms in control systems engineering

Chipperfield, A., Fleming, P. · ePrints Soton (University of Southampton) · 1995

Developments in computational models of evolutionary processes have led to the realization of powerful, robust, and general optimization and adaptive systems collectively called evolutionary algorithms. In this paper, we consider one member of this class of algorithms, the genetic algorithm, and describe the features and characteristics that are particularly appropriate for applications in control systems engineering. The versatility and robust qualities of the algorithm are considered and a number of application areas described. Some prospective future directions are also identified

Cited by 8

Prospects for control methods in engineering systems

V. M. Mamedov, И. А. Архаров · Refrigeration Technology · 2023

The article discusses the prerequisites and natural consequences of the control methods development in engineering systems: (1) a simple deviation and perturbation controller, (2) a fuzzy logic controller with a fuzzifier and a rule base, (3) a neural network controller for dynamically adjusting the coefficients of the corresponding links, (4) a discrete neural network controller with a neural approximator and controller. The experience gained by researchers and engineers since the first description of the principles of regulation in 1910 and the level of information technologies design, in particular the neural network method of machine learning and the enormous computing potential of computer devices, today can be integrated into a fundamentally new method of discrete neural network regulation. The review carried out in the article is aimed at identifying and demonstrating the significance of experimental and operational data, which must be properly structured and marked up at the stage of their collection and archiving. It is this approach that will allow us to quickly implement neural network controllers in engineering systems, since the most important stage for their creation is the process of learning and optimizing the architecture of neural networks. The principle of operation, advantages and disadvantages, the optimal stages of a neural network controller improvement based on two neural networks for the formation of a control strategy, taking into account the most probable state of the system at the next point in time, are given.

Cited by 0Open access

Challenges in Engineering Distributed Shipboard Control Systems

Lonnie R. Welch, Binoy Ravindran, Robert D. Harrison et al. · 1996

This paper presents a representative Navy shipboard control system and discusses its distributed implementation. The real-time, fault tolerance and scalability aspects are discussed; the system configuration is described; and the computing infrastructure is summarized. The paper also presents challenges which are likely to be faced by the engineers of the next generation of Navy surface combatants. 1. Introduction Although the end of the Cold War minimized the threat of massive open ocean naval combat, new littoral warfare missions and scenarios involving both combined arms and joint allied operations, have emerged. New threats, missions and operating environments present a greater challenge to target identification, reaction time, command and control, and tracking and weapons accuracy, requiring even more computer automation and data processing than in the past. As new systems designed to meet these challenges enter fleet service, it is increasingly difficult to engineer cost effecti...

Cited by 0

Control Engineering Approach in Safety Management Systems

Victor Girondin, Stéphane Morel · 2017

Safety Management Systems (SMS) are mainly based on an operational feedback approach for continuous safety enhancement. Closed loop approaches have been dramatically developed and applied in aeronautics by control engineers. In this article, SMS is redefined in terms of automatic control and this analogy leads to the identification of three classical feedback strategies. The theoretical effect of these strategies on performances is also discussed. As in any closed loop systems, the importance of understanding how the mission stakeholders react will be found to be particularly crucial. The last aspect of this analogy is discussed through quantitative SMS, or the standard use of SMS indicators. Several aspects of decision making and quantitative analysis are then discussed.

Cited by 0