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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Wiley-IEEE Press Book Series on Control Systems Theory and Applications
- Sprache: Englisch
The Impact of Automatic Control Research on Industrial Innovation Bring together the theory and practice of control research with this innovative overview Automatic control research focuses on subjects pertaining to the theory and practice of automation science and technology subjects such as industrial automation, robotics, and human-machine interaction. With each passing year, these subjects become more relevant to researchers, policymakers, industrialists, and workers alike. The work of academic control researchers, however, is often distant from the perspectives of industry practitioners, creating the potential for insights to be lost on both sides. The Impact of Automatic Control Research on Industrial Innovation seeks to close this distance, providing an industrial perspective on the future of control research. It seeks to outline the possible and ongoing impacts of automatic control technologies across a range of industries, enabling readers to understand the connection between theory and practice. The result is a book that combines scholarly and practical understandings of industrial innovations and their possible role in building a sustainable world. The Impact of Automatic Control Research on Industrial Innovation readers will also find: * Insights on industrial and commercial applications of automatic control theory. * Detailed discussion of industrial sectors including power, automotive, production processes, and more. * An applied research roadmap for each sector. This book is a must-own for both control researchers and control engineers, in both theoretical and applied contexts, as well as for graduate or continuing education courses on control theory and practice. Editorial board: Silvia Mastellone, University of Applied Science Northwestern Switzerland; Alex van Delft, vanDelft.it, DSM; Tariq Samad, University of Minnesota; Iven Mareels, Federation University Australia, IBM; Scott Bortoff, Mitsubishi Electric Research Labs; Stefano Di Cairano, Mitsubishi Electric Research Labs; Alisa Rupenyan, ETHZ.
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Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
Dedication
About the Editors
List of Contributors
Foreword
Preface
Acknowledgments
1 Introduction
1.1 Background and Motivation
1.2 The Cradle of Innovation
1.3 Final Remarks
References
Part I: Infrastructure and Mobility
2 Data Industry
2.1 Introduction
2.2 Anatomy of a Data Center
2.3 Reducing Power in a Data Center
2.4 Codesigning Energy Assets
2.5 Beyond the Data Center
2.6 Conclusion
References
Notes
3 Building Automation
3.1 Introduction
3.2 HVAC Background
3.3 Industry Trends and Drivers of Innovation
3.4 Consequences and Implications
3.5 Industry Needs‐Driven Innovation
3.6 Vision‐Driven Innovation
3.7 Conclusions
References
Notes
4 Future Impact and Challenges of Automotive Control
4.1 Introduction
4.2 Internal Combustion Powertrain
4.3 Electrification
4.4 Driver Assistance Systems and Automated Driving
4.5 Connected and Integrated Transportation Systems
References
Part II: Energy and Production
5 Control of Electric Power Conversion Systems
5.1 Introduction
5.2 Power Electronic Conversion Systems
5.3 Grid‐Connected Power Converters
5.4 Industrial Drives
5.5 Research Roadmap
5.6 Conclusions
References
Note
6 Robotics and Manufacturing Automation
6.1 Introduction
6.2 Vision
6.3 Future Challenges and Trends
References
7 Process Industry
7.1 Introduction
7.2 Existing Control Challenges in Process Industry
7.3 Vision of the Future Generation of Products and Processes
7.4 Formulation of Control Research – Directions for Process Industry
7.5 Conclusion: What Drives and Blocks Innovation in Process Industry?
References
Index
End User License Agreement
List of Tables
Chapter 7
Table 7.1 Process Industry customer challenges and the two example processes...
Table 7.2 Control problems/research directions applied to the two example pr...
List of Illustrations
Chapter 1
Figure 1.1 Mechanical Turk or Automaton Chess Player was a fake chess‐playin...
Figure 1.2 From research to realized application, from customer needs to res...
Figure 1.3 The framework to close the gap, enabled by the cradle of innovati...
Chapter 2
Figure 2.1 Gross domestic product (GDP) per capita data as per the Maddison ...
Figure 2.2 Number of free parameters in language models over the last five y...
Figure 2.3 GFlops per Watt performance is only slowly increasing.
Figure 2.4 Managing IT workload, sleep state, active state in response to ca...
Figure 2.5 Monte Carlo optimization of asset sizing consider the dynamic int...
Chapter 3
Figure 3.1 Basic vapor compression cycle (a) with corresponding pressure‐ent...
Figure 3.2 Nonexhaustive examples of different vapor compression system arch...
Figure 3.3 Built‐up system.
Figure 3.4 A packaged unitary HVAC product.
Figure 3.5 A VRF system.
Figure 3.6 Four‐zone VRF system showing temperature sensors, indicted with d...
Figure 3.7 Multi‐row air‐source tube‐fin heat exchanger (a), and the density...
Figure 3.8 Sparsity pattern of the Jacobian (a) and Hankel singular values (...
Figure 3.9 Eigenvalues of the Jacobian for the four‐zone VRF system diagramm...
Figure 3.10 Effect of ASHRAE 90.1 on US building energy efficiency, 1970–201...
Figure 3.11 JIS B8616 minimum compressor speed test.
Figure 3.12 EU limits on F‐Gas use in terms of GWP‐equivalent of (a), and ...
Figure 3.13 Natural gas use and emissions by sector, showing buildings to be...
Figure 3.14 Mitsubishi Electric's SUSTIE building in Ofuna, Japan.
Figure 3.15 Five modes of electric demand flexibility.
Chapter 4
Figure 4.1 Evolution of NOx and particulate limits for diesel passenger cars...
Figure 4.2 Keyword co‐occurrence map for papers on diesel engines, by Aronis...
Figure 4.3 Aftertreatment configuration for next‐generation gasoline (a) and...
Figure 4.4 Decentralized connected diagnostics framework, as proposed in ASS...
Figure 4.5 Potential locations for 48V motors within a Hybrid electric vehic...
Figure 4.6 Voltage source inverters are typically used to drive three‐phase ...
Figure 4.7 Heavy‐Duty Fuel Cell Systems include multiple fuel cell stacks, h...
Figure 4.8 Autonomy levels according to SAE standard, and related vehicle fe...
Figure 4.9 Layered architecture for automated driving planning and control....
Figure 4.10 Feedback interactions between control and perception of the envi...
Figure 4.11 Schematic of deployment process steps from control research to p...
Figure 4.12 Connected and automated vehicles in a traffic environment. Sourc...
Figure 4.13 Control architecture in a connected and automated vehicle.
Figure 4.14 A mixed‐traffic environment consisting of human‐driven and conne...
Chapter 5
Figure 5.1 Examples of various applications of drive systems: power generati...
Figure 5.2 General power converter and bi‐directional power flow. Left to ri...
Figure 5.3 General two‐level converter connected to an ideal grid (G) and a ...
Figure 5.4 Complex multi‐drive industrial setup with several rotating equipm...
Figure 5.5 Abstraction of the various control layers engaged in a drive syst...
Figure 5.6 Topology controller: example of current control and modulation sc...
Figure 5.7 Control strategies for grid-connected, grid-supporting, and grid-...
Figure 5.8 Representation of the electrical grid as a stiff element as seen ...
Figure 5.9 The past and future energy systems.
Figure 5.10 Aggregate models of DOL machines and generators, and of PE conve...
Figure 5.11 Simplified aggregated model with two voltage sources to illustra...
Figure 5.12 Typical evolution of power and grid frequency as a reaction to a...
Figure 5.13 Simplified aggregate model with a voltage source representing al...
Figure 5.14 Power sharing between generators and converters and grid frequen...
Figure 5.15 Power sharing between generators and converters and grid frequen...
Figure 5.16 Power sharing between generators and converters and grid frequen...
Figure 5.17 Reduced bus voltage quality because of increased equivalent grid...
Figure 5.18 Weakly damped resonance with capacitive filter bank due to inter...
Figure 5.19 Complex driveline with two power converters and two motors conne...
Chapter 6
Figure 6.1 Levels of automation following the J3016 SAE standard on Automate...
Figure 6.2 (a) Motion planning framework for autonomous systems.(b) Prop...
Figure 6.3 Illustration of the contour error on a reference geometry.
Figure 6.4 Digital Twin‐based trajectory optimization and control loop. 1 – ...
Figure 6.5 Experimental comparison of the contour error for different feedfo...
Figure 6.6 Experimental results for applying iterative learning control with...
Chapter 7
Figure 7.1 Key figures of the process industry in The Netherlands.
Figure 7.2 Processes, Tools/systems, and People leading to Sustainable resul...
Figure 7.3 The automation pyramid.
Figure 7.4 Level of investment in technologies for production processes over...
Figure 7.5 Ranking of main drivers for automatic control in Process Industry...
Figure 7.6 Continuous plant example.
Figure 7.7 Batch plant example.
Figure 7.8 The innovation cycle.
Figure 7.9 Levels in autonomous operations (see also SAE definitions in Chap...
Guide
Cover
Table of Contents
Title Page
Copyright
Dedication
About the Editors
List of Contributors
Foreword
Preface
Acknowledgments
Begin Reading
Index
End User License Agreement
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Ahmet Murat Tekalp
The Impact of Automatic Control Research on Industrial Innovation
Enabling a Sustainable Future
Edited by
Silvia MastelloneInstitute for Electric Power SystemsUniversity of Applied Science Northwestern SwitzerlandWindisch, Switzerland
Alex van DelftVanDelft.ITSittard, The Netherlands
Copyright © 2024 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
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Library of Congress Cataloging‐in‐Publication Data
Names: Mastellone, Silvia, editor. | Delft, Alex van, editor.Title: The impact of automatic control research on industrial innovation : enabling a sustainable future / edited by Silvia Mastellone, Alex van Delft.Description: Hoboken, New Jersey : Wiley, [2024] | Includes index.Identifiers: LCCN 2023039036 (print) | LCCN 2023039037 (ebook) | ISBN 9781119983613 (hardback) | ISBN 9781119983620 (adobe pdf) | ISBN 9781119983637 (epub)Subjects: LCSH: Automatic control. | Technological innovations. | Technological innovations–Environmental aspects.Classification: LCC TJ213 .I5375 2024 (print) | LCC TJ213 (ebook) | DDC 629.8–dc23/eng/20230909LC record available at https://lccn.loc.gov/2023039036LC ebook record available at https://lccn.loc.gov/2023039037
Cover Design: WileyCover Image: © Andriy Onufriyenko/Getty Images
Silvia Mastellone: To Peter, Julian, and Lorenz, who constantly inspire me to create something beautiful.
Alex van Delft: To Cariene, for her patience, love, support, and understanding why this is important to me.
About the Editors
Silvia Mastellone is a professor of Control and Signal Processing at the University of Applied Science Northwestern Switzerland. She holds a PhD degree in Systems and Entrepreneurial Engineering from the University of Illinois at Urbana‐Champaign, a master's degree in Electrical Engineering from the University of New Mexico and a Laurea degree in Computer Engineering from the University of Rome.
She held several R&D positions across different regions at companies including Xerox, Alenia Marconi Systems, and ABB. From 2008 to 2016, she worked as a principal scientist for the ABB Corporate Research Center in Switzerland, where she led research projects and contributed to defining the research strategy in the areas of advanced control and optimization for energy systems.
Her research interests include decentralized control and estimation of networked control systems, applied to sustainable optimal operation and diagnostics for power conversion and energy systems. She served as a member of the IFAC Industry Executive Committee from 2015 to 2023.
She is a principal investigator, an executive member of the NCCR‐Automation, and a member of the advisory board for the multiutility company IBB. She currently serves as the VP of Finance for the International Federation of Automatic Control and is a member of the CSS Board of Governors.
Alex van Delft has a master's degree in engineering physics and a PhD in physics/process control and optimization from Eindhoven University of Technology. In 1989, he joined Royal DSM, The Netherlands, and held several positions in Manufacturing, Engineering and (Program) Management. He developed and applied methods to set up an automation strategy for production plants and was engaged in setting up strategic partnerships with suppliers in the field of process automation. In 2005, he moved to DSM's headquarters as Corporate Manager Process Control, a position he held until 2020, where his focus was to strengthen the role of process automation in company‐wide operational excellence programs.
In December 2020, he founded VanDelft.IT as an independent consultancy company. The technology portfolio comprises knowledge and support in the areas of process control and automation, machine learning, and data engineering, as well as endeavors to close the gap between theory and practice in process automation.
From 2010 to 2020, he served as the chairman of WIB, the Dutch‐Belgian Process Automation End‐users Association, working on application standards and practices jointly with European sister organizations in countries such as Germany, the United Kingdom, France, and Italy.
He also fulfilled board roles in measurement and control for organizations including the Royal Dutch Institution of Engineers and the Dutch Association for Post‐Academic Education, where he provided training. He has been a member of the IFAC Industry Executive Committee since 2015.
List of Contributors
Veronica AdetolaPacific Northwest National LaboratoryRichlandWAUSA
Efe C. BaltaControl and Automation Groupinspire AGZurichSwitzerland
Scott A. BortoffMitsubishi Electric Research LaboratoriesCambridgeMAUSA
Stefano Di CairanoMitsubishi Electric Research LaboratoriesCambridgeMAUSA
Amritam DasDepartment of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
Bryan EisenhowerCarrier CorporationPalm Beach GardensFLUSA
Carlos GuardiolaEscuela Técnica Superior de Ingeniería IndustrialUniversitat Politècnica de ValènciaValenciaSpain
Peter HokayemABB MotionABB Switzerland LtdTurgiSwitzerland
Pieder JoergABB MotionABB Switzerland LtdTurgiSwitzerland
Ramachandra R. KolluriIBM Consulting AustraliaIBMSouth BankVictoriaAustralia
Andreas A. MalikopoulosMechanical EngineeringUniversity of DelawareNewarkDEUSA
Iven MareelsInstitute for Innovation, Science and SustainabilityFederation University AustraliaBerwickVictoriaAustralia
Silvia MastelloneInstitute for Electric Power SystemsUniversity of Applied Science Northwest SwitzerlandWindischSwitzerland
Zheng O'NeillDepartment of Mechanical EngineeringTexas A&M UniversityCollege StationTXUSA
Alisa RupenyanZHAW Centre for AISchool of EngineeringZHAW Zurich University for Applied SciencesWinterthurSwitzerland
Mario SchweizerABB Corporate ResearchABB Switzerland LtdBaden‐DättwilSwitzerland
Jason B. SiegelMechanical EngineeringUniversity of MichiganAnn ArborMIUSA
Alex van DelftVanDelft.ITSittardThe Netherlands
Foreword
“Control is everywhere,” control engineers and scientists are fond of stating, and indeed it is. The complex engineered systems, solutions, and products, whose design, operation, maintenance – indeed, whose lifecycles – rely crucially on control technology, are virtually synonymous with what makes societies, industries, and our civilization run. The list includes aircraft and automobiles, homes and buildings, factories and process plants, biomedical devices and healthcare systems, power generators and transmission grids, and much more.
And yet, or perhaps because of this ubiquity, control is nowhere as well, in the sense that its contribution to the performance, safety, efficiency, reliability, economy, and other critical characteristics of these products and systems is hidden from view. We see a wind turbine rotating, or a rocket launch, or electricity at the outlet, or the internet at the call of a browser, and the first thought that comes to mind is not, “that's control!”
This disconnect between abstract technology and its physical manifestation extends to control technologists as well. On the exploratory end of the research, development, and engineering spectrum, we develop theories and algorithms. On the other end, our output is consumed by a specific application domain or industry subsector. Yet effective products and solutions require collaboration across the scale. Researchers need to have a deeper understanding of how, where, and why control is used. Product developers need to appreciate the relevance of broad‐based R&D. Those working in the extended middle must be able to map each end to the other. Students should be apprised of the opportunities open to them, regardless of their theoretical or practical bent. All of us, even beyond the control community, must be able to connect the dots.
The Impact of Automatic Control Research on Industrial Innovation addresses such imperatives. It introduces the “cradle of innovation” as a model for the interplay between research‐driven and market‐driven innovation, illustrating the dynamics involved with examples from numerous domains. Several industry sectors are surveyed in depth, with analyses of the state of the art, market trends, innovation drivers and obstacles, and technology roadmaps, as well as nontechnological aspects such as culture and ethics.
The seeds for this edited volume were laid in discussions in the IFAC Industry Committee on the topic of innovation in industry applications and the role of control in it. Prof. Silvia Mastellone and Dr. Alex van Delft took on the challenge of delving into this multifaceted topic. This book is the culmination of a years‐long effort by the editors, not the least of which is their recruitment of some of the top experts in the world for the industries and sectors covered.
Read the book! Regardless of your area of expertise or theory/practice alignment, if you are interested in control systems and their critical role in ensuring a sustainable future for humanity and the planet, you'll come away with a new appreciation for this ubiquitous and foundational discipline.
Tariq Samad, PhD
Technological Leadership Institute,University of Minnesota, USAFounding Chair, IFAC Industry Committee
Preface
This book is the result of a longstanding cooperation between members of the Industry Committee of the International Federation of Automatic Control. It started with the vision and ambition to address the challenge of the academic–industry gap in research and was initially discussed during a workshop at the 2017 IFAC World Congress in Toulouse, France. Subsequently, we conducted an industry‐wide survey on the topic of academic–industry cooperation, and we published the results in a Control Engineering Practice article in 2021, in which we introduced a framework for innovation in the field of automatic control. Inspired by the positive feedback received and the request for more industry‐specific insights, we decided to work on an in‐depth study of different industry sectors. We were fortunate to win the engagement of a board of main authors, both from academia and industry, who committed to work on this edited volume. Meanwhile, we organized webinars and conducted several forum sessions at conferences on the topic, which allowed us to gain more insights into the link between control research, innovation, and technology. We also realized that bridging the gap between fundamental research and application, as well as understanding and enabling an innovation process, are important topics, not only for control engineers and researchers but also for graduate students in automatic control, as they will lead future research and innovation. With this volume, we would like to offer them a vision of the possibilities for their professional paths and impact in academic and industry alike. We realized that there is an important driving force that binds us all: the current societal and environmental challenges demand to employ the best innovative technologies to encompass performance, efficiency, and reliability targets at the service of a sustainable future.
November 2023
Silvia Mastellone
Windisch, Switzerland
Alex van Delft
Sittard, The Netherlands
Acknowledgments
This edited volume would not have been possible without the joint effort and inspirational cooperation between all people, see the List of Contributors, from different industry sectors and academic backgrounds.
We would like to express our special gratitude to Tariq Samad for being the continuous driving force and challenging partner in this process; to the members of the Editorial Board for their great leadership; and to all members of the IFAC Industry Executive Committee: Kevin Brooks, Moncef Chioua, Stefano Di Cairano, Philippe Goupil, Steve Kahne, Iven Mareels, Alisa Rupenyan, and Atanas Serbezov, for the stimulating discussions that contributed to shaping the ideas behind this work.
Editorial Board: Silvia Mastellone (FHNW, University of Applied Science Northwestern Switzerland), Alex van Delft (vanDelft.IT, former DSM, The Netherlands), Tariq Samad (University of Minnesota, USA), Iven Mareels (Federation University Australia, former IBM, Australia), Scott Bortoff (Mitsubishi Electric Research Labs, USA), Stefano Di Cairano (Mitsubishi Electric Research Labs, USA), and Alisa Rupenyan (ETHZ, Switzerland).
1Introduction
Silvia Mastellone1and Alex van Delft2
1Institute for Electric Power Systems, University of Applied Science Northwestern Switzerland, Windisch, Switzerland
2VanDelft.IT, Sittard, The Netherlands
Technological innovation has shaped human lives across generations, but what are the basic forces driving the innovation process? Arguably we can state that the drive for innovation is rooted in the genuine human curiosity for knowledge, the desire to realize ambitious visions, and, at the same time, in the need for progress and comfort in our daily lives.
Automatic control, as an elegant multidisciplinary science that sets systems in motion, has enabled key steps in the history of technological innovation, from the Kalman filter that empowered humans to reach the moon, to optimal and robust controllers today pervasively present in every system and every process across industry sectors. In an environment where the complexity of engineering systems is ever‐growing and technology is developing toward more digital and data‐based solutions, automatic control is undergoing a transformation by integrating classical methods with data‐driven approaches to address the new complexity, thus opening the door to a new chapter in its history. In this context, it is valuable to identify the way automatic control can enable the next innovation steps in different industrial sectors and thus realize its full potential. To address this question from an application perspective, in [1] we proposed a framework at the interplay between incremental improvement and disruptive innovation. The framework, named the cradle of innovation, will be presented in Section 1.2 and consists of a sustainable innovation process driven by a long‐term vision and market requirements, where system know‐how, economical and technical requirements are considered to ultimately bring a brilliant idea into practice.
The work presented in this volume is part of a broader ongoing effort within the IFAC Industry Committee formed by academic and industrial members and established by IFAC in 2017 with the objective of bridging the gap between industry and academia in the field of automatic control.
Besides providing a framework for the innovation process, the scope of the paper [1] was to link automatic control research to technology innovation. Within this scope, different industrial sectors and government institutions were surveyed, the data were analyzed and translated into technical requirement specifications. Finally, the paper provided pointers to research directions that would address the sustainability challenges across industries.
Starting from this point, with the present volume, we aim to apply the framework of the cradle of innovation, expand and detail this concept across six industry sectors.
Building on this vision, in the present volume we invite the reader to join a journey toward the birth of innovation across six specific industry sectors. The journey is inspired by a story that took place in the eighteenth century; the story of the Turk [2], an eighteenth‐century automaton that could beat human chess opponents (see Figure 1.1).
The Turk first appeared in Vienna in 1770 as a chess‐playing robot dressed in Turkish clothing, seated above a cabinet with a chessboard on top. The operator would assemble a paying audience and invite a challenger to play chess. The automaton would gaze at the opponent's move, ponder, then raise its mechanical arm, and make a move. Of course, the thing was a hack – a clever magician's illusion. The only real ingenuity was a hidden chess player inside the machine.
Figure 1.1 Mechanical Turk or Automaton Chess Player was a fake chess‐playing machine constructed in the late eighteenth century.
Source: Joseph Racknitz/Humboldt University Library.
It is true that the late eighteenth century was a great age of automatons, but the deeper truth that chess‐playing was an entirely different kind of creative activity seemed as obscure to people at that time as it seems obvious to us now.
The great‐grandfather of computer science, Charles Babbage, saw the Turk and though he realized that it was probably a magic trick, he also asked himself what exactly would be required to produce an elegant solution. What kind of technology would one need to develop in order to build a machine that plays chess? And his “difference engine” – the first computer – rose in part from his desire to believe that there was a beautiful solution to the problem, even if the one before him was not.
Taking inspiration from the story of the Turk, with this volume, we ask the same question for the next generation of products, processes, and services across several industrial sectors: What does the future look like? What is beyond hacking? What would an elegant solution look like?
The volume includes six chapters and is organized into two main parts: Part I focuses on Infrastructure and Mobility and includes the following:
Data Industry
Building Automation
Automotive Control
Part II addresses Energy and Production and includes:
Power Conversion Systems
Robotics and Manufacturing Automation
Process Industry
Each chapter will discuss drivers and limits to innovation for a specific sector. Starting from customer needs and challenges, and system requirements, an applied research agenda will be formulated.
In addition to the research directions driven by industrial requirements, there are visionary ideas that promise to spark a new drive for innovation and where automatic control plays a pivotal role. Examples of such disruptive visions include the city of the future characterized by pervasive automation in the transportation (e.g. hyperloop and autonomous cars), energy (e.g. autonomous microgrids and economy), manufacturing (e.g. Industry 4.0), and financial sectors. Additionally, the adoption of control concepts in support of management decision‐making could open completely new dimensions with great benefits for both fields.
1.1 Background and Motivation
The gap between fundamental control research and practice has been addressed by several authors from different perspectives. In 1964, Axelby [3] observed that “Certainly some gap between theory and application should be maintained, for without it there would be no progress…. It appears that the problem of the gap is a control problem in itself; it must be properly identified and optimized through proper action.”
In a paper by Bennett [4], a historic overview is given of the landmark developments in automatic control. It began in the nineteenth century, when developments were mainly driven by industrial problems, e.g. the steam engine governor. Later on, the PID controller was developed by Elmer Sperry. The first theoretical analysis of a PID controller was published by Nicolas Minorsky in 1922. Another development highlighted in the paper is the feedback amplifier that enabled long‐distance telephony, combining experimental data and mathematical models. In the era of classical control theory, the focus was on the development of rigorous mathematical foundations. Later on, the development was driven and sponsored by aerospace and defense, and the advancements in computing power allowed to solve more complex problems.
Rosenbrock, in his work [5], addresses the dilemma of whether automatic control should further develop toward fundamental theory backed up by rigorous mathematics or engineering more centered around experience and intuition. He points toward future developments where computers enhance the human skills rather than replace them.
Aström and Kumar [6] describe the dynamic gap between theory and practice as rooted in the open‐loop process of theoretical research without feedback from practice. With current technology, deployment and implementation of complex control solutions have become simpler, thus reducing the gap between theory and application.
Lamnabhi‐Lagarrigue et al. [7] build on this analysis and bring it a step further by describing the cross‐fertilization and bi‐directional interplay between five critical societal challenges (transportation, energy, water, healthcare, and manufacturing) and seven research and innovation challenges (cyber‐physical systems of systems, distributed networked control systems, autonomy, cognition and control, data‐driven dynamic modeling and control, cyber‐physical and human systems, complexity and control in networks, and critical infrastructure systems). The main recommendation from their analysis is the fostering of both fundamental and application‐oriented research in sector‐specific programs and in ICT as a program that provides enabling technologies for all sectors.
In the paper by Deng [8], the author provides an overview on developments and application areas in automatic control that are driven by societal challenges such as food production, land use, water, logistics, and e‐health.
In his 2020 editorial, Grimble [9] establishes a concise link between historical developments in automatic control and the need for a broader, systems‐engineering‐driven approach.
In summary, the evolution of automatic control has been driven so far by industry, the requirements for theoretically rigorous foundations, aerospace, defense, and the need to address various societal challenges.
In this volume, we aim to further establish control as a discipline that enables innovation in technology by analyzing the innovation dynamics in more detail for specific industry sectors. We introduce a cyclic process for innovation based on [1], where ideas evolve through various stages of selection and transformation and are finally brought to life. Within this process, we identify barriers, enablers, and key drivers for the process in various industry sectors, then through a thorough analysis, those drivers are linked to system requirement specifications and finally to a control research framework or roadmap.
1.2 The Cradle of Innovation
1.2.1 A Framework for Innovation
To establish an innovation enabling framework, it is required to identify factors that affect innovation. To this end, we consider two innovation processes depicted in Figure 1.2. The first process, referred to as research‐driven innovation, starts from an abstract idea, a theoretical concept, that is transformed and finally realized in an application (product, process, or service). The second process, referred to as market‐driven innovation, starts from customer requirements that define concrete required technology developments and leading to a research portfolio.
Figure 1.2 From research to realized application, from customer needs to research focus.
In the first process, “from research to realized application,” a preliminary idea is proposed without considering technical feasibility and financial benefits. The idea is then developed and matured through different stages to be finally implemented in a product or process. At each stage, the idea undergoes a transformation and often does not survive the feasibility and profitability tests that are posed at each stage.
In the process “from customer needs to research focus,” the starting point is the customer intended as the end user of a specific technology, the market, and in a broader sense, society and its needs. The customer might not have know‐how about the technology, but he or she can provide user requirement specifications for a product or process, that is, what are concrete characteristics that he or she would like to see in the product. Those specifications are then translated into product requirement specifications and finally into technical requirement specifications.
In both approaches, once a vision of the next generation of product, processes, or services is formed, the next step is the identification of the key challenges toward the realization of the vision.
The flow in the cyclic innovation process described in Figure 1.2 is catalyzed by systematically translating customer requirements into technical requirements and finally populating the research portfolio. Similarly, an idea is matured through a multi-stage transformation process, where profitability and feasibility criteria are considered while shaping the idea from one stage to the next, until its realization into practice. This requires properly balancing the research agenda so as to include fundamental and implementation aspects.
Vision‐driven innovation tools, like design thinking, but also agile and scrum methods, serve to increase the effectiveness and speed of the idea transformation process at each stage.
Figure 1.3 The framework to close the gap, enabled by the cradle of innovation.
In both processes, we can additionally characterize innovation as disruptive or incremental. Disruptive innovation is mostly guided by a long‐term vision that looks beyond the existing technology, and it is typically accompanied by larger risks. Examples of such disruptive innovations are the touch screen (invented first by IBM but really made disruptive by Apple's Steve Jobs) and the Solar‐X program.
Incremental innovation is characterized by smaller improvements in the current technology as it takes into account the constraints and limitations in implementing the innovation, and it is a structured process and requires analysis of each step. It is, however, limited in its possibility to accommodate substantial innovation.
In the case of incremental innovation, the probability of successfully driving an idea into the market is estimated to be up to 60–75% for an innovation using existing technology in the company and intended for the company's current market, see [10]. This success rate decreases significantly to 5–25% for “out of the box” innovation.
Disruptive innovation is such a rare stone and without proper grounding in the majority of cases, the initial idea dies at some point between the vision and the implementation phase. On the other hand, the incremental innovation without a long‐term vision can bring a technology to complete alienation as nonproperly planned incremental steps will accumulate creating an unmanageable complexity.
Combining an incremental innovation with the vision of a long‐term solution can lead to a sustainable and rich process that allows for the realization of a minimum viable product that can accommodate subsequent innovation steps. Starting from the two innovation processes depicted in Figure 1.2, the cradle of innovation offers the means to link the two in a circular process and activate the flow as depicted in Figure 1.3.
Disruptive innovation or vision‐driven innovation is rare in most industry sectors due to the high risks that it entails. Typically, the most disruptive innovative sectors are those related to consumer products where there is enough demand for novelty and less for reliability. The trade‐off between innovation and reliability seems to often require compromises, one interesting exception in the automotive sector is Tesla, where high demand drives disruptive innovation but also addresses safety requirements.
The literature on innovation processes is widely dominated by vision‐driven innovation often referred to as design‐driven innovation, where the concept of design thinking, as explained by T. Brown [11], with its focus on creativity and experimentation, plays a fundamental role; see [5]. Often those approaches to innovation begin with a brainstorming phase based on the dream question: imagining to wake up five years from now and all of industry and societal problems have been solved; how does this vision of the future look like? Some examples of those visionary ideas are: man to the moon, iPhone, touch screen, bullet trains, and flying reconfigurable cars running on solar energy. Realizing such visions will require an extensive combined effort from several interdisciplinary fields, from fundamental to applied results.
1.2.2 What Drives Innovation?
The probability to successfully introduce a new technology in the market is correlated to the measure in which the technology meets customer requirements at affordable time and cost. This principle is reflected in a standard product development process, where the customers are surveyed about the limitations of the current product and the desired features for the next generation. Based on those inputs, product requirement specifications are defined. In the second stage, those requirements will be translated into technical system requirement specifications by asking the critical question: what would it take to make it happen? Here a combination of creativity and technical know-how is required to understand possibilities and limitations.
Some key drivers for the next‐generation technology that have been identified across industry sectors are: cost, time to market, energy, efficiency, process availability, performance, quality, reduction of variability, throughput, yield, sustainability footprint, and digitalization.
Different sectors exhibit specific innovation drivers related to the nature of their business, some examples of key differentiating factors are: B2C versus B2B business, market and business size, competitive versus niche markets and businesses, with or without safety requirements. Those factors determine to a large extent the dominance of one or more drivers. Interesting differences across sectors are the focus on quality and reliability, for example, in the aerospace sector, where factors such as safety and human psychology play a dominant role. In sectors where safety does not play a dominant role cost and time to market tend to be key drivers. This is typically characterized by sectors that focus on consumer products but not exclusively.
Other interesting differences can be observed in robotics, with the main focus on productivity, and IT, with a focus on time to market; as typical consumer product businesses, the high competitiveness requires agile development. In the energy, oil and gas sector, cost, and reliability play a dominant role in addition to availability. In some applications, the optimality of the process performance is secondary with respect to the process availability. As an example, for a power converter driving a gas pipe, every hour of inactivity leads to major losses or blackouts in an electric grid. For the process industry, cost and quality dominate the scene, here, the proximity to consumer product defines the high priority of quality. The drivers presented here provide a lighthouse to identify the direction of the research effort; the next step is to determine the path to reach this goal and specifically identify what are the obstacles in the way.
1.2.3 Challenges Toward Enabling Innovation
Identifying innovation drivers contributes to shaping a vision and defining a direction for technological innovation. The next step is the identification of the obstacles toward the realization of the vision. From the survey results reported in [1], the following limiting factors related to technology have been identified across different industrial sectors: abundance of data – but limited contextualization, data acquisition from the field and data reliability, design and development time, agile approach, complexity of system and solution, solution integration within the full process or product, security, and cost. Additional context‐based points have to be considered that are not directly related to technology, but represent obstacles toward establishing the innovation processes. Some examples are: maturity of the industry and its adaptation to the deployment of new technology, training of developers and operators, legacy processes, change management, open platforms across vendors, IT, human factors, and market acceptance.
Similarly, we can identify innovation enablers that are beyond technology and related to societal factors. Starting from the education system, we may ask whether we are shaping the new generation to be free thinkers and innovators and whether we are offering stimulating study and work environments. To innovate requires thinking out of the box, exploring nontrivial directions as well as a comprehensive system understanding and knowledge of the process through which an idea is implemented in a product.
Business and industry broadcast that future‐ready employees need to have multiple areas of expertise or at least appreciate how a range of skills fit together. Grimble [9] especially highlights the need for control engineers to have additional skill sets, including broader system understanding, implementation aspects, application knowledge, and economic aspects, to identify potential and limitation.
Additionally, a greater need for the education system has been recognized in order to integrate science, technology, engineering, and maths (STEM) concepts with the arts (STEAM) across the wider curriculum. Control design is also an “art” [5]. Human minds excel in pattern recognition, assessment of complicated situations and have an intuitive leap toward new solutions. Those skills should be cultivated in young innovators. As for the work environment, as argued in the Free innovation paradigm [12], companies like Google have been experimenting with ideal environments for creation, with large spaces for thinking, discussing, and generating ideas. But there is more when it goes to motivation and creation. A series of studies on work motivation carried out at MIT, and summarized in [13], describes the intrinsic nature of human motivation, highlighting the main aspects that drive sustainable motivation: autonomy, mastery, and purpose. The author argues against old models of motivation driven by rewards and fear of punishment, dominated by extrinsic factors such as monetary reward. Finally, the drive for innovation does not stop at the formulation of an idea, the knowledge, and capability to bring the idea into the real world requires the alignment of economic and technical requirements. This process can be simplified if the idea was originally conceived with the techno‐economical aspects of the end product.
1.3 Final Remarks
With this volume, we offer an industrial perspective on the future of control research, highlighting its impact on technological innovation and opportunity for technology transfer. The main scope is to create a bridge between the control research community and the various industry sectors.
The volume is dedicated to three main groups: (i) the scientific and technical control community, including researchers and control engineers in academic, government, and industrial institutions. For this group, the volume offers a possible research agenda leading to sustainable technological innovation. (ii) Industry representatives: product managers, project managers, and business owners who are aware of the key innovation steps required in their specific fields. This group has a vision for the future product/process/service and wants to learn how it can be enabled. (iii) Academics that use the volume as reference material for graduate courses or continuing education, e.g. graduate course: “Control practice and its impact in the future of industry.” The volume provides students with links between theory and practice and insights into the various industry sectors where control can enable technological innovation.
Finally, the next chapter in the history of technological advancement has to consider the reality of limited natural resources. A large portion of the industry will focus in the next 10–20 years mainly on moving from fossil fuels to electricity (energy transition) and further reducing the ecological footprint, according to the UN's sustainable development goals. But energy is only one of several limited resources we rely on, water, mineral, energy, and biological resources will pose our next challenge.
Performance and efficiency can no longer be the only criteria considered for innovation. Sustainability has to become part of our objectives, constraints, incentives, and decision making when we engineer new solutions.
Automatic control, as a rigorous discipline that connects the foundation of elegant mathematics with the application aspects of engineering, has a pivotal role in orchestrating the multidisciplinary group to address the societal and technological challenges for a sustainable future.
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Part IInfrastructure and Mobility
2Data Industry
Amritam Das1, Ramachandra R. Kolluri2, and Iven Mareels3
1Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
2IBM Consulting Australia, IBM, South Bank, Victoria, Australia
3Institute for Innovation, Science and Sustainability, Federation University Australia, Berwick, Victoria, Australia
2.1 Introduction
This chapter deals with the emerging platform industries that support and enable the digitization revolution,1 with a particular attention to address its contribution to enabling a sustainable future, without losing sight of its own significant ecological footprint.
