Panagiotis D. Christofides

This book is focused on the development of rigorous, yet practical, methods for the design of advanced process control systems to improve process operational safety and cybersecurity for a wide range of nonlinear process systems. Process Operational Safety and Cybersecurity develops designs for novel model predictive control systems accounting for operational safety considerations, presents theoretical analysis on recursive feasibility and simultaneous closed-loop stability and safety, and discusses practical considerations including data-driven modeling of nonlinear processes, characterization of closed-loop stability regions and computational efficiency. The text then shifts focus to the design of integrated detection and model predictive control systems which improve process cybersecurity by efficiently detecting and mitigating the impact of intelligent cyber-attacks. The book explores several key areas relating to operational safety and cybersecurity including: machine-learning-based modeling of nonlinear dynamical systems for model predictive control;a framework for detection and resilient control of sensor cyber-attacks for nonlinear systems; insight into theoretical and practical issues associated with the design of control systems for process operational safety and cybersecurity; and a number of numerical simulations of chemical process examples and Aspen simulations of large-scale chemical process networks of industrial relevance. A basic knowledge of nonlinear system analysis, Lyapunov stability techniques, dynamic optimization, and machine-learning techniques will help readers to understand the methodologies proposed. The book is a valuable resource for academic researchers and graduate students pursuing research in this area as well as for process control engineers. Advances in Industrial Control reportsand encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

This book is focused on the development of rigorous, yet practical, methods for the design of advanced process control systems to improve process operational safety and cybersecurity for a wide range of nonlinear process systems. Process Operational Safety and Cybersecurity develops designs for novel model predictive control systems accounting for operational safety considerations, presents theoretical analysis on recursive feasibility and simultaneous closed-loop stability and safety, and discusses practical considerations including data-driven modeling of nonlinear processes, characterization of closed-loop stability regions and computational efficiency. The text then shifts focus to the design of integrated detection and model predictive control systems which improve process cybersecurity by efficiently detecting and mitigating the impact of intelligent cyber-attacks. The book explores several key areas relating to operational safety and cybersecurity including: machine-learning-based modeling of nonlinear dynamical systems for model predictive control;a framework for detection and resilient control of sensor cyber-attacks for nonlinear systems; insight into theoretical and practical issues associated with the design of control systems for process operational safety and cybersecurity; and a number of numerical simulations of chemical process examples and Aspen simulations of large-scale chemical process networks of industrial relevance. A basic knowledge of nonlinear system analysis, Lyapunov stability techniques, dynamic optimization, and machine-learning techniques will help readers to understand the methodologies proposed. The book is a valuable resource for academic researchers and graduate students pursuing research in this area as well as for process control engineers. Advances in Industrial Control reportsand encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Economic Model Predictive Control (EMPC) is a control strategy that moves process operation away from the steady-state paradigm toward a potentially time-varying operating strategy to improve process profitability. The EMPC literature is replete with evidence that this new paradigm may enhance process profits when a model of the chemical process provides a sufficiently accurate representation of the process dynamics. Systems using EMPC often neglect the dynamics associated with equipment and are often neglected when modeling a chemical process. Recent studies have shown they can significantly impact the effectiveness of an EMPC system. Concentrating on valve behavior in a chemical process, this monograph develops insights into the manner in which equipment behavior should impact the design process for EMPC and to provide a perspective on a number of open research topics in this direction. Written in tutorial style, this monograph provides the reader with a full literature review of the topic and demonstrates how these techniques can be adopted in a practical system.

This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes:Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples. The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes. In addition to being mathematically rigorous, these methods accommodate key practical issues, for example, direct optimization of process economics, time-varying economic cost functions and computational efficiency. Numerous comments and remarks providing fundamental understanding of the merging of process economics and feedback control into a single framework are included. A control engineer can easily tailor the many detailed examples of industrial relevance given within the text to a specific application. The authors present a rich collection of new research topics and references to significant recent work making Economic Model Predictive Control an important source of information and inspiration for academics and graduate students researching the area and for process engineers interested in applying its ideas.

This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes:Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples. The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes. In addition to being mathematically rigorous, these methods accommodate key practical issues, for example, direct optimization of process economics, time-varying economic cost functions and computational efficiency. Numerous comments and remarks providing fundamental understanding of the merging of process economics and feedback control into a single framework are included. A control engineer can easily tailor the many detailed examples of industrial relevance given within the text to a specific application. The authors present a rich collection of new research topics and references to significant recent work making Economic Model Predictive Control an important source of information and inspiration for academics and graduate students researching the area and for process engineers interested in applying its ideas.

Fault-Tolerant Process Control focuses on the development of general, yet practical, methods for the design of advanced fault-tolerant control systems; these ensure an efficient fault detection and a timely response to enhance fault recovery, prevent faults from propagating or developing into total failures, and reduce the risk of safety hazards. To this end, methods are presented for the design of advanced fault-tolerant control systems for chemical processes which explicitly deal with actuator/controller failures and sensor faults and data losses. Specifically, the book puts forward: · A framework for detection, isolation and diagnosis of actuator and sensor faults for nonlinear systems;· Controller reconfiguration and safe-parking-based fault-handling methodologies;· Integrated-data- and model-based fault-detection and isolation and fault-tolerant control methods;· Methods for handling sensor faults and data losses; and· Methods for monitoring the performance of low-level PID loops. The methodologies proposed employ nonlinear systems analysis, Lyapunov techniques, optimization, statistical methods and hybrid systems theory and are predicated upon the idea of integrating fault-detection, local feedback control, and supervisory control. The applicability and performance of the methods are demonstrated through a number of chemical process examples. Fault-Tolerant Process Control is a valuable resource for academic researchers, industrial practitioners as well as graduate students pursuing research in this area.

Networked and Distributed Predictive Control presents rigorous, yet practical, methods for the design of networked and distributed predictive control systems – the first book to do so. The design of model predictive control systems using Lyapunov-based techniques accounting for the influence of asynchronous and delayed measurements is followed by a treatment of networked control architecture development. This shows how networked control can augment dedicated control systems in a natural way and takes advantage of additional, potentially asynchronous and delayed measurements to maintain closed loop stability and significantly to improve closed-loop performance. The text then shifts focus to the design of distributed predictive control systems that cooperate efficiently in computing optimal manipulated input trajectories that achieve desired stability, performance and robustness specifications but spend a fraction of the time required by centralized control systems. Key features of this book include: • new techniques for networked and distributed control system design; • insight into issues associated with networked and distributed predictive control and their solution; • detailed appraisal of industrial relevance using computer simulation of nonlinear chemical process networks and wind- and solar-energy-generation systems; and • integrated exposition of novel research topics and rich resource of references to significant recent work. A full understanding of Networked and Distributed Predictive Control requires a basic knowledge of differential equations, linear and nonlinear control theory and optimization methods and the book is intended for academic researchers and graduate students studying control and for process control engineers. The constant attention to practical matters associated with implementation of the theory discussed will help each of these groups understand the application of the book’s methods in greater depth.

Fault-Tolerant Process Control focuses on the development of general, yet practical, methods for the design of advanced fault-tolerant control systems; these ensure an efficient fault detection and a timely response to enhance fault recovery, prevent faults from propagating or developing into total failures, and reduce the risk of safety hazards. To this end, methods are presented for the design of advanced fault-tolerant control systems for chemical processes which explicitly deal with actuator/controller failures and sensor faults and data losses. Specifically, the book puts forward: · A framework for detection, isolation and diagnosis of actuator and sensor faults for nonlinear systems;· Controller reconfiguration and safe-parking-based fault-handling methodologies;· Integrated-data- and model-based fault-detection and isolation and fault-tolerant control methods;· Methods for handling sensor faults and data losses; and· Methods for monitoring the performance of low-level PID loops. The methodologies proposed employ nonlinear systems analysis, Lyapunov techniques, optimization, statistical methods and hybrid systems theory and are predicated upon the idea of integrating fault-detection, local feedback control, and supervisory control. The applicability and performance of the methods are demonstrated through a number of chemical process examples. Fault-Tolerant Process Control is a valuable resource for academic researchers, industrial practitioners as well as graduate students pursuing research in this area.

The interest in control of nonlinear partial differential equation (PDE) sys­ tems has been triggered by the need to achieve tight distributed control of transport-reaction processes that exhibit highly nonlinear behavior and strong spatial variations. Drawing from recent advances in dynamics of PDE systems and nonlinear control theory, control of nonlinear PDEs has evolved into a very active research area of systems and control. This book­ the first of its kind- presents general methods for the synthesis of nonlinear and robust feedback controllers for broad classes of nonlinear PDE sys­ tems and illustrates their applications to transport-reaction processes of industrial interest. Specifically, our attention focuses on quasi-linear hyperbolic and parabolic PDE systems for which the manipulated inputs and measured and controlled outputs are distributed in space and bounded. We use geometric and Lyapunov-based control techniques to synthesize nonlinear and robust controllers that use a finite number of measurement sensors and control actuators to achieve stabilization of the closed-loop system, output track­ ing, and attenuation of the effect of model uncertainty. The controllers are successfully applied to numerous convection-reaction and diffusion-reaction processes, including a rapid thermal chemical vapor deposition reactor and a Czochralski crystal growth process. The book includes comparisons of the proposed nonlinear and robust control methods with other approaches and discussions of practical implementation issues.

Networked and Distributed Predictive Control presents rigorous, yet practical, methods for the design of networked and distributed predictive control systems – the first book to do so. The design of model predictive control systems using Lyapunov-based techniques accounting for the influence of asynchronous and delayed measurements is followed by a treatment of networked control architecture development. This shows how networked control can augment dedicated control systems in a natural way and takes advantage of additional, potentially asynchronous and delayed measurements to maintain closed loop stability and significantly to improve closed-loop performance. The text then shifts focus to the design of distributed predictive control systems that cooperate efficiently in computing optimal manipulated input trajectories that achieve desired stability, performance and robustness specifications but spend a fraction of the time required by centralized control systems. Key features of this book include: • new techniques for networked and distributed control system design; • insight into issues associated with networked and distributed predictive control and their solution; • detailed appraisal of industrial relevance using computer simulation of nonlinear chemical process networks and wind- and solar-energy-generation systems; and • integrated exposition of novel research topics and rich resource of references to significant recent work. A full understanding of Networked and Distributed Predictive Control requires a basic knowledge of differential equations, linear and nonlinear control theory and optimization methods and the book is intended for academic researchers and graduate students studying control and for process control engineers. The constant attention to practical matters associated with implementation of the theory discussed will help each of these groups understand the application of the book’s methods in greater depth.

Particulate processes are characterized by the co-presence of a contin­ uous phase and a dispersed (particulate) phase, and are widely used in industry for the manufacturing of many high-value products. Examples include the crystallization of proteins for pharmaceutical applications, the emulsion polymerization reactors for the production of latex, the aerosol synthesis of titania powder used in the production of white pig­ ments, and the thermal spray processing of nanostructured coatings. It is now well understood that the physico-chemical and mechanical properties of materials made with particulates depend heavily on the characteristics of the corresponding particle size distribution. This fact, together with recent advances in dynamics of infinite-dimensional sys­ tems and nonlinear control theory, has motivated extensive research on model-based control of particulate processes using population balances to achieve tight control of particle size distributions. This book - the first of its kind - presents general methods for the synthesis of nonlinear, robust and constrained feedback controllers for broad classes of particulate process models and illustrates their applica­ tions to industrially-important crystallization, aerosol and thermal spray processes. The controllers use a finite number of measurement sensors and control actuators to achieve stabilization of the closed-loop system, output tracking, attenuation of the effect of model uncertainty and han­ dling of actuator saturation.

This book-the first of its kind-presents general methods for feedback controller synthesis and optimization of multiscale systems, illustrating their application to thin-film growth, sputtering processes, and catalytic systems of industrial interest. The authors demonstrate the advantages of the methods presented for control and optimization through extensive simulations. Included in the work are new techniques for feedback controller design and optimization of multiscale process systems that are not included in other books. The book also contains a rich collection of new research topics and references to significant recent work.

This monograph provides insight and fundamental understanding into the feedback control of nonlinear and hybrid process systems. It presents state-of-the-art methods for the synthesis of nonlinear feedback controllers for nonlinear and hybrid systems with uncertainty, constraints and time-delays with numerous applications, especially to chemical processes. It covers both state feedback and output feedback (including state estimator design) controller designs. Control of Nonlinear and Hybrid Process Systems includes numerous comments and remarks providing insight and fundamental understanding into the feedback control of nonlinear and hybrid systems, as well as applications that demonstrate the implementation and effectiveness of the presented control methods. The book includes many detailed examples which can be easily modified by a control engineer to be tailored to a specific application. This book is useful for researchers in control systems theory, graduate students pursuing their degree in control systems and control engineers.

Particulate processes are characterized by the co-presence of a contin­ uous phase and a dispersed (particulate) phase, and are widely used in industry for the manufacturing of many high-value products. Examples include the crystallization of proteins for pharmaceutical applications, the emulsion polymerization reactors for the production of latex, the aerosol synthesis of titania powder used in the production of white pig­ ments, and the thermal spray processing of nanostructured coatings. It is now well understood that the physico-chemical and mechanical properties of materials made with particulates depend heavily on the characteristics of the corresponding particle size distribution. This fact, together with recent advances in dynamics of infinite-dimensional sys­ tems and nonlinear control theory, has motivated extensive research on model-based control of particulate processes using population balances to achieve tight control of particle size distributions. This book - the first of its kind - presents general methods for the synthesis of nonlinear, robust and constrained feedback controllers for broad classes of particulate process models and illustrates their applica­ tions to industrially-important crystallization, aerosol and thermal spray processes. The controllers use a finite number of measurement sensors and control actuators to achieve stabilization of the closed-loop system, output tracking, attenuation of the effect of model uncertainty and han­ dling of actuator saturation.

The interest in control of nonlinear partial differential equation (PDE) sys­ tems has been triggered by the need to achieve tight distributed control of transport-reaction processes that exhibit highly nonlinear behavior and strong spatial variations. Drawing from recent advances in dynamics of PDE systems and nonlinear control theory, control of nonlinear PDEs has evolved into a very active research area of systems and control. This book­ the first of its kind- presents general methods for the synthesis of nonlinear and robust feedback controllers for broad classes of nonlinear PDE sys­ tems and illustrates their applications to transport-reaction processes of industrial interest. Specifically, our attention focuses on quasi-linear hyperbolic and parabolic PDE systems for which the manipulated inputs and measured and controlled outputs are distributed in space and bounded. We use geometric and Lyapunov-based control techniques to synthesize nonlinear and robust controllers that use a finite number of measurement sensors and control actuators to achieve stabilization of the closed-loop system, output track­ ing, and attenuation of the effect of model uncertainty. The controllers are successfully applied to numerous convection-reaction and diffusion-reaction processes, including a rapid thermal chemical vapor deposition reactor and a Czochralski crystal growth process. The book includes comparisons of the proposed nonlinear and robust control methods with other approaches and discussions of practical implementation issues.