Jinfeng Liu – författare

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.

Advanced methodologies in machine learning, optimal control, and agricultural water management to address irrigation scheduling in large-scale agriculture Through a multidisciplinary approach, Precision Irrigation for Agriculture presents rigorous and practical methods that integrate machine learning, optimal control, and agricultural water management to design irrigation schedulers tailored for large-scale agricultural fields. The book includes case studies and comparative studies, bridging the gap between theory and real-world application. The book begins with a thorough review of existing irrigation scheduling practices and recent advancements in the field, then proceeds to examine the application of machine learning methods and optimal control strategies to address various challenges in irrigation scheduling. The central focus of the book is the development of a novel irrigation scheduler. This novel scheduler unifies model predictive control with three machine learning paradigms—supervised, unsupervised, and reinforcement learning—into a cohesive framework specifically designed for the daily irrigation scheduling problem in large-scale agricultural fields. The book also presents a computationally efficient methodology that leverages remote sensing observations to estimate soil moisture content and soil hydraulic parameters, which are key elements in the design of precise irrigation schedulers. Written by a team of qualified academics, Precision Irrigation for Agriculture includes information on: Soil moisture modeling, including water content, energy status of soil water, the soil water retention curve, Darcy’s law, and the Richards’ equationModel predictive control and its application in irrigation scheduling, covering problem formulation, feasibility, solution techniques, and controller tuningParameter selection and state estimation, including sensitivity analysis for parameter identifiability, the orthogonal projection method for parameter selection, and extended Kalman filter for simultaneous state and parameter estimationMulti-agent reinforcement learning for irrigation scheduling, including the integration of decentralized actor–critic agents, the limiting management zone concept, and model predictive control (MPC) to form a multi-agent MPC paradigm for irrigation scheduling; a semi-centralized multi-agent reinforcement learning framework to further refine irrigation timing decisions; and agent design, testing, and comparative studies against traditional irrigation scheduling schemes.Precision Irrigation for Agriculture is a valuable resource for researchers in process control and irrigation management, irrigation practitioners, and students of agriculture, water management, machine learning, and optimal control.

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.

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.

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; andEMPC 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.