IEEE Press Series on Control Systems Theory and Applications

A fulsome and robust presentation of disturbance observers complete with MATLAB sample programs and simulation results. In “Disturbance Observer for Advanced Motion Control with MATLAB/Simulink”, distinguished electronics engineer Dr. Akira Shimada delivers a comprehensive exploration of the suppression of actual and unknown disturbances. In the book, you'll find a systematic discussion of the basic theory and design methods of disturbance observers accompanied by instructive MATLAB and Simulink simulation examples. Included appendices cover the mathematical background of classical, modern, and digital control and ground the reader's understanding of the more advanced sections. The included material is ideal for students enrolled in courses in advanced motion control, mechatronics system control, electrical drives, motion control, robotics, and aeronautics. In addition to topics like model predictive control, vibration systems, acceleration control, adaptive observers, and multi-rate sampling, readers will find: • A thorough introduction to the various types of disturbance observers and the fundamentals of disturbance observers, including disturbance estimation and disturbance rejection • Comprehensive explorations of stabilized control and coprime factorization, including the derivation of stabilizing controllers • Practical discussions of disturbance observers in state space, including identity input disturbance observers and identity reaction force observers • Fulsome treatments of the mathematical foundations of control theory, methods??for measuring and estimating velocities, and the disturbance estimation Kalman filter Perfect for undergraduate and graduate students with existing knowledge of the fundamentals of control engineering who wish to learn how to design disturbance observers, “Disturbance Observer for Advanced Motion Control with MATLAB/Simulink” will also benefit professional engineers and researchers studying alternative control theories.

Discover the latest research in path planning and robust path tracking control. In Autonomous Road Vehicle Path Planning and Tracking Control, a team of distinguished researchers delivers a practical and insightful exploration of how to design robust path tracking control. The authors include easy to understand concepts that are immediately applicable to the work of practicing control engineers and graduate students working in autonomous driving applications. Controller parameters are presented graphically, and regions of guaranteed performance are simple to visualize and understand. The book discusses the limits of performance, as well as hardware-in-the-loop simulation and experimental results that are implementable in real-time. Concepts of collision and avoidance are explained within the same framework and a strong focus on the robustness of the introduced tracking controllers is maintained throughout. In addition to a continuous treatment of complex planning and control in one relevant application, the Autonomous Road Vehicle Path Planning and Tracking Control includes: • A thorough introduction to path planning and robust path tracking control for autonomous road vehicles, as well as a literature review with key papers and recent developments in the area • Comprehensive explorations of vehicle, path, and path tracking models, model-in-the-loop simulation models, and hardware-in-the-loop models • Practical discussions of path generation and path modeling available in current literature • In-depth examinations of collision free path planning and collision avoidance Perfect for advanced undergraduate and graduate students with an interest in autonomous vehicles, “Autonomous Road Vehicle Path Planning and Tracking Control” is also an indispensable reference for practicing engineers working in autonomous driving technologies and the mobility groups and sections of automotive OEMs.

A novel exploration of distributed control in power systems with insightful discussions of physical and cyber restrictions In “Merging Optimization and Control in Power Systems” an accomplished team of engineers deliver a comprehensive introduction to distributed optimal control in power systems. The book re-imagines control design within the framework of cyber-physical systems with restrictions in both the physical and cyber spaces, addressing operational constraints, non-smooth objective functions, rapid power fluctuations caused by renewable generations, partial control coverage, communication delays, and non-identical sampling rates. This book bridges the gap between optimization and control in two ways. First, optimization-based feedback control is explored. The authors describe feedback controllers which automatically drive system states asymptotically to specific, desired optimal working points. Second, the book discusses feedback-based optimization. Leveraging the philosophy of feedback control, the authors envision the online solving of complicated optimization and control problems of power systems to adapt to time-varying environments. Readers will also find: • A thorough argument against the traditional and centralized hierarchy of power system control in favor of the merged approach described in the book • Comprehensive explorations of the fundamental changes gripping the power system today, including the increasing penetration of renewable and distributed generation, the proliferation of electric vehicles, and increases in load demand • Data, tables, illustrations, and case studies covering realistic power systems and experiments • In-depth examinations of physical and cyber restrictions, as well as the robustness and adaptability of the proposed model Perfect for postgraduate students and researchers with the prerequisite knowledge of power system analysis, operation, and dynamics, convex optimization theory, and control theory, “Merging Optimization and Control in Power Systems” is an advanced and timely treatment of distributed optimal controller design.

An all-in-one resource for designing and implementing embedded control in mobile robotics. In “Embedded Control for Mobile Robotic Applications”, a distinguished trio of researchers delivers an authoritative and fulsome resource for understanding embedded control and robotics. The book includes coverage of a variety of embedded platforms, their use in controller implementation, stability analyses of designed controllers, and two new approaches for designing embedded controllers. The authors offer a full chapter on Field-Programmable-Gate-Array (FPGA) architecture development for controller design that is perfect for both practitioners and students taking robotics courses and provide a companion website that includes MATLAB codes for simulation and embedded platform-specific code for mobile robotic applications (in Embedded C and Verilog). The two approaches discussed by the authors-the top-down methodology and the bottom-up methodology-are of immediate practical utility to both practicing professionals in the field and students studying control applications and mobile robotics. The book also offers: • A thorough introduction to embedded control, including processor, IC, and design technology, as well as a discussion of limitations in embedded control design • Comprehensive explorations of the bottom-up and top-down methods, including computations using CORDIC, interval arithmetic, sliding surface design, and switched nonlinear systems • Practical discussions of generic FPGA architecture design, including Verilog, PID controllers, DC motors and Encoder, and a systematic approach for designing architecture using FSMD • In-depth examinations of discrete-time controller design, including the approximation to discrete-time transfer function and embedded implementation stability Perfect for practitioners working in embedded control design and control applications in robotics, “Embedded Control for Mobile Robotic Applications” will also earn a place in the libraries of academicians, researchers, senior undergraduate students, and graduate students in these fields.

Explore a comprehensive and practical approach to reinforcement learning. Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory-optimal control and dynamic programming—or on algorithms-most of which are simulation-based. “Model-Based Reinforcement Learning” provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning control. In doing so, the authors seek to develop a model-based framework for data-driven control that bridges the topics of systems identification from data, model-based reinforcement learning, and optimal control, as well as the applications of each. This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end framework-from design to application-of a more tractable model-based reinforcement learning technique. “Model-Based Reinforcement Learning” readers will also find: • A useful textbook to use in graduate courses on data-driven and learning-based control that emphasizes modeling and control of dynamical systems from data • Detailed comparisons of the impact of different techniques, such as basic linear quadratic controller, learning-based model predictive control, model-free reinforcement learning, and structured online learning • Applications and case studies on ground vehicles with nonholonomic dynamics and another on quadrator helicopters • An online, Python-based toolbox that accompanies the contents covered in the book, as well as the necessary code and data “Model-Based Reinforcement Learning” is a useful reference for senior undergraduate students, graduate students, research assistants, professors, process control engineers, and roboticists.

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.

Unique resource presenting advanced nonlinear control methods for power converters, plus simulation, controller design, analyses, and case studies. “Advanced Control of Power Converters” equips readers with the latest knowledge of three control methods developed for power converters: nonlinear control methods such as sliding mode control, Lyapunov-function-based control, and model predictive control. Readers will learn about the design of each control method, and simulation case studies and results will be presented and discussed to point out the behavior of each control method in different applications. In this way, readers wishing to learn these control methods can gain insight on how to design and simulate each control method easily. The book is organized into three clear sections: introduction of classical and advanced control methods, design of advanced control methods, and case studies. Each control method is supported by simulation examples along with Simulink models which are provided on a separate website. Contributed to by five highly qualified authors, “Advanced Control of Power Converters” covers sample topics such as: • Mathematical modeling of single-and three-phase grid-connected inverter with LCL filter, three-phase dynamic voltage restorer, design of sliding mode control and switching frequency computation under single-and double-band hysteresis modulations • Modeling of single-phase UPS inverter and three-phase rectifier and their Lyapunov-function-based control design for global stability assurance • Design of model predictive control for single-phase T-type rectifier, three-phase shunt active power filter, three-phase quasi-Z-source inverter, three-phase rectifier, distributed generation inverters in islanded ac microgrids • How to realize the Simulink models in sliding mode control, Lyapunov-function-based control and model predictive control • How to build and run a real-time model as well as rapid prototyping of power converter by using OPAL-RT simulator Advanced Control of Power Converters is an ideal resource on the subject for researchers, engineering professionals, and undergraduate/graduate students in electrical engineering and mechatronics; as an advanced level book, and it is expected that readers will have prior knowledge of power converters and control systems.

Sensorless Control of Permanent Magnet Synchronous Machine Drives A comprehensive resource providing basic principles and state-of-the art developments in sensorless control technologies for permanent magnet synchronous machine drives Sensorless Control of Permanent Magnet Synchronous Machine Drives highlights the global research achievements over the last three decades and the sensorless techniques developed by the authors and their colleagues, and covers sensorless control techniques of permanent magnet machines, discussing issues and solutions. Many worked application examples are included to aid in practical understanding of concepts. Written by pioneering authors in the field, Sensorless Control of Permanent Magnet Synchronous Machine Drives covers topics such as: • Permanent magnet brushless AC and DC drives • Single three-phase, dual three-phase, and open winding machines • Modern control theory based sensorless methods, covering model reference adaptive system, sliding mode observer, extended Kalman filter, and model predictive control • Flux-linkage and back-EMF based methods for non-salient machines, and active flux-linkage and extended back-EMF methods for salient machines • Pulsating and rotating high frequency sinusoidal and square wave signal injection methods with current or voltage response, at different reference frames, and selection of amplitude and frequency for injection signal • Sensorless control techniques based on detecting third harmonic or zero-crossings of back-EMF waveforms • Parasitic effects in fundamental and high frequency models, impacts on position estimation and compensation schemes, covering cross-coupling magnetic saturation, load effect, machine saliency and multiple saliencies Describing basic principles, examples, challenges, and practical solutions, Sensorless Control of Permanent Magnet Synchronous Machine Drives is a highly comprehensive resource on the subject for professionals working on electrical machines and drives, particularly permanent magnet machines, and researchers working on electric vehicles, wind power generators, household appliances, and industrial automation.

Advanced and systematic examination of the design and analysis of networked control systems and multi-agent systems “Control Over Communication Networks” provides a systematic and nearly self-contained description of the analysis and design of networked control systems (NCSs) and multi-agent systems (MASs) over imperfect communication networks, with a primary focus on fading channels and delayed channels. The text characterizes the effect of communication channels on the stability and performance of NCSs, and further studies the joint impact of communication channels and network topology on the consensus of MASs. By integrating communication and control theory, the four highly-qualified authors present fundamental results concerning the stabilization of NCSs over power-constrained fading channels and Gaussian finite-state Markov channels, linear-quadratic optimal control of NCSs with random input gains, optimal state estimation with intermittent observations, consensus of MASs with communication delay and packet dropouts, and synchronization of delayed Vicsek models. Simulation results are given in each chapter to demonstrate the developed analysis and synthesis approaches. The references are comprehensive and up-to-date, enabling further study for readers. Topics covered in Control Over Communication Networks include: • Basic foundational knowledge, including control theory, communication theory, and graph theory, to enable readers to understand more complex topics • The stabilization, optimal control, and remote state estimation problems of linear systems over channels with fading, signal-to-noise constraints, or intermittent measurements • Consensus problems of MASs over fading/delayed channels, with directed and undirected communication graphs “Control Over Communication Networks” provides a valuable unified platform for understanding the analysis and design of NCSs and MASs for researchers, control engineers working on control systems over communication networks, and mechanical engineers working on unmanned systems. Preliminary knowledge of linear system theory and matrix analysis is required.

Dynamic System Modeling & Analysis with MATLAB & Python A robust introduction to the advanced programming techniques and skills needed for control engineering. In “Dynamic System Modeling & Analysis with MATLAB & Python: For Control Engineers”, accomplished control engineer Dr. Jongrae Kim delivers an insightful and concise introduction to the advanced programming skills required by control engineers. The book discusses dynamic systems used by satellites, aircraft, autonomous robots, and biomolecular networks. Throughout the text, MATLAB and Python are used to consider various dynamic modeling theories and examples. The author covers a range of control topics, including attitude dynamics, attitude kinematics, autonomous vehicles, systems biology, optimal estimation, robustness analysis, and stochastic system. An accompanying website includes a solutions manual as well as MATLAB and Python example code. “Dynamic System Modeling & Analysis with MATLAB & Python: For Control Engineers” provides readers with a sound starting point to learning programming in the engineering or biology domains. It also offers: • A thorough introduction to attitude estimation and control, including attitude kinematics and sensors and extended Kalman filters for attitude estimation • Practical discussions of autonomous vehicles mission planning, including unmanned aerial vehicle path planning and moving target tracking • Comprehensive explorations of biological network modeling, including bio-molecular networks and stochastic modeling • In-depth examinations of control algorithms using biomolecular networks, including implementation “Dynamic System Modeling & Analysis with MATLAB & Python: For Control Engineers” is an indispensable resource for advanced undergraduate and graduate students seeking practical programming instruction for dynamic system modeling and analysis using control theory.

Interval Analysis An innovative and unique application of interval analysis to optimal control problems In Interval Analysis: Application in the Optimal Control Problems, celebrated researcher and engineer Dr. Navid Razmjooy delivers an expert discussion of the uncertainties in the analysis of optimal control problems. In the book, Dr. Razmjooy uses an open-ended approach to solving optimal control problems with indefinite intervals. Utilizing an extended, Runge-Kutta method, the author demonstrates how to accelerate its speed with the piecewise function. You'll find recursive methods used to achieve more compact answers, as well as how to solve optimal control problems using the interval Chebyshev's function. The book also contains: • A thorough introduction to common errors and mistakes, generating uncertainties in physical models • Comprehensive explorations of the literature on the subject, including Hukurara's derivatives • Practical discussions of the interval analysis and its variants, including the classical (Minkowski) methods • Complete treatments of existing control methods, including classic, conventional advanced, and robust control. Perfect for master's and PhD students working on system uncertainties, Interval Analysis: Application in the Optimal Control Problems will also benefit researchers working in laboratories, universities, and research centers.