Bøger i Wiley-IEEE Press Book Series on Control Systems Theory and Applications serien

An all-in-one resource for designing and implementing embedded control in mobile roboticsIn 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 stabilityPerfect 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.

Merging Optimization and Control in Power SystemsA novel exploration of distributed control in power systems with insightful discussions of physical and cyber restrictionsIn 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 modelPerfect 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.

Dynamic System Modeling & Analysis with MATLAB & PythonA robust introduction to the advanced programming techniques and skills needed for control engineeringIn 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 implementationDynamic 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.