Following his book on the origin of control engineering (1800-1930) the author now traces development through the critical period 1930-1955, widely identified as the period of 'classical' control theory. In the 1930s basic automatic control devices were developed and used in process industries, as were servos for the control of aircraft and ships and amplifiers for the telephone system and early computers etc.

This book is about prediction and control of processes which can be expressed by discrete-time models (i.e. the characteristics vary in some way with time). The aim of the book is to provide a unified and comprehensive coverage of the principles, perspectives and methods of adaptive prediction, which is used by scientists and researchers in a wide variety of disciplines.

This book provides a unified, practically-oriented treatment to many constrained control paradigms. Recently proposed control strategies are unified in a generalised framework to deal with different kinds of constraints. The book's solutions are based on reference conditioning ideas implemented by means of supervisory loops, and they are complementary to any other control technique used for the main control loop. Although design simplicity is a book priority, the use of well established sliding mode concepts for theoretical analysis make it also rigorous and self-contained. The first part of the book focuses on providing a simple description of the method to deal with system constraints in SISO systems. It also illustrates the design and implementation of the developed techniques through several case studies. The second part is devoted to multivariable constrained control problems: improving system decoupling under different plant or controller constraints, and reducing the undesired effects caused by manual-automatic or controller switching. The key aim of this book is to reduce the gap between the available constrained control literature and industrial applications.

This book has been inspired by the portfolio of recent scientific outputs from a range of European and national research initiatives in cognitive science. It presents an overview of recent developments in cognition research and unites the various emerging research strands within a single text as a reference point for progress in the subject. It also provides guidance for new researchers on the breadth of the field and the interconnections between the various research strands identified here. Advances in Cognitive Systems brings together a wide range of material from leading workers in the field as well as the outputs from research groups around the world, covering the two principal cognition paradigms of cognitivism and emergence. Furthermore, it suggests some interesting lines of thought about the challenges and opportunities for future work in the field and for promoting the various research agendas highlighted within. This compilation will be of interest not only to those working in the fields of computer science and AI but also to psychologists, neural scientists, researchers in information science, and engineers involved in the development of advanced robotics, mechatronic systems and HCI systems.

Fractional control techniques provide an effective way to control dynamic behaviours, using fractional differential equations. This can include the control of fractional plants, the control of a plant using a fractional controller, or the control of a plant so that the controlled system will have a fractional behaviour to achieve a performance that would otherwise be hard to come by. An Introduction to Fractional Control outlines the theory, techniques and applications of fractional control. After an initial introduction to fractional calculus, the book explores many fractional control systems including fractional lead control, fractional lag control, first, second and third generation Crone control, fractional PID, PI and PD control, fractional sliding mode control, logarithmic phase Crone control, fractional reset control, fractional H₂ and H_∞ control, fractional predictive control, and fractional time-varying control. Each chapter contains solved examples and references for further reading. Common definitions and proofs are included, along with a discussion of how MATLAB can be used to assist in the design and implementation of fractional control. This book is an essential guide for researchers and advanced students of control engineering in academia and industry.

Dimensional analysis is an engineering tool that is widely applied to numerous engineering problems, but has only recently been applied to control theory and problems such as identification and model reduction, robust control, adaptive control, and PID control. Application of Dimensional Analysis in Systems Modeling and Control Design provides an introduction to the fundamentals of dimensional analysis for control engineers, and shows how they can exploit the benefits of the technique to theoretical and practical control problems. Topics covered include dimensional analysis and dimensional similarity, dynamical systems dimensionless representation, dimensionless systems identification and model order reduction, homogeneity of PID tuning rules, dimensionless PID tuning rules comparison, dimensional analysis control fundamentals, control of dimensionally similar systems, and adaptive control in the presence of input saturation.

Cognitive sensing systems combined with IoTs and smart technologies are used in countless applications such as industrial robotics, computer-aided diagnosis, brain-computer interface (BCI), human-computer interaction (HCI), telemedicine, driverless cars and smart energy systems.

With contributions from an international team of experts from a wide range of research areas including sensing, computer vision, signal processing and device and control applications, this book highlights the emerging role of cognitive sensors in a growing number of real time applications including smart health, smart cities, smart transportation and smart agriculture.

The volume will be suitable for a broad audience of researchers in the fields of smart sensing, signal processing, automation and robotics, environmental engineering, energy engineering, biomedical engineering and allied disciplines where smart sensors are part of the curriculum.

State space analysis of systems. Modal control. Quadratic optimal control. Design of observers. Other selected design methods in the state space. Frequency domain analysis of multivariable systems. The Inverse Nyquist array method. The characteristic locus method. Frequency domain design by factorisation methods. Selected stochastic problems.

Using clear tutorial examples, this fully updated new edition concentrates on explaining and illustrating the concepts that are at the heart of control theory. It seeks to develop a robust understanding of the underlying principles around which the control subject is built. This simple framework is studded with references to more detailed treatments and also has interludes that are intended to inform and entertain. The book is intended as a companion on the journey through control theory, and although the early chapters concentrate on fundamental ideas such as feedback and stability, later chapters deal with more advanced topics such as state variables, optimisation, estimation, Kalman filtering and robust control.

Robotic and mechatronic systems, autonomous vehicles, electric power systems and smart grids, as well as manufacturing and industrial production systems can exhibit complex nonlinear dynamics or spatio-temporal dynamics which need to be controlled to ensure good functioning and performance.

In this comprehensive reference, the authors present new and innovative control and estimation methods and techniques based on dynamical nonlinear and partial differential equation systems. Such results can be classified in five main domains for the control of complex nonlinear dynamical systems using respectively methods of approximate (local) linearization, methods of exact (global) linearization, Lyapunov stability approaches, control and estimation of distributed parameter systems and stochastic estimation and fault diagnosis methods.

Control and Estimation of Dynamical Nonlinear and Partial Differential Equation Systems: Theory and applications will be of interest to electrical engineering, physics, computer science, robotics and mechatronics researchers and professionals working on control problems, condition monitoring, estimation and fault diagnosis and isolation problems. It will also be useful to skilled technical personnel working on applications in robotics, energy conversion, transportation and manufacturing.