About the book
The modern world is a world of digital technology. Almost every advancement in nowadays is triggered by one or the other new technical capability based on the science of digitization. Technologies of the past, like time-keeping, industrial automation, automobile, communication, etc. are either updated to digital or augmented with it. This is enabled by the ever-increasing power of digital computation, along with its usability and accessibility. It thus remains without doubt that the age of automation we live in is driven by the digital technologies and is going to be carried on its shoulders in the future.
This necessitated a need to understand control theoretical concepts and system analysis in a discrete time domain, which gave rise to the area of discrete time control systems. This has helped control engineers and designers to theoretically ascertain the possibilities and limitations of a control system design implemented in a digital framework, whereas continuous time designs suffer from the essential mismatch in the nature of the underlying independent time variable in theoretical studies and practical implementation. Also, many practical systems are inherently discrete time in nature, sensors and transducers sample data only at fixed time intervals, and computers calculate the control input only in some finite time.
Traditionally, fundamental concepts of discrete time control systems are derived from the continuous time counterpart upon time discretization of the latter and subsequent formal analysis. This gave rise to discrete time counterparts of system models and controllers in z-domain as well as in state space form. However, discrete time control system design and analysis matured as a discipline in itself with the advent of optimal and adaptive techniques solely based on discrete time approach. Robust nonlinear discrete time controllers were also developed utilizing the ideas of sliding modes, model predictive control, etc.
The techniques for parameter estimation and system identification are largely dominated by discrete time methods. Well-established Kalman filter and extended Kalman filters are developed in discrete time. Many discrete time stochastic filters are utilized in control systems to reduce the impact of noise and disturbance during practical implementation.
Despite the developments in discrete time control designs and their usefulness in control system implementation, there are a few challenges like discretization effect on systems stability, communication loss, etc. which are also areas of serious research. With all its usefulness and limitations, discrete time control systems have found vast areas of application from process control and automation, robotics, network control systems and internet of things, control of networks and multi-agent systems, etc.
This book intends to provide the reader with an overview of detailed control system design methodologies in discrete time which are well-established in literature. Emerging areas of interest in discrete time systems catering to new and existing challenges are also welcomed.