Control With Constraints

This book introduces the principle theories and applications of control and filtering problems to address emerging hot topics in feedback systems. With the development of IT technology at the core of the 4th industrial revolution, dynamic systems are becoming more sophisticated, networked, and advanced to achieve even better performance. However, this evolutionary advance in dynamic systems also leads to unavoidable constraints.

This book presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. The book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware.

by Basil Kouvaritakis (Author), Mark Cannon (Author)
Equips the student to deal with broad classes of system uncertainties with the first textbook treatment of stochastic predictive control
Gives the student an up-to-date source on robust predictive control including details of ten years’ of developments
Illustrates the tutorial material in each chapter with worked examples
Problems and solutions are provided for many of the chapters
Exposes students and practitioners to important new probabilistic applications of model predictive control

Significant progress has been made on nonlinear control systems in the past two decades. However, many of the existing nonlinear control methods cannot be readily used to cope with communication and networking issues without nontrivial modifications. For example, small quantization errors may cause the performance of a "well-designed" nonlinear control system to deteriorate.