About the book
Model Predictive Control (MPC) is a highly studied topic in the control system field. The inclusion of the dynamic models of a plant or process into the formulation aids in improving the control system performance. With advances in computational devices, MPC has started to gain recognition by practitioners in varied industrial sectors such as chemical engineering, industrial plant applications, and recently in robotics and autonomous vehicles, among many others. However, despite the advance and progress, implementation in uncertain environments and highly nonlinear scenario remains challenging. This book tries to visit the recent works on different types of Model Predictive Control, for example, Learning-Based MPC, Robust MPC, Nonlinear MPC, and Explicit MPC.
As the potential of MPC is vast, the book is aimed to cover the application of MPC in multi-applications such as automotive, robotics, agriculture automation, aircraft, chemical process engineering as well as unmanned surface vehicles, and other related topics. The editors believe that this book will be beneficial for researchers, practitioners, and a generic audience who has interests in control engineering in varied applications across different sectors. The publication is also expected to serve as centralized information gathering for aspiring researchers who are starting their research journey in the model predictive control topic. To summarising, this book is aimed to provide a comprehensive wide overview of the recent discussions on the Model Predictive Control topics.