Both authors have lectured and tutored undergraduate students, and have supervised many final year undergraduate projects and doctoral students in control engineering at the Department of Engineering Science, University of Oxford (Doctor Cannon’s university teaching career spans 20 years whereas Professor Kouvaritakis’ spans more than 40 years). They have also been active in research, publishing hundreds of articles, in prestigious control journals. In addition they have been Investigators and Principal Investigators in several research projects, some of which are connected with industrial partners.

“This book is suitable for advanced undergraduate and graduate students as well as professional researchers and academics. …  The book will also be of interest to the practitioners of advanced process control. … the effort invested in writing this book will certainly be appreciated by its readers. … I am very happy to encourage colleagues active in conventional, robust, and stochastic MPC to acquire this book for their personal collections and make use of it in their research studies.” (Saša V. Raković, IEEE Control Systems Magazine, Vol. 36 (6), December, 2016)“Model Predictive Control (MPC) is a very popular and successful control technique in both the academic and industrial control communities. … undoubtedly, MPC should be part of any current modern control course. This book collects together the many results of the Oxford University predictive control group which have been carried out over a long period and have been very influential in stimulating interest in both linear and nonlinear systems.” (Rosario Romera, Mathematical Reviews, October, 2016)“This book manages to provide complete and mathematically rigorous solutions to all the raised problems, under the considered assumptions. In conclusion, the reviewed book is highly recommended to all students (and in particular starting PhD students), researchers and practitioners seeking for a self-standing, clear and mathematically rigorous exposition of the theory and design of classical, robust and stochastic MPC with a linear prediction model structure.” (Octavian Pastravanu, zbMATH 1339.93005, 2016)

• From the Contents: Introduction.- Classical Model Predictive Control.- Robust Model Predictive Control with Additive Uncertainty: Open-loop Optimization Strategies.- Robust Model Predictive Control with Additive Uncertainty: Closed-loop Optimization Strategies.

Both authors have lectured and tutored undergraduate students, and have supervised many final year undergraduate projects and doctoral students in control engineering at the Department of Engineering Science, University of Oxford (Doctor Cannon’s university teaching career spans 20 years whereas Professor Kouvaritakis’ spans more than 40 years). They have also been active in research, publishing hundreds of articles, in prestigious control journals. In addition they have been Investigators and Principal Investigators in several research projects, some of which are connected with industrial partners.

“This book is suitable for advanced undergraduate and graduate students as well as professional researchers and academics. …  The book will also be of interest to the practitioners of advanced process control. … the effort invested in writing this book will certainly be appreciated by its readers. … I am very happy to encourage colleagues active in conventional, robust, and stochastic MPC to acquire this book for their personal collections and make use of it in their research studies.” (Saša V. Raković, IEEE Control Systems Magazine, Vol. 36 (6), December, 2016)“Model Predictive Control (MPC) is a very popular and successful control technique in both the academic and industrial control communities. … undoubtedly, MPC should be part of any current modern control course. This book collects together the many results of the Oxford University predictive control group which have been carried out over a long period and have been very influential in stimulating interest in both linear and nonlinear systems.” (Rosario Romera, Mathematical Reviews, October, 2016)“This book manages to provide complete and mathematically rigorous solutions to all the raised problems, under the considered assumptions. In conclusion, the reviewed book is highly recommended to all students (and in particular starting PhD students), researchers and practitioners seeking for a self-standing, clear and mathematically rigorous exposition of the theory and design of classical, robust and stochastic MPC with a linear prediction model structure.” (Octavian Pastravanu, zbMATH 1339.93005, 2016)

• From the Contents: Introduction.- Classical Model Predictive Control.- Robust Model Predictive Control with Additive Uncertainty: Open-loop Optimization Strategies.- Robust Model Predictive Control with Additive Uncertainty: Closed-loop Optimization Strategies.

Both authors have lectured and tutored undergraduate students, and have supervised many final year undergraduate projects and doctoral students in control engineering at the Department of Engineering Science, University of Oxford (Doctor Cannon’s university teaching career spans 20 years whereas Professor Kouvaritakis’ spans more than 40 years). They have also been active in research, publishing hundreds of articles, in prestigious control journals. In addition they have been Investigators and Principal Investigators in several research projects, some of which are connected with industrial partners.

“This book is suitable for advanced undergraduate and graduate students as well as professional researchers and academics. …  The book will also be of interest to the practitioners of advanced process control. … the effort invested in writing this book will certainly be appreciated by its readers. … I am very happy to encourage colleagues active in conventional, robust, and stochastic MPC to acquire this book for their personal collections and make use of it in their research studies.” (Saša V. Raković, IEEE Control Systems Magazine, Vol. 36 (6), December, 2016)“Model Predictive Control (MPC) is a very popular and successful control technique in both the academic and industrial control communities. … undoubtedly, MPC should be part of any current modern control course. This book collects together the many results of the Oxford University predictive control group which have been carried out over a long period and have been very influential in stimulating interest in both linear and nonlinear systems.” (Rosario Romera, Mathematical Reviews, October, 2016)“This book manages to provide complete and mathematically rigorous solutions to all the raised problems, under the considered assumptions. In conclusion, the reviewed book is highly recommended to all students (and in particular starting PhD students), researchers and practitioners seeking for a self-standing, clear and mathematically rigorous exposition of the theory and design of classical, robust and stochastic MPC with a linear prediction model structure.” (Octavian Pastravanu, zbMATH 1339.93005, 2016)

• From the Contents: Introduction.- Classical Model Predictive Control.- Robust Model Predictive Control with Additive Uncertainty: Open-loop Optimization Strategies.- Robust Model Predictive Control with Additive Uncertainty: Closed-loop Optimization Strategies.