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This book offers a comprehensive and practical guide to the design of advanced model predictive control (MPC) strategies for the three-dimensional motion control of autonomous underwater vehicles (AUVs). It addresses the full six-degrees-of-freedom dynamics of AUVs using convex optimization-based MPC techniques, making the resulting control problems computationally tractable.
The book adopts a structured two-stage approach. The first stage provides a self-contained tutorial on advanced MPC design for uncertain systems, including strategies to ensure closed-loop stability. This equips students, academic researchers and engineers with the theoretical and practical foundations needed to understand and apply advanced MPC to complex systems. The second stage applies these methods to the real-world challenge of three-dimensional AUV motion control, offering novel control formulations that outperform conventional methods.
Throughout the book, special attention is given to robustness, constraint handling and optimization structure. Through detailed case studies and extensive simulations, including downloadable MATLAB® implementations in many cases, the book validates the proposed strategies against conventional methods using quantitative performance metrics to demonstrate improved control accuracy, robustness and efficiency. This makes Robust Model Predictive Control for Autonomous Underwater Vehicles a valuable resource not only for researchers and postgraduate students, but also for practicing engineers working on marine robotics and model-based control system design.
List of contents
Autonomous Underwater Vehicle Motion Control: An Introduction.- Towards Robustness in Model Predictive Control.- Autonomous Underwater Vehicle Modelling.- Advances in MPC-Based Motion Control for AUVs Subject to Environmental Disturbances.- Velocity Form MPC for Dynamic Positioning and Trajectory Tracking of an AUV.- Tube-Based MPC for Trajectory Tracking of AUVs using 3D Line-of-Sight Replanning.- Duality-Based Feedback Min-Max MPC for Path-Following of a Dynamically Coupled AUV in Uncertain Environment.- Reflections and Future Directions.- Appendices: Fourth-order Runge Kutta Method for AUV Simulation.- Demonstration of Software Implementation of MPC.
About the author
Dr
Isah A. Jimoh
received his M.Sc. degree in Applied Instrumentation and Control from Glasgow Caledonian University, United Kingdom, in 2019, and completed his Ph.D. in Optimal Control Theory and Applications at the University of Strathclyde, Glasgow, in 2025. His research spans a wide range of interests, including model predictive control, nonlinear time-invariant control, dynamic optimisation, renewable energy systems, marine robotics, mechatronic systems and deep learning. Dr Jimoh has been recognised for academic excellence through prestigious awards such as the Commonwealth Scholarship Award from the UK Foreign, Commonwealth & Development Office (FCDO), and the Petroleum Technology Development Fund (PTDF) Award. He currently serves as a Lead Control Engineer at GE Vernova, where he applies advanced techniques to industrial challenges in the energy sector. He has authored over ten publications in leading international journals and conferences and is an active peer-reviewer for several high-impact journals and technical conferences. His work reflects a strong commitment to bridging theoretical research with real-world engineering applications.
Summary
This book offers a comprehensive and practical guide to the design of advanced model predictive control (MPC) strategies for the three-dimensional motion control of autonomous underwater vehicles (AUVs). It addresses the full six-degrees-of-freedom dynamics of AUVs using convex optimization-based MPC techniques, making the resulting control problems computationally tractable.
The book adopts a structured two-stage approach. The first stage provides a self-contained tutorial on advanced MPC design for uncertain systems, including strategies to ensure closed-loop stability. This equips students, academic researchers and engineers with the theoretical and practical foundations needed to understand and apply advanced MPC to complex systems. The second stage applies these methods to the real-world challenge of three-dimensional AUV motion control, offering novel control formulations that outperform conventional methods.
Throughout the book, special attention is given to robustness, constraint handling and optimization structure. Through detailed case studies and extensive simulations, including downloadable MATLAB® implementations in many cases, the book validates the proposed strategies against conventional methods using quantitative performance metrics to demonstrate improved control accuracy, robustness and efficiency. This makes
Robust Model Predictive Control for Autonomous Underwater Vehicles
a valuable resource not only for researchers and postgraduate students, but also for practicing engineers working on marine robotics and model-based control system design.
