This book provides a comprehensive and practical framework for model-based control of MKC (mass–stiffness–damping or mass–spring–damper) systems, emphasizing seamless integration of theory and application. It explores the intricacies of modeling and control strategies tailored to the complexities of MKC systems, prevalent in various industrial applications. Clear explanations and real-world examples equip readers with advanced techniques for enhancing system performance, robustness, and adaptability in the face of nonlinearities and uncertainties.
Key topics include:
- fundamentals of MKC system modeling;
- strategies for feedback linearization and dynamic decoupling; and
- robust control techniques essential for managing real-world systems.
This book is an important resource for anyone dealing with multivariable systems, introducing innovative approaches to disturbance and uncertainty reduction, and decentralized adaptive pole placement. It addresses the need for robust and adaptable control strategies that can handle the inherent complexities and uncertainties of MKC systems, often encountered in industries like robotics, automotive engineering, and aerospace. Collectively, these topics help engineers and researchers deal with common challenges in designing controllers for systems with complex dynamics and interactions.
Model-Based Control of Mass–Stiffness–Damping Systems is valuable for control engineers, researchers, and postgraduate students looking to enhance their understanding and practical familiarity with advanced control methods. Offering a generally applicable and expandable control framework, this book enables immediate practical improvements in existing control schemes and a solid foundation for further exploration and innovation in the control of complex dynamic systems.
Chapter
1. Introduction.- Part I. Modeling.
Chapter
2. Mathematical
Models.
Chapter
3. Model Identification.
Chapter
4. Model Reduction.-
Chapter
5. Controllability and Observability of MKC Systems.- Part II. Basic
Control.
Chapter
6. Model-Based Feedback Linearization.
Chapter
7.
Synthesis of Outer-Loop Controllers.- Part III. Enhanced Control.
Chapter
8.
Model-based Decoupling.
Chapter
9. Model-based Disturbance Rejection and
Uncertainty Attenuation.
Chapter
10. Enhanced Model-Following Control.-
Chapter
11. Structural Properties of Model-following Schemes.
Chapter
12.
Enhanced Feedforward Control.
Chapter
13. Enhanced Model-Reference Adaptive
Control.
Chapter
14. Enhanced Internal Model Control.
Chapter
15. Machine
Learning in Modeling and Control.
Chapter
16. Concluding Remarks.-
Appendices.- Index.
Dr. Hai-An Zhu is Chief Engineer at Omni Technologies. He previously held senior technology and business leadership roles across various divisions of General Electric in Asia. Before GE, he served as Chief Engineer at Philips in Singapore and as Manager of the Technology Center at FESTO, Singapore. His academic career includes serving as Lecturer at the Institute of Artificial Intelligence and Robotics at Xian Jiaotong University and as Research Scholar at the National University of Singapore.
Dr. Zhu holds B.Sc. and M.Sc. degrees in Control Engineering from Xian Jiaotong University, and a Ph.D. in Control Engineering from the National University of Singapore. His expertise is focused on advanced control techniques for complex real-world systems, bridging theoretical insights with practical applications. Throughout his career, Dr. Zhu has received many prestigious awards from governments, professional institutions, and industry clients, recognizing his contributions to scientific innovation and technological advancements that positively impact society.
