Precision motion control is strongly required in many fields, such as precision engineering, micromanufacturing, biotechnology, and nanotechnology. Although great achievements have been made in control engineering, it is still challenging to fulfill the desired performance for precision motion control systems. Substantial works have been presented to reveal an increasing trend to apply optimization approaches in precision engineering to obtain the control system parameters. In this book, we present a result of several years of work in the area of advanced optimization for motion control systems.
The book is organized into two parts: Part I focuses on the model-based approaches, and Part II presents the data-based approaches. To illustrate the practical appeal of the proposed optimization techniques, theoretical results are verified with practical examples in each chapter. Industrial problems explored in the book are formulated systematically with necessary analysis of the control system synthesis.
By virtue of the design and implementation nature, this book can be used as a reference for engineers, researchers, and students who want to utilize control theories to solve the practical control problems. As the methodologies have extensive applicability in many control engineering problems, the research results in the field of optimization can be applied to full-fledged industrial processes, filling in the gap between research and application to achieve a technology frontier increment.
Table of Contents
List of Figures
List of Tables
1.1 Overview of Motion Control Systems
1.2 Optimization Methods
1.3 Model-based Optimization for Motion Control Systems
1.4 Data-based Optimization for Motion Control Systems
I Model-based Optimization for Motion Control Systems
2 Constrained Linear Quadratic Optimization
2.2 Constrained Linear Quadratic Optimization Algorithm
2.3 Case Study
3 Constrained H2 Optimization
3.2 Constrained H2 Optimization Algorithm
3.3 Case Study
4 Constrained H2 Guaranteed Cost Optimization
4.2 Parameter Space Optimization with Structural Constraints
4.3 Constrained H2 Guaranteed Cost Optimization Algorithm
4.4 Case Study
II Data-based Optimization for Motion Control Systems
5 Reduced-order Inverse Model Optimization
5.2 Overview of the 3-DOF Control Structure
5.3 Reduced-order Inverse Model Optimization Algorithm
5.4 Simulation Analysis
5.5 Experimental Validation
6 Reference Profile Alteration and Optimization
6.2 Problem Formulation
6.3 Predictive Feedforward Scheme with O setting Mechanism
6.4 Optimization Algorithm for Reference Profile Alteration
6.5 Simulation Analysis
6.6 Experimental Validation
7 Disturbance Observer Sensitivity Shaping Optimization
7.2 Overview of Disturbance Observer based Control Systems
7.3 Sensitivity Shaping Optimization Procedures
7.4 Simulation Analysis
7.5 Experimental Validation
Jun Ma is currently a Visiting Scholar with the Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA, USA. His research interests include control and optimization, precision mechatronics, robotics, and medical technology. He was a recipient of the Singapore Commonwealth Fellowship in Innovation.
Xiaocong Li is currently a Research Scientist with the Mechatronics Group, Singapore Institute of Manufacturing Technology, Agency for Science, Technology and Research, Singapore. His research interests include precision
motion control, data-driven intelligent control, and industrial automation.
Kok Kiong Tan is currently a Professor with the Department of Electrical and Computer Engineering, National University of Singapore, Singapore. His current research interests include precision motion control and instrumentation, advanced process control and auto-tuning, and general industrial automation.