Adaptive Control: Second Edition (Dover Books on Electrical Engineering)
Starting with a broad overview, the text explores real-time estimation, self-tuning regulators and model-reference adaptive systems, stochastic adaptive control, and automatic tuning of regulators. Additional topics include gain scheduling, robust high-gain control and self-oscillating controllers, and suggestions for implementing adaptive controllers. Concluding chapters feature a summary of applications and a brief review of additional areas closely related to adaptive control.
Both authors are Professors at the Lund Institute of Technology in Sweden, and this text has evolved from their many years of research and teaching. Their insights into properties, design procedures, and implementation of adaptive controllers are complemented by the numerous examples, simulations, and problems that appear throughout the book.
Why Read This Book
You should read this book to get a rigorous, practitioner-oriented treatment of adaptive control algorithms (self-tuning regulators, MRAC, stochastic adaptive control) and the underlying estimation theory used in adaptive signal processing. It will sharpen your sense of when to use adaptive methods, how to analyze convergence and robustness, and how to implement controllers in real time.
Who Will Benefit
Graduate students, control and DSP engineers, and researchers who design adaptive filters/controllers and need a solid theoretical and practical grounding in parameter estimation and adaptive-system design.
Level: Advanced — Prerequisites: Undergraduate-level signals & systems and linear control theory, linear algebra, basic probability and stochastic processes; familiarity with MATLAB is helpful.
Key Takeaways
- Understand the theory and design of model-reference adaptive systems (MRAC) and self-tuning regulators (STR).
- Apply recursive parameter estimation methods (e.g., recursive least squares) for real-time identification.
- Analyze convergence, stability, and robustness properties of adaptive control schemes.
- Design and tune adaptive controllers for stochastic and noisy environments.
- Recognize practical implementation issues (filtering, forgetting factors, numerical problems) and how to mitigate them.
Topics Covered
- 1. Introduction and motivation for adaptive control
- 2. Basic concepts: adaptation, identification, and performance criteria
- 3. Real-time parameter estimation and recursive least squares
- 4. Self-tuning regulators: prediction, control law design, and implementation
- 5. Model-reference adaptive control (MRAC) techniques
- 6. Stochastic adaptive control and probabilistic analysis
- 7. Convergence and stability analysis of adaptive schemes
- 8. Gain scheduling, high-gain and self-oscillating controllers
- 9. Robustness considerations and modification algorithms
- 10. Practical implementation issues and numerical aspects
- 11. Applications and case studies
- 12. Appendices: mathematical tools and background
Languages, Platforms & Tools
How It Compares
More control-oriented than Haykin's 'Adaptive Filter Theory' (which targets signal-processing adaptive filters); closely aligned with classical texts on adaptive control such as Åström & Wittenmark but with a concise, implementation-minded treatment.
