Biomimicry for Optimization, Control, and Automation

1. Front Matter

   Pages i-xxxi

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2. Introduction

   1. Challenges in Computer Control and Automation

      Pages 7-55

   2. Scientific Foundations for Biomimicry

      Pages 57-94

   3. For Further Study

      Pages 95-98

3. Elements of Decision Making

   1. Neural Network Substrates for Control Instincts

      Pages 105-152

   2. Rule-Based Control

      Pages 153-223

   3. Planning Systems

      Pages 225-261

   4. Attentional Systems

      Pages 263-308

   5. For Further Study

      Pages 309-311

4. Learning

   1. Learning and Control

      Pages 321-419

   2. Linear Least Squares Methods

      Pages 421-469

   3. Gradient Methods

      Pages 471-546

   4. Adaptive Control

      Pages 547-600

   5. For Further Study

      Pages 601-605

5. Evolution

   1. The Genetic Algorithm

      Pages 613-645

   2. Stochastic and Nongradient Optimization for Design

      Pages 647-717

   3. Evolution and Learning: Synergistic Effects

      Pages 719-753

   4. For Further Study

      Pages 755-758

6. Foraging

   1. Cooperative Foraging and Search

      Pages 765-828

   2. Competitive and Intelligent Foraging

      Pages 829-894

Biomimicry uses our scienti?c understanding of biological systems to exploit ideas from nature in order to construct some technology. In this book, we focus onhowtousebiomimicryof the functionaloperationofthe “hardwareandso- ware” of biological systems for the development of optimization algorithms and feedbackcontrolsystemsthatextendourcapabilitiestoimplementsophisticated levels of automation. The primary focus is not on the modeling, emulation, or analysis of some biological system. The focus is on using “bio-inspiration” to inject new ideas, techniques, and perspective into the engineering of complex automation systems. There are many biological processes that, at some level of abstraction, can berepresentedasoptimizationprocesses,manyofwhichhaveasa basicpurpose automatic control, decision making, or automation. For instance, at the level of everyday experience, we can view the actions of a human operator of some process (e. g. , the driver of a car) as being a series of the best choices he or she makes in trying to achieve some goal (staying on the road); emulation of this decision-making process amounts to modeling a type of biological optimization and decision-making process, and implementation of the resulting algorithm results in “human mimicry” for automation. There are clearer examples of - ological optimization processes that are used for control and automation when you consider nonhuman biological or behavioral processes, or the (internal) - ology of the human and not the resulting external behavioral characteristics (like driving a car). For instance, there are homeostasis processes where, for instance, temperature is regulated in the human body.

• Biomimicry
• Control
• Neural
• Optimization
• Simulation
• algorithms
• automation
• autonom
• genetic algorithms
• robot

From the reviews:

The book is well-written and succeeds in its stated goal of providing a rich overview on the subject of biologically inspired "intelligent Control" for both students and researchers in the controls community. Although the book is aimed at readers with graduate degrees, it can also be used by undergraduate students who are sufficiently advanced in their understanding of control theory.

The text is amply illustrated with charts and graphs, as well as important mathematical equations, although this book makes a point of not going too deeply into the mathematics… The margins of the text are usefully annotated with statements that seek to summarize key points that the author wishes to bring to the reader's attention.

The author also maintains a companion website that is continually updated and provides the reader with further examples and design problems, solution hints, lecture slides, and Matlab codes to help solve a number of key problems… the book is amply supported by an extensive reference list with 560 entries that serve as a "gateway" to publications on both the subjects covered in the book and those that have been omitted.

At 926 pages, this book serves as a hefty but useful introduction and overview to a fascinating and growing field of engineering. It succeeds in its stated goal of providing the reader with a modern overview of biomimicry.

IEEE Transactions on Automatic Control 51 (2006) 1406 – 1407 (Reviewer: Jonathan K. Chow)

"The recent book … is a welcome addition to the field of intelligent control, providing a good treatment on how to use our understanding of biological systems for the development of optimization algorithms and feedback control systems. … The book is quite readable, and it challenges the readers to explore further ideas and issues in the field. In this sense, the main objectives of the author have been successfully achieved. … the book will find its appropriate place in university lecture rooms and libraries." (Clarence W. De Silva, SIAM Reviews, Vol. 47 (4), 2005)

• Department of Electrical and Computer Engineering, 416 Dreese Laboratories, The Ohio State University, Columbus, USA

  Kevin M. Passino

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