Biomimicry for Optimization, Control, and Automation by Kevin M. Passino

By Kevin M. Passino

Biomimicry makes use of our scienti?c figuring out of organic structures to use rules from nature with the intention to build a few know-how. during this publication, we concentration onhowtousebiomimicryof the functionaloperationofthe “hardwareandso- ware” of organic structures for the advance of optimization algorithms and feedbackcontrolsystemsthatextendourcapabilitiestoimplementsophisticated degrees of automation. the first concentration isn't at the modeling, emulation, or research of a few organic procedure. the point of interest is on utilizing “bio-inspiration” to inject new principles, recommendations, and viewpoint into the engineering of complicated automation platforms. there are lots of organic approaches that, at a few point of abstraction, can berepresentedasoptimizationprocesses,manyofwhichhaveasa basicpurpose computerized keep watch over, selection making, or automation. for example, on the point of daily adventure, we will view the activities of a human operator of a few approach (e. g. , the driving force of a motor vehicle) as being a sequence of the easiest offerings she or he makes in attempting to in achieving a few target (staying at the road); emulation of this decision-making technique quantities to modeling a kind of organic optimization and decision-making procedure, and implementation of the ensuing set of rules ends up in “human mimicry” for automation. There are clearer examples of - ological optimization techniques which are used for keep watch over and automation when you think about nonhuman organic or behavioral techniques, or the (internal) - ology of the human and never the ensuing exterior behavioral features (like using a car). for example, there are homeostasis procedures the place, for example, temperature is regulated within the human body.

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Extra resources for Biomimicry for Optimization, Control, and Automation

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Software Engineering for Complex Control Systems . . . . . 1 Software Engineering Methods . . . . . . . . . . . . 2 Software Vs. Control Engineering Methodologies . . . . . . . 3 Complex Control System Design Methodology . . . . . . . Implementing Complex Control Systems . . . . . . . . Hybrid System Theory and Analysis . . . . . . . . . . Exercises . . . . . . . . . . . . . . . . . . 1 The Role of Traditional Feedback Control Systems in Automation Automation has had, and will continue to have, a significant impact on society.

1 Understand Plant and Specify Design Objectives . . . . . . . 2 Construct Models and Uncertainty Representations . . . . . . 3 Analyze Model Accuracy and System Properties . . . . . . . 4 Construct and Evaluate the Control System . . . . . . . . 5 Summary: The Iterative Design Procedure When Using Mathematical Models . . . . . . . . . . . . . . . . . . 6 Methodology Without Mathematical Models: The Use of Heuristics . Complex Hierarchical Control Systems for Automation .

Control System Design Methodology . . . . . . . . . 1 Understand Plant and Specify Design Objectives . . . . . . . 2 Construct Models and Uncertainty Representations . . . . . . 3 Analyze Model Accuracy and System Properties . . . . . . . 4 Construct and Evaluate the Control System . . . . . . . . 5 Summary: The Iterative Design Procedure When Using Mathematical Models . . . . . . . . . . . . . . . . . . 6 Methodology Without Mathematical Models: The Use of Heuristics .

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