Fundamentals of Engineering Cybernetics : Principles, Modeling, Control, Communication, and Intelligent Systems
Language: English
Published by Amazon Digital Services LLC - Kdp Aug 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 110.37
Quantity: 2 available
Add to basketItem description from seller
Neuware - Modern engineered systems do not fit neatly inside one subject-and neither should the way you learn to analyze them.A real feedback system links physical dynamics, measurements, estimation, communication, computation, decision-making, actuation, uncertainty, constraints, and human oversight. Learning these topics separately can leave a critical gap: understanding how the complete loop behaves when noise, delay, model error, saturation, changing conditions, or faults appear.Fundamentals of Engineering Cybernetics closes that gap with a structured engineering path from mathematical foundations and system modeling to advanced control, estimation, optimization, learning, robotics, and resilient cyber-physical systems. The emphasis is not only on obtaining a numerical answer, but on understanding the assumptions, units, validity limits, and evidence behind it.Inside, you will explore how to: - Build dynamic and state-space models from clearly defined variables, boundaries, assumptions, and physical relationships.- Connect feedback and stability with classical design, state feedback, optimal control, observers, Kalman filtering, and sensor fusion.- Account for sampling, quantization, communication delay, jitter, packet loss, actuator limits, and numerical implementation.- Explore constrained optimization, model predictive control, robust and adaptive methods, and learning-augmented control with validation and fallback in view.- Extend the same framework to robotics, autonomy, multi-agent coordination, digital twins, cybersecurity, safety, resilience, and human-machine assurance.- Reinforce learning through transparent derivations, SI-unit calculations, worked examples, practice problems, cross-references, and practical appendix checklists.The manuscript is specifically structured around these interconnected areas, progressing from mathematical foundations through control and estimation to optimization, intelligent control, robotics, distributed cyber-physical systems, safety, and resilience. …
Seller Inventory # 9798191983448
- Title
- Fundamentals of Engineering Cybernetics : Principles, Modeling, Control, Communication, and Intelligent Systems
- Author
- Adrian T Norcroft
- Publisher
- Amazon Digital Services LLC - Kdp Aug 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798191983448
- Item weight
- 1,070 grams
- Dimensions
- 279x216x24 mm
A real feedback system links physical dynamics, measurements, estimation, communication, computation, decision-making, actuation, uncertainty, constraints, and human oversight. Learning these topics separately can leave a critical gap: understanding how the complete loop behaves when noise, delay, model error, saturation, changing conditions, or faults appear.
Fundamentals of Engineering Cybernetics closes that gap with a structured engineering path from mathematical foundations and system modeling to advanced control, estimation, optimization, learning, robotics, and resilient cyber-physical systems. The emphasis is not only on obtaining a numerical answer, but on understanding the assumptions, units, validity limits, and evidence behind it.
Inside, you will explore how to:- Build dynamic and state-space models from clearly defined variables, boundaries, assumptions, and physical relationships.
- Connect feedback and stability with classical design, state feedback, optimal control, observers, Kalman filtering, and sensor fusion.
- Account for sampling, quantization, communication delay, jitter, packet loss, actuator limits, and numerical implementation.
- Explore constrained optimization, model predictive control, robust and adaptive methods, and learning-augmented control with validation and fallback in view.
- Extend the same framework to robotics, autonomy, multi-agent coordination, digital twins, cybersecurity, safety, resilience, and human–machine assurance.
- Reinforce learning through transparent derivations, SI-unit calculations, worked examples, practice problems, cross-references, and practical appendix checklists.
The manuscript is specifically structured around these interconnected areas, progressing from mathematical foundations through control and estimation to optimization, intelligent control, robotics, distributed cyber-physical systems, safety, and resilience.
"Synopsis" may belong to another edition of this title.
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