Ditemukan 21866 dokumen yang sesuai dengan query
Chichester: John Wiley & Sons, 1996
670.427 CAD
Buku Teks SO Universitas Indonesia Library
Gordon, M. Joseph
New Jersey: John Wiley & Sons, 2003
620.1 GOR i
Buku Teks SO Universitas Indonesia Library
Woodson, Wesley E.
New York: McGraw-Hill, 1992
R 620.82 WOO h
Buku Referensi Universitas Indonesia Library
Woodson, Wesley E.
New York: McGraw-Hill, 1992
R 620.82 WOO h
Buku Referensi Universitas Indonesia Library
Heumann, William L.
New York : McGraw-Hill, 1997
628.53 HEU i
Buku Teks Universitas Indonesia Library
Lin, Ching-Fang
Englewood Cliffs, NJ: Prentice-Hall, 1994
003.5 LIN a
Buku Teks SO Universitas Indonesia Library
Hostetter, Gene H., 1939-
Fort Worth: Saunders College Publishing, 1989
629.831 2 HOS d
Buku Teks Universitas Indonesia Library
Gopal, M.
Boston: McGraw-Hill, 2002
629.8 GOP c
Buku Teks Universitas Indonesia Library
Mahmoud, Magdi S.
"Applied control system design examines several methods for building up systems models based on real experimental data from typical industrial processes and incorporating system identification techniques. The text takes a comparative approach to the models derived in this way judging their suitability for use in different systems and under different operational circumstances. A broad spectrum of control methods including various forms of filtering, feedback and feedforward control is applied to the models and the guidelines derived from the closed-loop responses are then composed into a concrete self-tested recipe to serve as a check-list for industrial engineers or control designers. System identification and control design are given equal weight in model derivation and testing to reflect their equality of importance in the proper design and optimization of high-performance control systems. "
London: Springer-Verlag, 2012
e20418572
eBooks Universitas Indonesia Library
Lewis, F.L.
"Neural networks and fuzzy systems are model free control design approaches that represent an advantage over classical control when dealing with complicated nonlinear actuator dynamics. Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities brings neural networks and fuzzy logic together with dynamical control systems. Each chapter presents powerful control approaches for the design of intelligent controllers to compensate for actuator nonlinearities such as time delay, friction, deadzone, and backlash that can be found in all industrial motion systems, plus a thorough development, rigorous stability proofs, and simulation examples for each design. In the final chapter, the authors develop a framework to implement intelligent control schemes on actual systems.
Rigorous stability proofs are further verified by computer simulations, and appendices contain the computer code needed to build intelligent controllers for real-time applications. Neural networks capture the parallel processing and learning capabilities of biological nervous systems, and fuzzy logic captures the decision-making capabilities of human linguistics and cognitive systems."
Philadelphia : Society for Industrial and Applied Mathematics, 2002
e20443147
eBooks Universitas Indonesia Library