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The study of complex dynamic systems has become increasingly important in recent years due to its wide range of applications in fields such as engineering, physics, economics, and biology. These systems are characterized by their interconnectedness, nonlinearities, and feedback loops, which make them difficult to understand and control. As a result, there has been growing interest in developing tools and techniques for the modelling, identification, and control of complex dynamic systems.The aim of this reprint is to provide an overview of the state-of-the-art methods for the modelling, identification, and control of complex dynamic systems. This reprint covers a wide range of topics, including system identification, model-based control, adaptive control, nonlinear control, and predictive control. It also includes case studies and examples from different fields to demonstrate the practical application of these methods.This reprint is intended for researchers, graduate students, and practitioners in the field of control systems. It assumes a basic understanding of linear systems theory, calculus, and linear algebra. Overall, this reprint provides a comprehensive and up-to-date overview of the methods for the modelling, identification, and control of complex dynamic systems.

Titel:	Complex Dynamic System Modelling, Identification and Control
Autor/in / Beteiligte Person:	Zhu, Quanmin ; Fusco, Giuseppe ; Na, Jing ; Zhang, Weicun ; Azar, Ahmad Taher
Link:	View record at Directory of Open Access Books (Volltext)
Veröffentlichung:	Basel: MDPI - Multidisciplinary Digital Publishing Institute, 2023
Medientyp:	E-Book
Umfang:	352
ISBN:	978-3-0365-7661-9 (print) ; 978-3-0365-7660-2 (print)
DOI:	10.3390/books978-3-0365-7660-2
Schlagwort:	Internal Model Control (IMC) U-model U-model-based control (U-control) Two-Degree-of-Freedom IMC (TDF-IMC) dynamic inversion invariance entropy automatic control mutual information static detection Chi-square test permission FlowDroid series causality analysis Bayesian LSTM multi-sensor system meteorological data big measurement data deep fusion predictor cobalt removal process mechanistic kinetic model parameter estimation constrained parameter estimation data reconciliation robust estimator gross error detection feeding composition fault diagnosis sensor fault actuator fault deep convolutional neural network robot joints railway accident prevention critical hazard identification accident causality network integer programming active diagnosis active reconfiguration constrained systems fault tolerance interpolation control linear programming structured control flexible spacecraft prevent oscillations adaptive fixed-time control neural network control strict-feedback high-order nonlinear systems cluster-delay mean square consensus multi-agent systems stochastic disturbances impulse time windows impulsive control multiplicative adaptation gain adjustment spectral damping robust stability local unknown input interconnected system local reconstrucability global reconstrucability reduce-order uncertain observer chaos theory bifurcation stabilization chaos synchronization robust control rolling bearing fault CEEMDAN DFA improved wavelet threshold QPSO MPE SVM adaptive fixed-time neural network nonlinear interconnected systems X-ray pulsar signal denoising variational mode decomposition MIMO coupling PSO CDM measurement noise robust controller bic Book Industry Communication:T Technology, engineering, agriculture:TB Technology: general issues bic Book Industry Communication:T Technology, engineering, agriculture:TB Technology: general issues:TBX History of engineering & technology
Sonstiges:	Nachgewiesen in: Directory of Open Access Books Sprachen: English Document Type: eBook File Description: image/jpeg Language: English Rights: Attribution 4.0 International ; URL: https://creativecommons.org/licenses/by/4.0/ Notes: ONIX_20230623_9783036576619_155

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Complex Dynamic System Modelling, Identification and Control