Katalog : Details
 | Baraka Olivier MushageDesign and simulation of intelligent nonlinear controllers for nonlinear dynamical engineering systems using MATLAB/SIMULINKApplication to Selected Engineering systems | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| ISBN: | 978-3-8440-9049-9 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Reihe: | Smart System Technologies Herausgeber: Prof. Dr.-Ing. Kyandoghere Kyamakya Klagenfurt | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Band: | 7 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Schlagwörter: | Nonlinear; Adaptive; Intelligent; Robust; fault-tolerant; systems | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Publikationsart: | Fachbuch | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Sprache: | Englisch | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Seiten: | 358 Seiten | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abbildungen: | 36 Abbildungen | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Gewicht: | 534 g | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Format: | 21 x 14,8 cm | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Bindung: | Paperback | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Preis: | 49,80 € / 62,30 SFr | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Erscheinungsdatum: | April 2023 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Kaufen: | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| DOI: | 10.2370/9783844090499 (Online-Gesamtdokument) | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Zusammenfassung: | For many decades, the scientific community has devoted a tremendous amount of attention
to the design of efficient controllers for nonlinear plants, which can be problematic because of
challenges related to real world applications. However, there is still a need for addressing some
issues that remain not clearly solved related to limitations imposed by some assumptions or some
complicated approaches hard to apply for practitioners. There is also a need for approaches able
to handle simultaneously several potential issues without any unnecessary additional energy usage
and with improved control performances. Therefore, this book presents the design of efficient
adaptive nonlinear controllers for strict-feedback nonlinear systems by taking into account multiple
challenges for increased safety and reliability. The considered challenges are external disturbances,
uncertain dynamics, actuation faults, unmeasured states, constrained input, unknown
control direction, and singularity in the control law. Some adaptive nonlinear control schemes,
which are relatively easy to apply for practitioners in order to tackle simultaneously and efficiently
some and/or all aforementioned issues are presented. The book presents the design of schemes
based on a reaching law-based Sliding Mode Control strategies, combined with the Input-Output
Feedback Linearization technique. These schemes use Radial Basis Function Neural Networks
(RBFNN) and Fuzzy Neural Networks (FNNs) to approximate the unknown system dynamics. It is
illustrated how a model-free high-gain state observer is employed for estimating the unavailable
system state variables. For dealing with the unknown control direction, a Nussbaum type function
is presented. The schemes presented in this book have a wide scope of potential applications
as they overcome important restrictions imposed by some assumptions found in many works,
while ensuring very good transient and steady state performances (no peak overshoot, shorter
settling time, smaller error bound and Root-Mean-Square-Error) with low leveled continuous control
efforts. These canceled restrictions are the requirements about the knowledge of bounds for
system dynamics uncertainties, for RBFNN or FNN approximation errors, for actuator’s faults and
for external disturbances, the knowledge of control direction, the availability of full-state measurement,
and the requirement about the positive-definiteness of the control gain function with known
lower and upper bounds. Nonlinear controllers are designed for some particular engineering applications like
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