Peng Shi
Robust Adaptive Control for Fractional-Order Systems with Disturbance and Saturation
Ebook (PDF Format)
A treatise on investigating tracking control and synchronization control of fractional-order nonlinear systems with system uncertainties, external disturbance, and input saturation Robust Adaptive Control for Fractional-Order Systems with Disturbance and Saturation provides the reader with a good understanding of how to achieve tracking control and synchronization control of fractional-order nonlinear systems with system uncertainties, external disturbance, and input saturation. Although some texts have touched upon control of fractional-order systems, the issues of input saturation and disturbances have rarely been considered together. Th…
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Beschreibung
A treatise on investigating tracking control and synchronization control of fractional-order nonlinear systems with system uncertainties, external disturbance, and input saturation Robust Adaptive Control for Fractional-Order Systems with Disturbance and Saturation provides the reader with a good understanding of how to achieve tracking control and synchronization control of fractional-order nonlinear systems with system uncertainties, external disturbance, and input saturation. Although some texts have touched upon control of fractional-order systems, the issues of input saturation and disturbances have rarely been considered together. This book offers chapter coverage of fractional calculus and fractional-order systems; fractional-order PID controller and fractional-order disturbance observer; design of fractional-order controllers for nonlinear chaotic systems and some applications; sliding mode control for fractional-order nonlinear systems based on disturbance observer; disturbance observer based neural control for an uncertain fractional-order rotational mechanical system; adaptive neural tracking control for uncertain fractional-order chaotic systems subject to input saturation and disturbance; stabilization control of continuous-time fractional positive systems based on disturbance observer; sliding mode synchronization control for fractional-order chaotic systems with disturbance; and more. Based on the approximation ability of the neural network (NN), the adaptive neural control schemes are reported for uncertain fractional-order nonlinear systems Covers the disturbance estimation techniques that have been developed to alleviate the restriction faced by traditional feedforward control and reject the effect of external disturbances for uncertain fractional-order nonlinear systems By combining the NN with the disturbance observer, the disturbance observer based adaptive neural control schemes have been studied for uncertain fractional-order nonlinear systems with unknown disturbances Considers, together, the issue of input saturation and the disturbance for the control of fractional-order nonlinear systems in the presence of system uncertainty, external disturbance, and input saturation Robust Adaptive Control for Fractional-Order Systems with Disturbance and Saturation can be used as a reference for the academic research on fractional-order nonlinear systems or used in Ph.D. study of control theory and engineering.
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Produktdetails
Weitere Autoren: Chen, Mou / Shao, Shuyi
- ISBN: 978-1-119-39333-7
- EAN: 9781119393337
- Produktnummer: 24980630
- Verlag: Wiley
- Sprache: Englisch
- Erscheinungsjahr: 2017
- Seitenangabe: 256 S.
- Plattform: PDF
- Masse: 15'192 KB
Über den Autor
Mou Chen, PhD is a Professor at the College of Automation Engineering at Nanjing University of Aeronautics and Astronautics, China. He also serves as an associate editor for IEEE access and neurocomputing. Shuyi Shao is working toward a Ph.D. degree with a major in control theory and control engineering from the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, China. Peng Shi, PhD is a Professor and Chair in systems and control, at the University of Adelaide, and Victoria University, Australia. He is also an IEEE Distinguished Lecturer, and is a Member of the College of Expert, Australian Research Council.
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