Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm

This work deals with robust inverse neural control strategy for a class of single-input single-output (SISO) discrete-time nonlinear system affected by parametric uncertainties. According to the control scheme, in the first step, a direct neural model (DNM) is used to learn the behavior of the syste...

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Veröffentlicht in:Machine intelligence research (Print) Jg. 16; H. 2; S. 213 - 225
Hauptverfasser: Zaidi, Imen, Chtourou, Mohamed, Djemel, Mohamed
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Beijing Springer Nature B.V 01.04.2019
Schlagworte:
Algorithms
Automation
Back propagation
Discrete time systems
Neural networks
Nonlinear control
Nonlinear systems
Robust control
SISO (control systems)
System effectiveness
ISSN:2153-182X, 2153-1838
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Zusammenfassung:This work deals with robust inverse neural control strategy for a class of single-input single-output (SISO) discrete-time nonlinear system affected by parametric uncertainties. According to the control scheme, in the first step, a direct neural model (DNM) is used to learn the behavior of the system, then, an inverse neural model (INM) is synthesized using a specialized learning technique and cascaded to the uncertain system as a controller. In previous works, the neural models are trained classically by backpropagation (BP) algorithm. In this work, the sliding mode-backpropagation (SM-BP) algorithm, presenting some important properties such as robustness and speedy learning, is investigated. Moreover, four combinations using classical BP and SM-BP are tested to determine the best configuration for the robust control of uncertain nonlinear systems. Two simulation examples are treated to illustrate the effectiveness of the proposed control strategy.
Bibliographie:ObjectType-Article-1
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ISSN:2153-182X
2153-1838
DOI:10.1007/s11633-017-1062-2