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Neural Approximation-Based Adaptive Control for Pure-Feedback Fractional-Order Systems With Output Constraints and Actuator Nonlinearities

Neural Approximation-Based Adaptive Control for Pure-Feedback Fractional-Order Systems With Output Constraints and Actuator Nonlinearities

Farouk Zouari, Amina Boubellouta
Copyright: © 2018 |Pages: 28
ISBN13: 9781522554189|ISBN10: 1522554181|EISBN13: 9781522554196
DOI: 10.4018/978-1-5225-5418-9.ch015
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MLA

Zouari, Farouk, and Amina Boubellouta. "Neural Approximation-Based Adaptive Control for Pure-Feedback Fractional-Order Systems With Output Constraints and Actuator Nonlinearities." Advanced Synchronization Control and Bifurcation of Chaotic Fractional-Order Systems, edited by Abdesselem Boulkroune and Samir Ladaci, IGI Global, 2018, pp. 468-495. https://doi.org/10.4018/978-1-5225-5418-9.ch015

APA

Zouari, F. & Boubellouta, A. (2018). Neural Approximation-Based Adaptive Control for Pure-Feedback Fractional-Order Systems With Output Constraints and Actuator Nonlinearities. In A. Boulkroune & S. Ladaci (Eds.), Advanced Synchronization Control and Bifurcation of Chaotic Fractional-Order Systems (pp. 468-495). IGI Global. https://doi.org/10.4018/978-1-5225-5418-9.ch015

Chicago

Zouari, Farouk, and Amina Boubellouta. "Neural Approximation-Based Adaptive Control for Pure-Feedback Fractional-Order Systems With Output Constraints and Actuator Nonlinearities." In Advanced Synchronization Control and Bifurcation of Chaotic Fractional-Order Systems, edited by Abdesselem Boulkroune and Samir Ladaci, 468-495. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5418-9.ch015

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Abstract

In this chapter, an adaptive control approach-based neural approximation is developed for a category of uncertain fractional-order systems with actuator nonlinearities and output constraints. First, to overcome the difficulties arising from the actuator nonlinearities and nonaffine structures, the mean value theorem is introduced. Second, to deal with the uncertain nonlinear dynamics, the unknown control directions and the output constraints, neural networks, smooth Nussbaum-type functions, and asymmetric barrier Lyapunov functions are employed, respectively. Moreover, for satisfactorily designing the control updating laws and to carry out the stability analysis of the overall closed-loop system, the Backstepping technique is used. The main advantage about this research is that (1) the number of parameters to be adapted is much reduced, (2) the tracking errors converge to zero, and (3) the output constraints are not transgressed. At last, simulation results demonstrate the feasibility of the newly presented design techniques.

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