Adaptive neural network backstepping control for a class of uncertain fractional-order chaotic systems with unknown backlash-like hysteresis

Journal Article · Mon Aug 15 00:00:00 EDT 2016 · AIP Advances

DOI:https://doi.org/10.1063/1.4960110· OSTI ID:22611390

Wu, Yimin ^[1]; Lv, Hui ^[2]

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1. School of Mathematics and Statistics, Suzhou University, Suzhou 234000 (China)
2. Department of Applied Mathematics, Huainan Normal University, Huainan 232038 (China)

In this paper, we consider the control problem of a class of uncertain fractional-order chaotic systems preceded by unknown backlash-like hysteresis nonlinearities based on backstepping control algorithm. We model the hysteresis by using a differential equation. Based on the fractional Lyapunov stability criterion and the backstepping algorithm procedures, an adaptive neural network controller is driven. No knowledge of the upper bound of the disturbance and system uncertainty is required in our controller, and the asymptotical convergence of the tracking error can be guaranteed. Finally, we give two simulation examples to confirm our theoretical results.

• OSTI ID: 22611390
• Journal Information: AIP Advances, Vol. 6, Issue 8; Other Information: (c) 2016 Author(s); Country of input: International Atomic Energy Agency (IAEA); ISSN 2158-3226
• Country of Publication: United States
• Language: English

Related Subjects

71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
ALGORITHMS
CHAOS THEORY
CONVERGENCE
DIFFERENTIAL EQUATIONS
DISTURBANCES
HYSTERESIS
LYAPUNOV METHOD
NEURAL NETWORKS
NONLINEAR PROBLEMS
SIMULATION
STABILITY