Adaptive Neural Network Conformable Fractional-Order Nonsingular Terminal Sliding Mode Control for a Class of Second-Order Nonlinear Systems

Abstract

The paper introduces a novel adaptive neural network fractional-order nonsingular terminal sliding mode controller using conformable fractional-order (CFO) derivative for a class of uncertain nonlinear systems. For this purpose, a new conformable fractional-order nonlinear sliding surface is proposed and the corresponding control law is designed using Lyapunov stability theorem in order to satisfy the sliding condition in finite time. To deal with uncertainties, the lumped uncertainty is approximated by neural networks and adaptation laws are designed using Lyapunov stability concept. As adaptive neural network uses small switching control gain in the presence of large time varying uncertainties the chattering phenomenon is omitted. The proposed adaptive neural network conformable fractional-order nonsingular terminal sliding mode controller (ANN-CFONTSMC) exhibits better control performance, guaranties finite-time convergence and robust stability of the closed-loop control system. Finally, effectiveness of the proposed controller is illustrated through numerical simulations.

KEYWORDS:

• Adaptive control
• Conformable fractional derivative
• Finite-time stability
• Neural networks
• Nonlinear systems
• Nonsingular terminal sliding mode control

Additional information

Notes on contributors

Amir Razzaghian

Amir Razzaghian received his MSc degree in control engineering from Department of Electrical Engineering, Islamic Azad University of Mashhad, Iran in 2015. Currently, he is a PhD candidate at Islamic Azad University of Mashhad, Iran. His research interests include nonlinear control, fractional-order control systems, robotics and neural networks based control systems. Email: [email protected]

Reihaneh Kardehi Moghaddam

Reihaneh Kardehi Moghaddam received her BSc degree in bioelectric engineering from Amirkabir University of Technology, Tehran, Iran in 2001. She received her MSc and PhD degrees in control engineering from Ferdowsi University of Mashhad, Iran in 2004 and 2010, respectively. Currently, she is an associate professor at Islamic Azad University of Mashhad, Iran. Her research interests include nonlinear control, optimal control and neural networks.

Naser Pariz

Naser Pariz received his BSc and MSc degrees in electrical engineering from Ferdowsi University of Mashhad, Iran in 1988 and 1991, respectively, and PhD degree from the Department of Electrical Engineering, Ferdowsi University of Mashhad, Iran in 2001. From 1991 to 1995, he was a lecturer at Ferdowsi University of Mashhad, where he is currently a professor. His research interests include nonlinear control, fractional-order control systems and hybrid systems. Email: [email protected]