Neural networks-based adaptive command filter control for nonlinear systems with unknown backlash-like hysteresis and its application to single link robot manipulator

Mohamed Kharrat; Moez Krichen; Loay Alkhalifa; Karim Gasmi; Mohamed Kharrat; Moez Krichen; Loay Alkhalifa; Karim Gasmi

doi:10.3934/math.2024048

AIMS Mathematics

2024, Volume 9, Issue 1: 959-973. doi: 10.3934/math.2024048

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Research article

Neural networks-based adaptive command filter control for nonlinear systems with unknown backlash-like hysteresis and its application to single link robot manipulator

1.
Mathematics Department, College of Science, Jouf University, Sakaka, Saudi Arabia
2.
Faculty of CSIT, Al- Baha University, Al Bahah, Saudi Arabia
3.
Department of Mathematics, College of Sciences and Arts, Qassim University, Ar Rass 51921, Saudi Arabia
4.
Department of Computer Science, College of Arts and Sciences at Tabarjal, Jouf University, Jouf, Saudi Arabia

Received: 03 October 2023 Revised: 19 November 2023 Accepted: 30 November 2023 Published: 05 December 2023
MSC : 92B20, 93C10, 93C40

In this paper, an adaptive neural network control problem for nonstrict-feedback nonlinear systems with an unknown backlash-like hysteresis and bounded disturbance was presented. Radial basis function neural networks (RBFNN) were used to approximate the unknown functions and the problem of the explosion of complexity problem was handled by utilizing the command filter method. Furthermore, the influence of an unknown backlash-like hysteresis input was addressed by approximating an intermediate variable. Based on the backstepping method and the command filter technique, an adaptive neural network controller was designed via the approximation abilities of RBFNN. With the help of the Lyapunov stability theory, the proposed controller ensures that all of the signals in closed-loop systems are bounded and that the tracking error fluctuates close to the origin within a bounded area. Finally, a real-world example based on the single-link manipulator was shown to demonstrate the viability of the presented approach.
- nonlinear systems,
- backlash-like hysteresis,
- command filter,
- backstepping method,
- Lyapunov stability,
- single link robot manipulator
Citation: Mohamed Kharrat, Moez Krichen, Loay Alkhalifa, Karim Gasmi. Neural networks-based adaptive command filter control for nonlinear systems with unknown backlash-like hysteresis and its application to single link robot manipulator[J]. AIMS Mathematics, 2024, 9(1): 959-973. doi: 10.3934/math.2024048

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Abstract

In this paper, an adaptive neural network control problem for nonstrict-feedback nonlinear systems with an unknown backlash-like hysteresis and bounded disturbance was presented. Radial basis function neural networks (RBFNN) were used to approximate the unknown functions and the problem of the explosion of complexity problem was handled by utilizing the command filter method. Furthermore, the influence of an unknown backlash-like hysteresis input was addressed by approximating an intermediate variable. Based on the backstepping method and the command filter technique, an adaptive neural network controller was designed via the approximation abilities of RBFNN. With the help of the Lyapunov stability theory, the proposed controller ensures that all of the signals in closed-loop systems are bounded and that the tracking error fluctuates close to the origin within a bounded area. Finally, a real-world example based on the single-link manipulator was shown to demonstrate the viability of the presented approach.

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