Numerical Simulation of Adaptive Radial Basis NN-Based Non-Singular Fast Terminal Sliding Mode Control with Time Delay Estimator for Precise Control of Dual-Axis Manipulator
Source abstract
Abstract
Robotic manipulators can reduce the cost of production and improve productivity; however, controlling a manipulator to follow a desired trajectory is a thorny problem. In this study, we introduced various forms of interference to facilitate the modeling of a dual-axis manipulator. The interference associated with the payload is handled by an adaptive radial basis neural network (ARBNN) controller, while other interference is estimated by a time delay estimator (TDE). The control signal is output by a non-singular fast terminal sliding mode controller (NFTSMC) to minimize further interference. Since the proposed controller can deal with the payload, system uncertainties, external disturbances, friction, and backlash, compared with conventional control methods, it has better tracking accuracy and stability.
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Research context
OpenAlex topics: Adaptive Control of Nonlinear Systems · Dynamics and Control of Mechanical Systems · Advanced Control Systems Design
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Affiliations
Department of Electrical Engineering, National Central University, Taoyuan City 320317, Taiwan
Department of Electronic Engineering, Lunghwa University of Science and Technology, Taoyuan City 333326, Taiwan
Funding
Ministry of Science and Technology of the Republic of China
References (24)
Show source-provided references
1. Review of development and application of industrial robot technology
2. The development and the application of the industrial robot technology
3. Global Asymptotic Saturated PID Control for Robot Manipulators
4. Robust grbf static neuro controller with switch logic for control of robot manipulators10.1109/TNNLS.2012.2196053 ↗
5. Fuzzy-Neural-Network Inherited Sliding-Mode Control for Robot Manipulator Including Actuator Dynamics10.1109/TNNLS.2012.2228230 ↗
6. High-precision trajectory tracking control for space manipulator with neutral uncertainty and dead zone nonlinearity10.1109/TCST.2018.2848641 ↗
7. Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints10.1109/TCYB.2015.2411285 ↗
8. An Adaptive Backstepping Nonsingular Fast Terminal Sliding Mode Control for Robust Fault-Tolerant Control of Robot Manipulators10.1109/TSMC.2017.2782246 ↗
9. Adaptive bias RBF neural network control for a robotic manipulator10.1016/j.neucom.2021.03.033 ↗
10. Modelling of Joint Friction in Robotic Manipulators with Gear Transmissions
11. Controlling mechanical systems with backlash—A survey10.1016/S0005-1098(02)00047-X ↗
12. Modeling and identification of systems with backlash10.1016/j.automatica.2009.11.005 ↗
13. Adaptive Fuzzy Tracking Control of Nonlinear Systems With Asymmetric Actuator Backlash Based on a New Smooth Inverse10.1109/TCYB.2015.2443877 ↗
14. Radial basis function neural networks: A topical state-of-the-art survey10.1515/comp-2016-0005 ↗
15. Radial Basis Function Neural Networks: Theory and Applications
16. Variable structure control: A survey10.1109/41.184817 ↗
17. Sliding-mode variable structure control for complex automatic systems: A survey10.3934/mbe.2022120 ↗
18. Sliding mode control: A survey with applications in math10.1016/j.matcom.2010.10.003 ↗
19. Adaptive Neural Network Control for Robotic Manipulators With Unknown Deadzone10.1109/TCYB.2017.2748418 ↗
20. Adaptive High-Order Terminal Sliding Mode Control Based on Time Delay Estimation for the Robotic Manipulators With Backlash Hysteresis10.1109/TSMC.2019.2895588 ↗
21. Finite time fault tolerant control for robot manipulators using time delay estimation and con-tinuous nonsingular fast terminal sliding mode control10.1109/TCYB.2016.2555307 ↗
22. Learning force control for contact-rich manipulation tasks with rigid position-controlled robots IEEE Robot10.1109/LRA.2020.3010739 ↗
23. Nonsingular fast terminal sliding-mode control for nonlinear dynamical systems10.1002/rnc.1666 ↗
24. Anđelić, N., Lorencin, I., Mrzljak, V., and Carl, Z. (2019, January 11–13). Friction Modeling of Robot Manipulator Joints. Proceedings of the International Conference on Innovative Technologies, Belgrade, Serbia.