An Adaptive Neural Non-Singular Fast-Terminal Sliding-Mode Control for Industrial Robotic Manipulators
<p>Structure of radial basis function neural network.</p> "> Figure 2
<p>Block diagram of the proposed control method. RBR = radial basis function; NFTSM = nonsingular fast terminal sliding mode control.</p> "> Figure 3
<p>Trajectory tracking positions: (<b>a</b>) at Joint 1, (<b>b</b>) at Joint 2, and (<b>c</b>) at Joint 3.</p> "> Figure 4
<p>Trajectory tracking errors: (<b>a</b>) at Joint 1, (<b>b</b>) at Joint 2, and (<b>c</b>) at Joint 3.</p> "> Figure 5
<p>Control input signals: (<b>a</b>) FNTSMC, (<b>b</b>) classical SMC, and (<b>c</b>) the suggested control methodology. FNTSMC = fast nonsingular terminal sliding mode control.</p> "> Figure 6
<p>Time history of adaptive gain.</p> ">
Abstract
:Featured Application
Abstract
1. Introduction
- The inheritance of NFTSMC advantages in terms of non-singularity, finite-time convergence, fast transient response, low steady-state errors, and high position tracking accuracy.
- The achievement of smooth control inputs with chattering behavior elimination.
- The removal of demand for an exact dynamic model by applying an adaptive radial basis function neural network to approximate an unknown robot function.
- Better tracking performance and less impact by disturbances and uncertainties compared to classic SMC and other control methods based on TSMC.
- Improved robustness and stability of the robot system, as demonstrated by Lyapunov theory.
2. Problem Statements
2.1. Radial Basis Function Neural Network
2.2. Dynamic Model of the Robot Manipulator
3. Design Procedure for a Control Strategy
3.1. Design Non-Singular Fast-Terminal Sliding Variable
3.2. Design an Adaptive Neural Non-Singular Fast-Terminal Sliding-Mode Control for Robotic Manipulators
4. Simulation Analyses
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Control Strategy | Control Parameters | Parameter Value |
---|---|---|
Classical SMC | 2, 9.9, 1 | |
NFTSMC | 5, 3, 2 | |
9.9, 1, 0.1 | ||
Proposed Control Strategy (ANNFTSMC) | ||
0.5, 0.1, 0.01, 0.1 |
Error Control Strategy | |||
---|---|---|---|
SMC | |||
NFTSMC | |||
ANNFTSMC |
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Vo, A.T.; Kang, H.-J. An Adaptive Neural Non-Singular Fast-Terminal Sliding-Mode Control for Industrial Robotic Manipulators. Appl. Sci. 2018, 8, 2562. https://doi.org/10.3390/app8122562
Vo AT, Kang H-J. An Adaptive Neural Non-Singular Fast-Terminal Sliding-Mode Control for Industrial Robotic Manipulators. Applied Sciences. 2018; 8(12):2562. https://doi.org/10.3390/app8122562
Chicago/Turabian StyleVo, Anh Tuan, and Hee-Jun Kang. 2018. "An Adaptive Neural Non-Singular Fast-Terminal Sliding-Mode Control for Industrial Robotic Manipulators" Applied Sciences 8, no. 12: 2562. https://doi.org/10.3390/app8122562
APA StyleVo, A. T., & Kang, H. -J. (2018). An Adaptive Neural Non-Singular Fast-Terminal Sliding-Mode Control for Industrial Robotic Manipulators. Applied Sciences, 8(12), 2562. https://doi.org/10.3390/app8122562