- Title
- A dynamic surface controller based on adaptive neural network for dual arm robots
- Creator
- Le, Hai; Nguyen, Linh; Thiyagarajan, Karthick
- Date
- 2020
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/177418
- Identifier
- vital:15303
- Identifier
-
https://doi.org/10.1109/ICIEA48937.2020.9248241
- Identifier
- ISBN:9781728151694 (ISBN)
- Abstract
- The paper introduces an adaptive controller to efficiently manipulate the dual arms of a robot (DAR) under uncertainties including actuator nonlinearities, system parameter variations and external disturbances. It is proposed that by the use of the dynamic surface control (DSC) method, the control strategy is first established, which enables the robot arms to robustly operate on the desired trajectories. Nevertheless, the dynamic model parameters of the DAR system are unknown and impractically estimated due to its uncertain nonlinearities and unexpected external factors. Hence, it is then proposed to employ the radial basis function network (RBFN) to adaptively estimate the uncertain system parameters. The Lyapunov theory is theoretically utilized to derive the adaptation mechanism so that the stability of the closed-loop control system is guaranteed. The proposed RBFN-DSC approach was validated in a synthetic environment with the promising results. © 2020 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 p. 555-560
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2020 IEEE
- Subject
- Dual arm robot; Dynamic surface control; Lyapunov method; Radial basis function; Sliding mode control
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