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Robust Stabilization of a Wheeled Mobile Robot Using Model Predictive Control Based on Neurodynamics Optimization
Hanzhen Xiao,
Zhijun Li,
Chenguang Yang,
Lixian Zhang,
Peijiang Yuan,
Liang Ding,
Tianmiao Wang
IEEE Transactions on Industrial Electronics, Volume: 64, Issue: 1, Pages: 505 - 516
Swansea University Author: Chenguang Yang
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DOI (Published version): 10.1109/tie.2016.2606358
Abstract
In this paper, a robust model predictive control (MPC) scheme using neural network based optimization has been developed to stabilize a physically constrained mobile robot. By applying a state scaling transformation, the intrinsic controllability of a mobile robots can be regained by incorporation i...
| Published in: | IEEE Transactions on Industrial Electronics |
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| ISSN: | 0278-0046 1557-9948 |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa29909 |
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| Abstract: |
In this paper, a robust model predictive control (MPC) scheme using neural network based optimization has been developed to stabilize a physically constrained mobile robot. By applying a state scaling transformation, the intrinsic controllability of a mobile robots can be regained by incorporation into the control input with an additional exponential decaying term. An MPC based control method is then designed for the robot in the presence of external disturbances. The MPC optimization has been formulated as a convex nonlinear minimization problem and a primal-dual neural network (PDNN) is adopted to solve this optimization problem over a finite receding horizon. The computational efficiency of MPC has been significantly improved by the proposed neuro-dynamic approach. Experimental studies under various dynamic conditions have been performed to demonstrate the performance of the proposed approach, which can be applied for a large range of wheeled mobile robots. |
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| College: |
Faculty of Science and Engineering |
| Issue: |
1 |
| Start Page: |
505 |
| End Page: |
516 |