Neural network based-time optimal sliding mode control for an autonomous underwater robot |
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| Affiliation: | 1. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, China;2. School of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan, China;1. Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77204, United States;2. Department of Mechanical Engineering, University of Houston, Houston, TX 77204, United States;3. Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi, Jiangsu 214122, China;4. Department of Engineering Technology, Prairie View A & M University, Prairie View, TX 77446, United States |
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| Abstract: | This paper presents a robot control system using sliding mode control (SMC) as a core controller. The SMC switches according to the Pontryagin’s time optimal control principle, in which the solution is obtained by using neural network approach. The control system is implemented on Chalawan, a six-degree-of-freedom autonomous underwater robot developed at Mechatronics Laboratory, AIT. The control system can be applied to underwater robots, which have similar kind of architecture. Performance of the proposed controller is compared with various classical SMCs and conventional linear control system. The comparison detail results such as controller performance and error phase portrait are presented and analyzed. Such comparisons ensure the implementation success and prove it as a real time-optimal controller. The results also show the controller’s effective capabilities in plant nonlinearity and parameters uncertainties. |
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