Tracking control of redundant robot manipulators using RBF neural network and an adaptive bound on disturbances
العنوان: | Tracking control of redundant robot manipulators using RBF neural network and an adaptive bound on disturbances |
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المؤلفون: | Jin-Hwan Borm, Vikas Panwar, Jangbom Chai, Naveen Kumar |
المصدر: | International Journal of Precision Engineering and Manufacturing. 13:1377-1386 |
بيانات النشر: | Springer Science and Business Media LLC, 2012. |
سنة النشر: | 2012 |
مصطلحات موضوعية: | Lyapunov function, Engineering, Computer simulation, Artificial neural network, business.industry, Mechanical Engineering, Control engineering, Tracking (particle physics), Industrial and Manufacturing Engineering, Computer Science::Robotics, symbols.namesake, Control theory, Stability theory, symbols, Trajectory, Robot, Electrical and Electronic Engineering, business |
الوصف: | In this paper, a hybrid trajectory tracking controller is designed for redundant robot manipulators, consisting of RBF neural network and an adaptive bound on disturbances. The controller is composed of computed torque type part, RBF neural network and an adaptive controller. The controller achieves end-effector trajectory tracking as well as subtask tracking effectively. The controller is able to learn the existing structured and unstructured uncertainties in the system in online manner. The RBF network learns the unknown part of the robot dynamics with no requirement of the offline training. The adaptive controller is used to estimate the unknown bounds on unstructured uncertainties and neural network reconstruction error. The overall system is proved to be asymptotically stable in the sense of Lyapunov. Finally, numerical simulation studies are performed on a 3R planar robot manipulator to show the effectiveness of the control scheme. |
تدمد: | 2005-4602 2234-7593 |
URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::1ec6bf8cf5310562440d55184e00694b https://doi.org/10.1007/s12541-012-0181-5 |
حقوق: | CLOSED |
رقم الأكسشن: | edsair.doi...........1ec6bf8cf5310562440d55184e00694b |
قاعدة البيانات: | OpenAIRE |
تدمد: | 20054602 22347593 |
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