کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
413652 | 680653 | 2013 | 8 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Neural network-based robust finite-time control for robotic manipulators considering actuator dynamics Neural network-based robust finite-time control for robotic manipulators considering actuator dynamics](/preview/png/413652.png)
A novel neural network-based robust finite-time control strategy is proposed for the trajectory tracking of robotic manipulators with structured and unstructured uncertainties, in which the actuator dynamics is fully considered. The controller, which possesses finite-time convergence and strong robustness, consists of two parts, namely a neural network for approximating the nonlinear uncertainty function and a modified variable structure term for eliminating the approximate error and guaranteeing the finite-time convergence. According to the analysis based on the Lyapunov theory and the relative finite-time stability theory, the neural network is asymptotically convergent and the controlled robotic system is finite time stable. The proposed controller is then verified on a two-link robotic manipulator by simulations and experiments, with satisfactory control performance being obtained even in the presence of various uncertainties and external disturbances.
► A novel neural network based robust finite time control strategy is proposed.
► The actuator dynamics and uncertainties of robotic manipulators are considered.
► The neural network is used to approximate the nonlinear uncertainty function.
► A modified variable structure term is designed for the finite-time stability.
► The experiments show that the satisfactory control performance is obtained.
Journal: Robotics and Computer-Integrated Manufacturing - Volume 29, Issue 2, April 2013, Pages 301–308