Neural network-based robust finite-time control for robotic manipulators considering actuator dynamics

Adaptive neural finite-time control for a class of switched nonlinear systems

“…It has been proved that some inherently nonlinear systems, which cannot be stabilized by any smooth feedback control method, may be stabilized by using finite-time control methods [23,24]. Recently, some results on finite-time control for robotic manipulators by using neural network have been reported in [28,29]. Based on the terminal sliding mode control method, by using a radial basis function neural network (RBFNN) based compensator to approximate the uncertain nonlinear function, a neural network based robust finite-time control strategy was proposed for the trajectory tracking of robotic manipulators with structured and unstructured uncertainties in [28].…”

“…Recently, some results on finite-time control for robotic manipulators by using neural network have been reported in [28,29]. Based on the terminal sliding mode control method, by using a radial basis function neural network (RBFNN) based compensator to approximate the uncertain nonlinear function, a neural network based robust finite-time control strategy was proposed for the trajectory tracking of robotic manipulators with structured and unstructured uncertainties in [28]. In [29], a second-order uncertain nonlinear dynamical system was considered, where the uncertainties and external disturbances w(x) were unknown and modeled by a RBFNN.…”

Finite-time fault-tolerant control for flutter of wing

“…In nonlinear control problem, the radial basis function (RBF) network is usually used as a tool for modeling nonlinear system because of its good capabilities in function approximation. In this paper, the unknown δ ( ) x u is approximated by the RBF network (Liu & Zhang, 2013;Reiner & Wilamowski, 2015;Yucelen & Calise, 2011) In fact, an active control system may inevitably suffer from time delay problem. Many factors including measurement of system variables, controller calculation, processes for actuators to build up the required control force, etc., may result in non-synchronization of control force.…”

Adaptive coordinated control of multi-mobile manipulator systems

“…In practice, this is difficult, and using them, the model obtained is usually uncertain. To overcome the problem of dynamic modelling and control, some researchers have proposed an adaptive control based on neural network control (Liu et al, 2014Liu and Zhang, 2013) and fuzzy logic approaches (Mai and Wang, 2014;Yoshimura, 2015;Faten Baklouti et al, 2016). For instance, non-model-based techniques have been developed for a different type of MMR with dynamic parameter uncertainties.…”