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—This paper presents a new image based visual ser-voing controller named Augmented Image Based Visual Servoing (AIBVS) for a 6 DOF manipulator. The main idea of this controller is that it produces acceleration as the controlling command. A proportional derivative (PD) controller is developed to provide the robot with the controlling command. This controller(More)
This paper presents a navigation method for a mobile robot in urban environments. The navigation method employs a so-called Polar Traversability Index (PTI) to evaluate terrain traversal property along a specific direction and guide the robot's motion. In the navigation system, a 2-D Laser Rangefinder (LRF) is used to produce terrain maps in various(More)
  • Wen-Fang Xie
  • 2007
In this paper, a novel sliding-mode observer based adaptive controller is developed for the servo actuators with friction. The LuGre dynamic friction model is adopted for adaptive friction compensation. A sliding-mode observer is proposed to estimate the internal friction state of LuGre model. Based on the estimated friction state, adaptation laws are(More)
In this paper, an on-line identification algorithm is proposed for nonlinear systems identification via dynamic neural networks with different time-scales including both fast and slow phenomenon. The main contribution of the paper is that the Lyapunov function and singularly perturbed techniques are used to develop the on-line update laws for both dynamic(More)
This paper deals with the adaptive nonlinear identification and trajectory tracking via dynamic multilayer neural network (NN) with different timescales. Two NN identifiers are proposed for nonlinear systems identification via dynamic NNs with different timescales including both fast and slow phenomenon. The first NN identifier uses the output signals from(More)
In this paper, a new discrete time identification scheme for a singularly perturbed nonlinear system using recurrent high order multi-time scale neural network is presented. The high-order neural network (HONN) is known for its simple structure and powerful nonlinearity approximation property, which make it more suitable for modeling the singularly(More)