This paper deals with image based visual servoing (IBSV) by a visual predictive control (VPC) approach. Based on nonlinear model predictive control (NMPC), the visual servoing problem is formulated into a nonlinear constrained minimization problem in the image plane. A global model describing the behavior of the robotic system equipped with the camera is used to predict the evolution of the visual feature on a future horizon. The main interest of this method is the capability to easily take into account different constraints like mechanical limitations and/or visibility constraints. Simulation experiments are performed on a planar manipulator with an omnidirectional camera. Comparisons with the classical control law based on the interaction matrix highlight the efficiency and the robustness of the proposed approach, especially in difficult initial configurations and large displacements.

To fit well the needs of autonomous mobile manipulator, two robust adaptive Neuro-Fuzzy motion controllers are developed. The first controller, based on a computational efficient processing scheme for fuzzy reactive navigation, is used to generate the commands for the servo-systems of robot arm so that, locally, it may choose its way to its goal autonomously. The second fuzzy reactive navigation is implemented in mobile platform so that it maintains a permanent flexible path between two nodes in network generated by a probabilistic roadmap approach. In order to consider the compatibility of stabilisation, mobilisation and manipulation, we derive a coordinated fuzzy local planner algorithm so that the mobile manipulator can avoid stably unknown and/or dynamic obstacles The purpose of an integration of robust controller and Modified Elman Neural Network is to deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics.