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In this paper, in order to track a moving target, we propose a new control law for an integrated mobile robotpan tilt-camera system. Our controller consists of two control loops, i.e., a kinematic and dynamic control loop, respectively. The kinematic control loop performs three tasks, i.e., allowing an image feature of the target to converge to the center of the image plane asymptotically, designing a trajectory for the mobile robot, and allowing the mobile robot to track the desired position and direction. In the dynamic control loop, the torques are determined; the actual angular velocities of the system, i.e., angular velocities of pan and tilt axis, angular velocities of right and left wheels, track the desired angular velocities which are the outputs of the kinematic controller of the kinematic control loop. According to the Lyapunov theory and Barbalat's theorem, the asymptotic stability of the whole system is proven. Simulation results achieved by using Matlab -Simulink are introduced.

In this paper, we propose a new method to control a robot-camera visual tracking system to track a moving target so that the image feature of the target can match some desired one. In particular, we develop a new control algorithm to calculate the necessary joint torques. To deal with the dynamics and Jacobian uncertainty of the problem, an on-line learning neural network (NN) is used to approximate uncertain components and tune the control scheme to ensure the mismatch of the image feature vanishing to 0. We also prove the asymptotical stability of the proposed tracking method by using Lyapunov stability method.

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