Robust predictive visual servoing control for an inertially stabilized platform with uncertain kinematics

In this paper, a disturbance observer (DOB) based predictive control approach is developed for the image-based visual servoing of an inertially stabilized platform (ISP). As the limitation in degrees of freedom of a two-axes ISP, it is hard to estimate the variable feature depth of the target at each control cycle when using an uncalibrated camera, which brings the challenge in the design of the visual servoing controller. To this end, a depth-independent kinematic matrix that only involves nominal parameters is obtained by employing the partitioned scheme in the system modeling. The uncertain kinematics arising from the unknown feature depth, angular velocity tracking errors, and uncalibrated intrinsic parameters is considered as the lumped uncertainty. A discrete-time DOB is then constructed to estimate the lumped uncertainty in real time. Instead of taking an integral action to eliminate tracking errors induced by the uncertain kinematics, the disturbance estimation is actively incorporated into the receding optimization process of the predictive controller. The stability of the closed-loop system is fully analyzed. Experiments on tracking a moving target are performed to validate promising qualities of the proposed approach.