Taking depth information and pixel coordinates of the feature points as image features, a quasi-min-max model
predictive control(MPC) algorithm for image-based visual servoing is presented. Compared with the traditional method, the
robot control signals can be obtained by the convex optimal problem involving linear matrix inequalities(LMIs), and the
closed-loop stability of visual servoing system is guaranteed by the feasibility of the LMIs. The proposed method is easy to
deal with the system constraints. Under the premise of actuator mechanical limitations, the image trajectories of the feature
points are effectively constrained. Furthermore, the introduction of the depth information significantly improves the three-
dimensional trajectory of the camera. The simulation results on a 6 degrees-of-freedom robot manipulator with eye-in-hand
configuration show the effectiveness of the proposed algorithm.