结合深度信息的视觉伺服准最小最大MPC方法

将特征点的深度信息和像素坐标作为视觉特征，提出一种视觉伺服准最小最大模型预测控制(MPC)方法。与传统方法相比，机器人控制信号通过在线求解线性矩阵不等式的凸优化问题获得，其可行解可保证系统的闭环渐近稳定性。该方法易于处理系统约束，在满足执行器机械限制的前提下能够有效规划特征点的图像轨迹时，深度特征的引入对于改进摄像机的三维轨迹具有显著效果，六自由度工业机器人手眼系统的仿真结果验证了所提出算法的有效性。。

Quasi-min-max MPC algorithm for visual servoing system with depth information

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.