# Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition
## 2 Literature Review  
As in [5], the visual servo control uses data from a vision system to control the movement of robot or mobile devices. In general, there are two kinds of visual servo control modes, according to the installation position of the camera. According to the fixation of the camera, visual servo can be  
Figure 1: Hoisting positioning. Figure 2: The rack and pinion positioning system. divided into two categories, one is where the camera is placed directly onto a robot or robotic arm, called \"eye-in-hand\", the other is where the camera is fixed at the workspace, called \"eye-to-hand\". Kinematic and dynamic modeling under the disturbance condition is key to completing the relationship between visual error and disturbance. For a visual servo system, a variety of modeling methods are constantly applied and proposed, and the interaction with the external environment is considered. [PERSON] [7] used vision measurement technology and the Extended Kalman Filter (EKF) algorithm to estimate the pose and motion parameters of the target. Based on the target pose and motion parameters, the incremental inverse kinematics model was established to obtain the desired position of the end effector. Different from the above control method, which only considers the system kinematics, [PERSON] [8] studied the kinematics and dynamics of the whole system, including nonlinear, coupling effect, interaction with the external environment and other factors, and applied the scene feedback information of the homography matrix between different images to the system dynamics to improve the control stability of the system. Dynamic modeling under different conditions is the key to solving the stability problem, and the state feedback controller is widely used for stability control [9]. [PERSON] [10] proposed a fault-tolerant control scheme based on a disturbance observer, which can effectively suppress the external disturbance and reduce the impact of actuator failure on the control system. Based on the disturbance observer, the uncertainty caused by external disturbance or actuator failure is established and compensated. Aiming to solve the the problem of control error convergence in a closed-loop control system, an attitude stabilization control system based on integral sliding film was proposed. [PERSON] [11] proposed an Model Predictive Control (MPC) control strategy, including an auxiliary state feedback controller and robot system. The kinematic state error of the nominal system was transformed into a chain system to solve the MPC optimization problem of the nominal system and generate the optimal state trajectory of the robot system. [PERSON] [12] used MPC to stabilize the physical constraints of the robot system; the kinematic equation of the nonholonomic chain system was transformed into the form of skew symmetry, and the exponential decay phase was introduced to solve the uncontrollable problem of the system. Obviously, this can be used as a reference to prove that it is possible to transform the kinematic state error into the chain optimization problem. It is necessary to improve the robustness of the visual servo system in the presence of uncertainty [13; 14; 15; 16]. Under the uncertainty of system dynamics and vision framework, [PERSON] [17] studied the control of planar mobile devices, in order to compensate the uncertainty of the system; a robust controller was designed to ensure the final uniform boundedness of the position tracking. [PERSON] [18] studied the singularity and local extremum in visual servo control, and proposed a robust design strategy to suppress image noise and external disturbance, so as to ensure the internal stability of the closed-loop system. However, when the external interference changes, the effect of this robust design strategy is not ideal. In the research, the constraint optimization problem is transformed into a Ho control framework, which improves the anti-interference of the system.