# Adaptive Visual Servoing Control for Hoisting Positioning Under Disturbance Condition
## 2 Literature Review
It means that the research system not only contains uncertainty, but also requires to a strongly conservative performance index to be achieved. In order to obtain better dynamic stability performance in a camera robot system, [PERSON] [19] proposed a new Lyapunov-positive definite function based on the asymptotic stability of the visual servo system. Considering the more complex situation of visual systems, including the uncertainty of system dynamics and camera parameters, the asymptotic convergence of image tracking errors was proved. [PERSON] [20] focused on the research of ship motion control, and put forward a scheme based on the sliding mode control. With the aim of studying wave disturbance, he introduced the nonlinear disturbance observer, and studied its suppression characteristics. The stability of the above studies are, respectively, observed from two different perspectives--aspects of visual function and disturbance suppression--but the environmental limitations are more prominent. Obviously, the nonlinear problem of the visual servoing system is inevitable [21; 22; 23]. Elastic objects will deform under the condition of complete constraint, which will bring more nonlinear problems to visual servo control. [PERSON] [21] proposed an uncalibrated algorithm based on Lyapunov to estimate the visual Jacobian matrix in the deformed state; he applied this method to a clamping operation based on visual servo control, and combined the pose information of the gripper with the visual information to realize the recognition and control of its pose. [PERSON] [22] extracted sensitive features from image information to meet the requirements of position and direction control. The direction and position of the target were controlled by the idea that the target size is sensitive to the depth of the image. The feature translation caused by the rotation process was used as compensation, and the image depth was estimated by the interaction matrix and the change in the image features. Too much dependence on camera-sensitive features, when the delay is too large, it is likely to cause data distortion. Aiming at solving the control problem of multi-camera visual servo control systems, [PERSON] [23] proposed a design method based on the embedded concept, and defined each subsystem in the multi-level system structure according to the conversion function. The accuracy of the multi camera system was limited, and the processing load of the system was increased. In recent years, machine learning methods such as neural networks have been used to solve various nonlinear problems [24; 25]. [PERSON] [26] proposed a vision servo (IBVS) dynamic positioning strategy. In the speed-tracking loop of the control system, an adaptive controller based on a neural network was designed. With the premise of ensuring the convergence of speed tracking error, the influence of cost function, the dynamic model and the speed reference model on system performance was studied compared to other schemes. Considering the dynamic and nonlinear problems of the system, [PERSON] [27] proposed a vision servo scheme based on a neural network. An adaptive neural network was used to fit the unknown dynamic model. The advantage of this scheme is that it solves the problem of nonlinear effect of output. The controller based on neural network improves the control stability, but puts forward higher requirements for neural network construction. In the application of photometry in visual servo systems, the image changes because of the appearance and disappearance of some scenes [28]. [PERSON] [29] focused on the research of visual servo technology based on photometric moment, and proposed a relatively direct and simple method that does not need feature extraction, visual tracking and image matching in traditional visual servo technology. The challenge of this method is that when the appearance of the image changes or partially disappears, its stability is difficult to guarantee.