# Manipulator Smooth Control Method Based on LSTM-XGboost and Its Optimization Model Construction
## 1 Introduction
In this context, the research on performance evaluation and quality inspection of industrial robots has become the focus of experts and enterprises in related fields. The serial manipulator is one of the most widely used industrial robots. It has a high degree of freedom and can complete the handling and assembly work through instructions. It is widely used in vehicle manufacturing, electronic assembly, and other fields. Many domestic scholars have also proposed various robot control strategies for different application scenarios [12; 13; 14; 15]. The deep deterministic policy gradient algorithm (DDPG) is used to control the robot arm to complete the action of object picking in a three-dimensional simulation environment. Compared with the manual debugging of the robot arm to achieve object picking, it takes much time. The use of deep reinforcement learning technology shortens the debugging time, and the algorithm after training is adaptive. The deep deterministic strategy gradient algorithm is used to solve the manipulator end control accuracy problem. To enable the manipulator to open the valve without being limited by the size of the valve handwheel, the model of the rotating valve handwheel is established based on the Markov decision process, and the state space and reward function are designed based on the relative position relationship between the end effector of the manipulator and the edge of the valve. Without the support of the kinematics model and dynamics model theory, the robot learns a deterministic grasping strategy through deep reinforcement learning to complete the task of grasping the moving target in space. With the application of the manipulator in various industrial fields, the requirements for its stability have gradually increased. The hardware manufacturing process, motion trajectory planning algorithm, and command parameter setting of the manipulator may lead to different degrees of jitter in the working process of the manipulator, which affects its working quality and efficiency [16; 17; 18; 19; 20]. Therefore, it is necessary to predict the stability of the manipulator. Currently, the research on the stability of industrial manipulators is mainly analyzed from the perspective of dynamic modeling, the trajectory planning algorithm, vibration suppression, and the less direct quantitative evaluation and prediction of its stability. The stability analysis of the manipulator is usually measured by the vibration generated by the movement of the manipulator. The standard vibration quantities include acceleration, velocity, and displacement [21; 22; 23; 24; 25]. In the research on mechanical arm vibration signal acquisition, it is proposed to segment the time series of the collected vibration signal according to the state. On this basis, anomaly detection is realized by analyzing the signal of each state. A wireless high-precision vibration sensor is developed to detect the state of the manipulator in real time. An industrial robot performance evaluation method based on the binocular vision of camera lenses is proposed, which improves the positioning accuracy of the end of the manipulator. The proposed synchronous measurement method of manipulator configuration and target pose improves the motion accuracy and working ability of intelligent cable-driven robots. In addition, the collected vibration signals of the manipulator need to be analyzed and extracted to study the stability [26; 27]. Robotic manipulator control represents a significant challenge in the field of robotics due to the need for precise, safe, and efficient movement in a variety of tasks and environments. Among the many approaches used to model and predict the behavior of these manipulators, machine learning techniques have shown remarkable promise. Specifically, a hybrid model incorporating Long Short-Term Memory (LSTM) and eXtreme Gradient Boosting (XGBoost) has been the focus of recent research. LSTM, a type of Recurrent Neural Network (RNN), is known for its ability to learn and predict time-series data.