# A Risk-Sensitive Intelligent Control Algorithm for Servo Motor Based on Value Distribution
## 1 Introduction  
Servo motors play an important role in the composition of automation equipment. They can convert the voltage signal into torque and speed to drive the controlled object with high control precision. Because servo motors can meet the torque and speed control requirements of most tasks, they have been widely used in many manufacturing fields, such as in aerospace, automotive manufacturing, and robotics, among others [1]. The most important thing in the servo motor control system is the control algorithm, which directly affects the performance. The traditional control algorithms, such as the proportion-integration-differentiation (PID), mainly use ordinary differential equations, transfer functions, and dynamic structure diagrams to describe the relationship between the input and output of the system [2]. The main research focus of traditional algorithms lies in the linear time-invariant system with a single input and single output, and bear the nature of a simple structure and easy understanding, thus having been widely used in various industrial scenarios. However, these algorithms can generally only describe the relationship between the input and output of the system, while not being able to describe the internal structure and the change in internal states of the system, so many nonlinear systems use modern control algorithms [3]. A set of modern control methods applying the related control theory have been employed on the servo motor control task [4]. In modern control theory, the analysis and design of control systems are mainly carried out through the description of the state variables of the system. Modern control theory can deal with wider problems than classicalcontrol theory, including linear and nonlinear systems, time-invariant and time-varying systems, and univariate and multivariable systems. Modern control algorithms have excellent control performance for nonlinear systems and can change controller parameters with time-varying systems [5; 6]. They are often used in nonlinear systems with high performance requirements. However, modern control algorithms cannot perform approximate local optimization, therefore, intelligent control algorithms are needed for high-performance and high-precision control systems. At present, the main intelligent control algorithms are based on reinforcement learning [7], which can prompt the servo motor control system to adapt to the complex control environment by automatically adjusting the output of the control system through the imitation of human decision-making behavior [8; 9; 10; 11]. By interacting with the environment, the intelligent controller learns optimal action selection strategies to maximize cumulative rewards. In each interaction, the intelligent controller outputs the duty cycle according to the current operating state of the motor and the target trajectory to obtain optimal control performance. These intelligent control algorithms can adapt to different environmental conditions and task requirements, and have strong generalization ability [12; 13; 14; 15]. However, almost all existing intelligent control methods only consider maximizing the expected value of the cumulative discount return in the interaction process, while usually ignoring its complete probability distribution. This means that only the average performance of the motor is considered, and the performance in special cases is not included in the calculation. Therefore, the motor may often trigger the overcurrent problem (i.e., the current exceeds the rating value) which increases the temperature of the motor and accelerates the aging of insulation, further damaging the motor's service life or even burning out the motor. In view of the aforementioned problems faced by existing control algorithms, this study intends to design a risk-sensitive intelligent control algorithm based on value distribution to avoid the overcurrent problem in the process for training a reinforcement learning agent.