# A Risk-Sensitive Intelligent Control Algorithm for Servo Motor Based on Value Distribution
## 5 Experiment and Analysis
### Comparison Experiment for Safety Performance  
In order to evaluate the safety performance, the number of times that each control algorithm triggers the overcurrent protection in each task is counted to verify its sensitivity to the current overload problem. The total number of training time steps for each control task is 1 million. During the training process, the cumulative number of times the servo motor control system triggered the overcurrent protection was recorded. In each control task, 10 random seeds were used for 10 repetitions of the training process.  
Figure 9 shows the safety performances of the different compared algorithms on each task. The horizontal coordinate represents the number of time steps taken in training, in millions of steps. The ordinate represents the number of times that the motor model triggered the overcurrent protection mechanism from the start of training to the current time step. The curve represents the mean value of the experimental results, and the shaded area represents the standard deviation of the experimental results.  
From Figure 9 we can see that the proposed risk-sensitive control algorithm achieves the best result in avoiding the current overload state. As shown in Figure 9a-c, in the early stage of training on the TC-ExtExDc task, SC-ExtExDc task, and TC-PMSM task, the cumulative trigger times of each algorithm all increased rapidly. But after about 50,000 steps, 400,000 steps, and 50,000 steps, respectively, our algorithm's cumulative number starts to grow more slowly than the others until the end of the training process. As shown in Figure 9d, in the early stage of training on the SC-PMSM task the cumulative times of all algorithms increase uniformly, and their rate of increase is basically the same. However, after a long period of time, the cumulative numbers of other algorithms begin to be higher than that of the proposed algorithm, which finally achieves the best performance. As shown in Figure 9e, all the compared algorithms do not frequently trigger the overcurrent protection mechanism during the training process on the TC-SeriesDc task. It may be that  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **State’s Dimension** & **Mode** & **Duty Cycle’s Dimension** & **Motor** \\ \hline TC-ExtExDc & 7 & Torque control & 2 & ExtEx DC motor \\ SC-ExtExDc & 7 & Speed control & 2 & ExtEx DC motor \\ TC-PMSM & 14 & Torque control & 3 & Three-phase PMSM motor \\ SC-PMSM & 14 & Speed control & 3 & Three-phase PMSM motor \\ TC-SeriesDc & 5 & Torque control & 1 & Series DC motor \\ SC-SeriesDc & 5 & Speed control & 1 & Series DC motor \\ \hline \hline \end{tabular}
\end{table}
Table 1: The six common tasks used for the performance evaluation of the proposed algorithm.  
the probability of triggering the overcurrent protection mechanism is lower on this task than the others. Especially on the SC-SeriesDc task, none of the compared algorithms triggers the overcurrent protection mechanism.  
Figure 9: The trigger times of overcurrent protection on six tasks: (**a**) TC-ExtExDc; (**b**) SC-ExtExDc; (**c**) TC-PMSM; (**d**) SC-PMSM; (**e**) TC-SeriesDc; and (**f**) SC-SeriesDc.  
In conclusion, except for the SC-SeriesDc task, the proposed risk-sensitive intelligent control algorithm has fewer overcurrent trigger times on the other five control tasks. In other words, it is safer.