## 3 Self-Learning Control System of SynRM Based on EI-BELBIC
### Self-Learning Regulation of Orbitofrontal Cortex  
The stimulation signals received by the orbitofrontal cortex mainly come from the sensory cortex and amygdala but not from the thalamus. The output value of the orbitofrontal cortex of the brain emotion controller is:  
\[O=\left(\mathrm{S}_{1}e+S_{2}\!\int edt\right)W_{1}, \tag{17}\]  
where \(W_{1}\) is the weight of the orbitofrontal cortex. The orbitofrontal cortex completes the real-time tracking of reward signals based on the adjustment control of the amygdala. The weight adjustment rate of the orbitofrontal cortex is as follows:  
\[\Delta W=\beta\bigg{[}\left(\mathrm{S}_{1}e_{1}+S_{2}\!\int edt\right)\cdot \max\!\left(E^{\prime}-REW\right)\bigg{]}, \tag{18}\]  
\[E^{\prime}=E-A_{th}\cdot V_{2}=\sum_{i=1}^{m+1}A_{i}-\sum_{i=1}^{m}O_{i}-A_{th }\cdot V_{m+1}, \tag{19}\]  
where \(\beta\) is the weight learning rate of the orbitofrontal cortex, and \(E^{\prime}\) is the output signal without thalamic stimulus signal. The value of \(\Delta W\) can be positive or negative; that is, the weight regulation rate of the orbitofrontal cortex can play an auxiliary or inhibitory role in the regulation function of the amygdala. By eliminating the error between the reward signal and \(E\), the output of the controller can approach the expected value and achieve accurate control.  
Figure 10 shows the adjustment curves of nodes in the orbitofrontal cortex with sudden loading. The output of the EI-BELBIC remains constant in the stable phase of the system when the weight change rate of the orbitofrontal cortex is 0. The rate of the weight of the orbitofrontal cortex \(\Delta W\) varies with sudden changes in load, that is, from 0 in the stabilization stage to a corresponding negative value with an inhibitory effect, which adjusts the system and then causes \(W\) in Figure 10b to automatically adjust and suppress the error, such that the controller output tends toward the expected value.  
Figure 10: Self-adjusting curves of parameters of orbitofrontal cortex under sudden loading: **(a)**\(\Delta W\); **(b)**\(W\).  
Figure 11 shows the self-adjusting curve of node parameters in the orbitofrontal cortex in the EI-BELBIC under magnetic saturation. As shown in Figure 11a, the larger the \(d\)-axis current, the faster the adjustment speed of \(\Delta W\) is. When \(i_{d}=15\) A, the adjustment time of \(\Delta W\) is 57% less than when \(i_{d}=5\) A, and 25% less than when \(i_{d}=10\) A. Correspondingly, in Figure 11b, the stability time of \(\Delta W\) when \(i_{d}=15\) A is 25% less than when \(i_{d}=10\) A and 57% less than when \(i_{d}=5\) A. It can be seen from the data in Figure 11 that with the deepening of magnetic saturation, the ability of the orbitofrontal cortex to follow and eliminate errors in the control system accurately also increases.  
Figure 12 shows the orbitofrontal cortex regulation curves of the SynRM under different loads with and without considering magnetic saturation, respectively. As shown in Figure 12a, the orbitofrontal cortex dynamically adjusts \(W\) in Figure 12b by changing \(\Delta W\) under different conditions. The greater the variation of motor parameters, the greater the adjustment force of the orbitofrontal cortex and the stronger the anti-interference ability.