## 2 Design Considerations for the SUZI Prototype
### The Control Law
Because it is preferable for the system to be driven (regulated) by optimal neurocontrol, fuzzy logic switches neurocontrol on whenever \(u_{n}\) is not saturated and whenever the scaled norm \(l_{2}\) (\(y_{1}\)) is smaller than a fixed value \(l_{2,\min}\). With the aim that things be in order, at time \(t_{s}\), when the switching (fuzzy logic control \(\rightarrow\) neuro-control) occurs, the weighting vector \(\
u_{r}\) must be readjusted by considering a scale factor \(\frac{u_{f}}{u_{n}}\)[42]  
\[\
u_{1r}=\frac{\Big{(}u_{f}-\
u_{2}y_{2}\Big{)}u_{f}}{(u_{n}y_{1})},\,\
u_{2r} =\frac{\
u_{2}u_{f}}{u_{n}} \tag{10}\]  
Figure 17: (**a**) Scaled variables \(y_{1},y_{2}\) and (**b**) \(l_{2}(y_{1})\) with their membership functions. After inherent several explorations that are typical for the \"trial and error\" method, the parameters of the neurofuzzy control law were fixed: initial weighting vector (see (2)) \(\
u=[0.001,1]\), the weights of cost function (3), \([q_{1},1,q_{2}]=\left[ \begin{array}{cc}300&1&0.1\end{array} \right]\), and the learning rates (see (4)) \([\delta_{1},\delta_{2}]=[1,1]\). The aforementioned control was implemented into the proof in various numerical simulations and was implemented in several processes [36; 42; 43; 44]. It was proven that the nonconventional neurofuzzy control improved the transient dynamics, mainly in the case of sinusoidal references compared to a proportional control: thus, better tracking, meaning smaller attenuation and dephasing, is achieved. An example of such explorations is presented in Figures 18 and 19. The top figure, Figure 18, shows how a manikin surface with an ambient temperature of 22 \({}^{\circ}\)C can reach the desired temperature of 34 \({}^{\circ}\)C in about 3 min, with the control system then working in steady regime. The figure below Figure 18 shows how thermoregulation is achieved on several levels of the desired temperature. One can also see the variation in the control in mA. Figure 18: Temperature variation: unsteady and steady regime. Figure 19 shows the thermoregulation for air blown from a regular fan blown onto one arm and forearm of the manikin at different flow rates corresponding to different air velocity values. A hot wire anemometer was installed at a 10 cm distance from the manikin's forearm in order to estimate the airflow effect. The air velocity near the manikin was measured using this anemometer, is providing information about the magnitude of the perturbation. The variations in temperature and the injected power for the air velocity values of 0.3 and 0.8 m/s and the perturbation time of 3 min was maintained. The yellow zone on the graphic shows the influence of the fan. The most exposed area was the forearm zone, as the airflow that was issued from the fan was orientated toward this zone. This test concluded that the surface temperature fluctuations did not exceed the limit of \(\pm\)0.1 \({}^{\circ}\)C, so the temperature control system proved to be suitable for this implementation. The temperature zones can be seen in the upper graph (forearm and arm), and the lower graph depicts the current consumption needed to maintain the temperature for these zones.