## 5 Verification Tests of the Control Algorithm  
The verification tests of the control algorithm were conducted on the laboratory stand, as shown in Figure 7, consisting of a computer with a dSpace DS1103 control, a measurement card, and a Husarion Panther WMR with mecanum wheels. The dimensions of the robot were 805 \(\times\) 840 \(\times\) 290 mm (length \(\times\) width \(\times\) height), its weight was 55 kg, and the maximum load capacity was 80 kg. The robot control system was built on the basis of an internal Raspberry Pi 4B control computer with a Broadcom BCM2711 processor (Broadcom, San Jose, CA, USA) and an additional Intel NUC10i7 FNK computer (Intel, Santa Clara, CA, USA) for the implementation of computationally demanding tasks, such as mapping the environment, generating collision-free trajectories in an unknown environment, and the implementation of computationally demanding artificial intelligence algorithms. The robot was also equipped with a Slamtec RPLIDAR S1 laser 2D scanner (Slamtec, Shanghai, China). The robot used four 80 PMB800K.80 RBLDC motors (rated power 473 W each) with planetary gears and incremental encoders. The rated torque of each of the drive modules was 34.5 Nm. The power source was a package of lithium-ion batteries with a voltage of 36 V and a capacity of 740 Wh. The maximum speed of the robot was 2 m/s. For the purposes of scientific research with the use of the dSpace control and the measurement card, the design of the robot had been modified by the manufacturer to provide measurement signals for the robot's motion parameters and direct control of the actuator systems, bypassing the internal controller.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Indicator** & **e\({}_{\text{i}\text{max}}\) [rad]** & \(\dot{\text{e}}_{\text{i}\text{max}}\) [rad/s] & \(\varepsilon_{\text{i}}\) [rad] & \(\dot{\varepsilon}_{\text{i}}\) [rad/s] & \(\mathbf{d}_{\text{max}}\) [m] & \(\mathbf{d}_{\text{n}}\) [m] & \(\mathbf{\rho}\) \\ \hline wheel 1, i = 1 & 0.3933 & 0.9179 & 0.1038 & 0.1828 & & & \\ wheel 2, i = 2 & 0.6514 & 1.3743 & 0.1359 & 0.2611 & & & \\ wheel 3, i = 3 & 0.6264 & 0.8208 & 0.1805 & 0.2529 & 0.0442 & 0.0175 & 0.0162 \\ wheel 4, i = 4 & 0.5039 & 0.9180 & 0.1825 & 0.2579 & & & \\ mean value & 0.5438 & 1.0078 & 0.1507 & 0.2387 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quality indicators for neural control simulation.  
The methods of rapid prototyping of control systems with the use of dSpace control and measurement cards provided quick and easy testing on a real object. During the verification tests, the dSpace DS1103 card was used to obtain the measurement data from the encoders and to generate motor control signals. The control algorithm was programmed in the MATLAB/Simulink environment, compiled to the level of optimized C code and then to the machine code and implemented in real time by the dSpace DS1103 card. The scheme of the laboratory stand is shown in Figure 8.  
Figure 8: Diagram of the laboratory stand.  
Figure 7: The laboratory stand.  
The same motion trajectory was used in the verification experiments as in the numerical tests, and the same settings of the control algorithms were also adopted. The parametric disturbance of the robot's motion was introduced by changing the texture of the test track surface (at t \(\in\) \(<\) 6.5, 11.2 \(>\) [s]), generating greater rolling resistance in the robot's wheels. The same set of quality indicators (31-37) as in the simulation studies were used to assess the quality of tracking.