## 6 Conclusions  
The objective of this work was to implement and compare the performance of the adaptive control strategy by gain scheduling and the fuzzy multimodel intelligent control strategy for tracking the speed of the wheels of non-holonomic robot motors with differential drive used in robot football competitions. The comparison was made between \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Left Wheel**} & & \\ \cline{2-5}  & \multicolumn{2}{c}{**Positive Direction**} & & **Negative Direction** \\  & \multicolumn{2}{c}{**ISE**} & **ITAE** & **ISE** & **ITAE** \\ \hline Gain Scheduling & \(6.557\times 10^{4}\) & \(2.574\times 10^{3}\) & \(4.489\times 10^{4}\) & \(2.511\times 10^{3}\) \\ Multimodel Control: 2 Models & \(7.955\times 10^{4}\) & \(2.863\times 10^{3}\) & \(7.240\times 10^{4}\) & \(2.723\times 10^{3}\) \\ Multimodel Control: 3 Models & \(5.725\times 10^{4}\) & \(2.425\times 10^{3}\) & \(4.415\times 10^{4}\) & \(2.142\times 10^{3}\) \\ Multimodel Control: 4 Models & \(6.261\times 10^{4}\) & \(2.581\times 10^{3}\) & \(4.447\times 10^{4}\) & \(2.211\times 10^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance criteria for the left wheel in positive and negative directions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Right Wheel**} & & \\ \cline{2-5}  & \multicolumn{2}{c}{**Positive Direction**} & & **Negative Direction** \\  & \multicolumn{2}{c}{**ISE**} & **ITAE** & **ISE** & **ITAE** \\ \hline Gain Scheduling & \(5.517\times 10^{4}\) & \(2.516\times 10^{3}\) & \(4.355\times 10^{4}\) & \(2.241\times 10^{3}\) \\ Multimodel Control: 2 Models & \(8.728\times 10^{4}\) & \(3.188\times 10^{3}\) & \(6.270\times 10^{4}\) & \(2.521\times 10^{3}\) \\ Multimodel Control: 3 Models & \(3.583\times 10^{4}\) & \(2.049\times 10^{3}\) & \(3.192\times 10^{4}\) & \(1.956\times 10^{3}\) \\ Multimodel Control: 4 Models & \(5.096\times 10^{4}\) & \(2.599\times 10^{3}\) & \(3.395\times 10^{4}\) & \(2.033\times 10^{3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance criteria for the right wheel in positive and negative directions. adaptive control by scaling gains and intelligent controllers considering two, three, and four models for the tuning and design of local controllers. For the fuzzy multimodel intelligent controller, the global control signal is derived from a fuzzy inference of PI controllers that are designed for each obtained and chosen local model. The results demonstrate that the choice of a multimodel approach was successful, especially in fuzzy logic applications. This allowed the system to track all speed steps used as references from a smooth change between signals from each local controller, which is impossible in the gain scheduling strategy. Furthermore, it allowed the team's robot to work with the entire speed range, which was not possible before. Notably, all implementations presented satisfactory results, with good response times and rapid convergence to the reference. Each method has its main positive and negative characteristics. However, based on the performance criteria analyzed, the multimodel fuzzy controller with three models proved to be the most efficient for application to the studied robot. It achieved the lowest errors regarding positive and negative wheel speeds in all possible scenarios. Therefore, the objective of this work to assist the robotics team at the Federal University of Itajuba, Campus Itajura, with solving the speed control problem was fulfilled, and in this way, the team can provide greater competitiveness in the competitions they compete in. Furthermore, this work can provide a basis for advanced control strategies to be explored in academic and research settings among team members. Conceptualization, M.G.M. and L.F.P. ; methodology, M.G.M. and L.F.P. ; validation, M.G.M., L.F.P. and W.W.A.G.S. ; writing--original draft preparation, M.G.M., L.F.P. and W.W.A.G.S. ; writing--review and editing, L.F.P., W.W.A.G.S., R.A.S.B.