## 1 Introduction
The main idea is that local models, that is, models based on operating regimes, can be used as measures for the modeling process around the region covered by the conditions of operation, allowing that classic control techniques to be applied. Thus, the concept of multimodels then is the idea of combining several simple models so that this combination emulates a complex model [23; 24; 25; 26]. Gain scheduling is a practical and efficient strategy for controlling nonlinear systems. Controllers that use this method are formed by interpolating between a set of linear controllers developed for different operating points. The interpolation is based on a set of predefined rules that capture the plant's nonlinearities. Therefore, the resulting controller is a linear system with parameters that are adjusted according to the scheduling rules [27]. Classic multimodel techniques such as gain scheduling allow only true or false conclusions (1 or 0). However, there are propositions with variable answers. In these cases, the result is obtained by weights that are acquired from inexact or partial knowledge, whereby the sampled responses are mapped onto a spectrum. Thus, intelligent multimodel techniques like fuzzy logic consider 0 or 1 as true cases but also consider the various states of truth within this interval [28; 29]. In this context, the objective of this work is to develop, implement, and compare the performance of two control strategies to track the speed of the wheels of non-holonomic robots. The first strategy is a gain scheduling adaptive control and the second is a fuzzy multimodel intelligent control strategy. Furthermore, error criteria are calculated to measure the performance of each controller and to verify which strategy is more efficient in its application. Thus, it will be possible to obtain different control options to be implemented in a robot football competition in which the team from the Federal University of Itajuba Campus Itabira participates, providing greater competitiveness throughout the competition. To achieve the objective, firstly, local dynamic models are obtained using the step response technique, and from each local model, PI controllers are tuned. Once the local controllers are designed, an adaptive gain scheduling controller is designed, and the control law is embedded in the low-level code of a non-holonomic robot with differential drive. Furthermore, a fuzzy multimodel intelligent controller is also designed, and the global control law from fuzzy inference is also embedded in the robot's low-level code, allowing tracking of the speed of the right and left wheels. From the implementations, a comparison is made between the adaptive control strategy by scaling gains and the fuzzy multimodel intelligent control strategy to verify the performance of the strategies for application to autonomous robots participating in a robot football competition. The study carried out in this work provides better development of the control sector of the robotics team at the Federal University of Itajuba (Unifei) Campus Itabira, helping the team's competitiveness in Very Small Size (VSS) robot football competitions. This is a competition in which several teams from different universities participate to show and present research and practical application results from each educational institution [30]. Matches between teams are carried out using autonomous mobile robots with differential drive. The positions of the ball and each robot on the field are determined by computer vision systems based on an overhead camera. High-level algorithms responsible for decision-making and movement planning are executed by a computer, which is connected to the low-level code of the microcontrollers in the robots. Figure 1 shows the robots developed by the team. Thus, in addition to the technical contributions of the implementations and the comparison between the two advanced control strategies, there is also a contribution to the application of the results from academic research carried out by the university's robotics team.