KEYWORD ABSTRACTwheeled mobile robots; robust formation control; leader-following;This paper addresses the robust formation control of non-holonomic mobile robots with homogeneous system architecture and decentralized control structure. Therefore, it was necessary the mathematical modeling of mobile robots, from which, the Separation-Bearing variant of Leader-Following control strategy was implemented. The stability sliding mode control. proof were based on the Lyapunov theory. The sliding mode control (SMC) strategy was used in the controller design to make the control robust to the incidence of uncertainties and disturbances. The Fuzzy Adaptive Formation Control is designed to eliminate the previous bounding knowledge of these uncertainties and disturbances. The proposed control effectiveness is demonstrated by results obtained with simulations in Matlab/Simulink. The pure kinematic and kinematic with disturbances is also analyzed. The results shows the controllers effectiveness to formation of multi-robots systems to the eight-shaped trajectory.Ediciones Universidad de Salamancacc by nc dc de Melo et al Robust and adaptive chatter free formation control of wheeled mobile robots with uncertainties This approach has two main variants: separation bearing ( l ≠ Y ) and separation separation (l ≠ l). In (l ≠ Y), one robot is defined as a leader and other is the follower. To the leader is prescribed a reference trajectory and it has to solve one of three problems: point stabilization, path following and trajectory tracking. The follower needs to maintain a desired distance and relative angle. In (l ≠ l), the configuration has three robots, two leaders and one follower. The leaders are similar to (l ≠ Y) approach, and the follower needs to maintain desired distances to each of the leaders. This two variants may also include obstacle avoidance.There are other approaches less popular like artificial potential field w [o], model predictive control n [a], flocking in fixed and switching networks e [h], feedback linearization and neural networks i [a].In e [i], the authors employed the (l ≠ Y) approach including the mobile robot dynamics into the control scheme. Obstacle avoidance based on potential artificial fields was also added in the scheme. s [a] has developed a control of multi-agent non-holonomic system. The control considered collision avoidance for a single robot based on potential artificial fields. The methodology was later applied in a [h] to in a combined formation controller. The authors have done a kinematic controller using sliding mode control. The obstacle avoidance was projected using artificial potential field with coefficient of attractive and repulsive functions determined by a fuzzy system.In n [a], an adaptive neural-network control strategy integrated both kinematic controller and input voltage controller. The authors designed the control law by backstepping technique based on separation-bearing formation control structure of leader-following. Radial basis function neural network (RBFNN) was used...