This study addresses the safe consensus tracking issue for a specific category of multiagent systems (MASs) featuring a static directed communication graph. Each follower agent is subject to external disturbances and governed by unknown pure-feedback nonaffine dynamics. To facilitate the back-stepping approach in nonaffine systems, the mean value theorem (MVT) is employed. Additionally, dynamic surface control (DSC) is implemented to mitigate the intricacies typically encountered in back-stepping frameworks. For the approximation of the unknown nonlinearities, radial basis function neural networks (NNs) are utilized. Integrating these methodologies with principles from graph theory and barrier Lyapunov functions (BLFs), we propose a tailored neuro-adaptive distributed control scheme. The objective of this scheme is to ensure that followers can accurately track the leader’s path while maintaining the globally uniformly bounded (GUB) property of all system signals within the closed loop. Comparative simulation results demonstrate the effectiveness and superiority of the proposed control method.

This article addresses the visual regulation problem of a differential-drive mobile robot with an arbitrarily installed monocular camera in the indoor environment. A three-stage controller is designed by using a novel nonadaptive switching approach, where the unknown image depth and the uncalibrated camera-to-robot translation parameters do not need to be estimated. Convergence of the error systems with the designed controller in each stage is analyzed. Moreover, the existence of the switching time instants from each stage is proved. The simulation results are presented to show the effectiveness of the proposed approach.

This article proposes a targeting posture control approach with dynamic obstacle avoidance of differential-drive wheeled mobile robot (WMR) systems in the presence of unknown skidding, slipping, input disturbances, model uncertainties, and torque saturation. First, a nonlinear model predictive control (NMPC) scheme is presented to generate a feasible trajectory from a starting posture to a targeting posture where dynamic obstacles in the environment and physical constraints of the robots are considered. Second, a robust virtual control law at the kinematic level is introduced for the robots to follow the trajectory. Third, taking the consideration of the unknown skidding, slipping, input disturbances, and model uncertainties in the dynamic model being lumped as a total disturbance, which is estimated by the linear extended state observer (LESO), a disturbance compensation-based saturation controller is designed to make the real velocity of the robot converge to the virtual velocity command. Finally, the effectiveness of the proposed control strategy is verified by simulation results.