Fixed-time formation control for multiagent systems with velocity-based collision avoidance

This paper investigates the formation tracking problem in collision-free multiagent systems (MASs). To address the disruption to control performance caused by external disturbances and unknown dynamics, an extended state observer (ESO) incorporating sliding mode and bias radial basis function neural network (RBFNN) is designed for expeditious estimation. The feedback from the observed information is then utilized to formulate a fixed-time formation strategy. Simultaneously, addressing these challenges increases the computational load and communication costs in MASs. Therefore, a distributed event-triggered mechanism is introduced to dynamically adjust controllers' update intervals. Furthermore, to address the high initial speed inherent in the fixed-time control strategy, a velocity-based artificial potential field (APF) is designed to prevent collisions between agents and alleviate actuator strain. The semi-globally ultimately fixed-time boundedness (SGUFTB) of the entire system is demonstrated via Lyapunov theory. The validity of the proposed strategy is subsequently confirmed through the execution of comparative simulation experiments involving five omnidirectional robots.