Adaptive neural network fixed-time formation control of unmanned ground vehicles

This paper is concerned with the fixed-time formation control problem of unmanned ground vehicles. The essential work of this paper is the development of a practical fixed-time formation control strategy for coordinated lateral and longitudinal control via adaptive neural control techniques. Initially, a leader-follower formation model is established for describing the relationship between unmanned ground vehicles. Subsequently, a novel adaptive neural network fixed-time controller is developed to achieve integrated lateral and longitudinal formation control within a fixed time. It is theoretically shown that all closed-loop signals remain bounded and the formation tracking errors converge to a small neighborhood of zero in a fixed time. Finally, simulation studies and comparisons are presented to show the effectiveness of the proposed control approach.