Event-Triggered Predictive Control Algorithm for Multi-AUV Formation Modeling

Abstract

Autonomous underwater robot has high flexibility and autonomy. Multi-autonomous underwater robot formation is its main research direction. In order to improve the application effect of this formation control method, event triggering mechanism and model predictive control are introduced in the experiment for method design. At the same time, neural network and filtering control are introduced in the experiment for the optimization of the method. The autonomous underwater robots 2A~5A were able to smoothly avoid the obstacles under the leader 1A, as demonstrated by the trial results. Autonomous underwater robots 1~5 had a maximum error of 3.8 m and a maximum velocity error of 3.7 m/s. After a period of time, their position and velocity errors converged to 0. The proposed method had a maximum rms of 2.4233 and an average rms of 1.4015. It required the least number of triggers of all the methods for the optimization problem solution. The above results confirm that the multi-autonomous underwater robot formation control method based on event-triggered mechanism and model predictive control proposed in the study can realize efficient and accurate control, and can reduce the difficulty of computation and resource consumption.