Fixed-time adaptive neural quantized formation control of USVs with tunnel prescribed performance

Abstract

This paper investigates fixed-time adaptive neural quantized formation control of unmanned surface vehicles (USVs) with tunnel prescribed performance in the presence of leader’s information unknown, model dynamic uncertainties and input quantization. Initially, a fixed-time observer is constructed to rapidly estimate the leader’s unknown information. Following this, a control strategy is designed employing a fixed-time sliding mode differentiator-based backstepping technique combined with a neural network-based minimum parameter learning approach (MLP-NN). This strategy addresses the ”complexity explosion” issue arising from the differentiation of the virtual control law and model dynamic uncertainties separately. Moreover, a specialized prescribed performance function is incorporated to ensure that performance specifications of the formation tracking errors meet specific performance criteria. The integration of hysteresis quantizer aids in conserving save communication resources and significantly reduces the detrimental effects caused by quantization errors through the proposed control methodology. It is demonstrated that all closed-loop signals are practical fixed-time stable and formation tracking errors consistently adhere to the predetermined performance envelopes. The efficacy of proposed control strategy is validated through comprehensive numerical simulations.