Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

315 Scopus Citations
View graph of relations


Related Research Unit(s)


Original languageEnglish
Pages (from-to)1083-1090
Journal / PublicationIEEE Transactions on Control Systems Technology
Issue number3
Online published6 Jun 2017
Publication statusPublished - May 2018


This brief is concerned with the distributed maneuvering of multiple autonomous surface vehicles guided by a virtual leader moving along a parameterized path. In the guidance loop, a distributed guidance law is developed by incorporating a constant bearing strategy into a path-maneuvering design such that a prescribed formation pattern can be reached. To optimize the guidance signal under velocity constraint as well as minimize control torque during transient phase, an optimization-based command governor is employed to generate an optimal guidance signal for vehicle kinetics. The optimization problem is formulated as a bound-constrained quadratic programming problem, which is solved using a recurrent neural network. In the control loop, an estimator is developed where a fuzzy system is used to approximate unknown kinetics based on input and output data. Next, a kinetic control law is constructed based on the optimal command signal and the fuzzy-system-based estimator. By virtue of cascade stability analysis, it is proven that distributed maneuvering errors converge to a residual set. The simulation results illustrate the efficacy of the proposed method.

Research Area(s)

  • Autonomous surface vehicles (ASVs), boundconstrained quadratic programming, constant bearing (CB), distributed maneuvering, fuzzy systems, recurrent neural network (RNN)