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

Zhouhua Peng, Jun Wang, Dan Wang

Research output: Contribution to journalArticlepeer-review

219 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1083-1090
Number of pages8
JournalIEEE Transactions on Control Systems Technology
Volume26
Issue number3
DOIs
Publication statusPublished - May 2018
Externally publishedYes

Keywords

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

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