Constrained Control of Autonomous Surface Vehicles for Multi-Target Encirclement via Fuzzy Modeling and Neurodynamic Optimization

Yue Jiang, Zhouhua Peng, Jun Wang

Research output: Contribution to journalArticlepeer-review

Abstract

This paper addresses the cooperative multi-target encircling control of under-actuated autonomous surface vehicles with unknown kinetics subject to velocity and input constraints. A distributed observer is designed for the vehicles to estimate the geometric center of the area covered by multiple moving targets. Based on the target center estimate, a multi-target encircling guidance law is developed to form encircling trajectories around the targets. A data-driven fuzzy predictor is designed for learning the vehicle kinetics including model input gains with available data. Based on the learned model, a nominal control law is developed to track reference guidance signals. In order to satisfy the velocity and input constraints, a feasibility condition for velocities is derived based on a control barrier function, and a neurodynamics-based optimal control law is developed based on the feasibility condition and input constraint. The bounded input-to-state stability of the closed-loop control system is theoretically proved. Simulation results are elaborated to substantiate the effectiveness of the proposed control approach for circumnavigating multiple moving targets.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalIEEE Transactions on Fuzzy Systems
DOIs
Publication statusAccepted/In press - 2022
Externally publishedYes

Keywords

  • Autonomous surface vehicles
  • control barrier function
  • cooperative multi-target encirclement
  • data-driven fuzzy modeling
  • Kinetic theory
  • neurodynamic optimization
  • Neurodynamics
  • Optimization
  • Predictive models
  • Safety
  • Target tracking
  • Uncertainty

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