Output-feedback path-following control of autonomous underwater vehicles based on an extended state observer and projection neural networks

Zhouhua Peng, Jun Wang

Research output: Contribution to journalArticlepeer-review

211 Citations (Scopus)

Abstract

This paper presents a design method for output-feedback path-following control of under-actuated autonomous underwater vehicles moving in a vertical plane without using surge, heave, and pitch velocities. Specifically, an extended state observer (ESO) is developed to recover the unmeasured velocities as well as to estimate total uncertainty induced by internal model uncertainty and external disturbance. At the kinematic level, a commanded guidance law is developed based on a vertical line-of-sight guidance scheme and the observed velocities. To optimize guidance signals, optimization-based reference governors are formulated as bound-constrained quadratic programming problems for computing optimal reference signals. Two globally convergent recurrent neural networks called projection neural networks are used to solve the optimization problems in real-time. Based on the optimal reference signals and ESO, a kinetic control law with disturbance rejection capability is constructed at the kinetic level. It is proved that all error signals in the closed-loop system are uniformly and ultimately bounded. Simulation results substantiate the efficacy of the proposed method for output-feedback path-following of under-actuated autonomous underwater vehicles.

Original languageEnglish
Pages (from-to)535-544
Number of pages10
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume48
Issue number4
DOIs
Publication statusPublished - Apr 2018
Externally publishedYes

Keywords

  • Autonomous underwater vehicles
  • extended state observer (ESO)
  • output-feedback
  • path-following
  • projection neural networks

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