Neurodynamics-Based Model Predictive Control of Continuous-Time Under-Actuated Mechatronic Systems

Jiasen Wang, Jun Wang, Qing Long Han

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

This article addresses neurodynamics-based model predictive control of continuous-time under-actuated mechatronic systems. The control problem is formulated as a global optimization problem based on sampled data, which is solved by using a collaborative neurodynamic approach. The closed-loop system is proven to be asymptotically stable. Specific applications on control of autonomous surface vehicles and unmanned wheeled vehicles are elaborated to substantiate the efficacy of the approach.

Original languageEnglish
Article number9167474
Pages (from-to)311-322
Number of pages12
JournalIEEE/ASME Transactions on Mechatronics
Volume26
Issue number1
DOIs
Publication statusPublished - Feb 2021
Externally publishedYes

Keywords

  • Model predictive control (MPC)
  • neurodynamic optimization
  • under-actuated mechatronic systems

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