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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Article number9167474
Pages (from-to)311-322
Journal / PublicationIEEE/ASME Transactions on Mechatronics
Volume26
Issue number1
Online published14 Aug 2020
Publication statusPublished - Feb 2021

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.

Research Area(s)

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