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

Jiasen Wang; Jun Wang; Qing-Long Han

doi:10.1109/TMECH.2020.3016757

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

Jiasen Wang, Jun Wang^*, Qing-Long Han

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

58 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 language	English
Article number	9167474
Pages (from-to)	311-322
Journal	IEEE/ASME Transactions on Mechatronics
Volume	26
Issue number	1
Online published	14 Aug 2020
DOIs	https://doi.org/10.1109/TMECH.2020.3016757
Publication status	Published - Feb 2021

Research Keywords

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

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10.1109/TMECH.2020.3016757

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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.",

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ER -

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

Abstract

Research Keywords

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GRF: Intelligent Mission Planning and Tracking Control of Autonomous Surface Vehicles Based on Neural Computation

GRF: Analysis and Design of Multiscale Neurodynamic Systems with Their Applications for Robust Control, Data Processing, and Supervised Learning

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

Abstract

Research Keywords

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GRF: Intelligent Mission Planning and Tracking Control of Autonomous Surface Vehicles Based on Neural Computation

GRF: Analysis and Design of Multiscale Neurodynamic Systems with Their Applications for Robust Control, Data Processing, and Supervised Learning

Cite this