Nonlinear Model Predictive Controller and Feasible Path Planning for Autonomous Robots

This paper develops the nonlinear model predictive control (NMPC) algorithm to control autonomous robots tracking feasible paths generated directly from the nonlinear dynamic equations.NMPC algorithm can secure the stability of this dynamic system by imposing additional conditions on the open loop N...

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Bibliographic Details
Published inOpen computer science Vol. 6; no. 1; pp. 178 - 186
Main Author Minh, Vu Trieu
Format Journal Article
LanguageEnglish
Published De Gruyter Open 01.01.2016
De Gruyter
Subjects
autonomous robots
feasible path generation
nonlinear dynamic system
Nonlinear model predictive control
system stabilization
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Summary:This paper develops the nonlinear model predictive control (NMPC) algorithm to control autonomous robots tracking feasible paths generated directly from the nonlinear dynamic equations.NMPC algorithm can secure the stability of this dynamic system by imposing additional conditions on the open loop NMPC regulator. The NMPC algorithm maintains a terminal constrained region to the origin and thus, guarantees the stability of the nonlinear system. Simulations show that the NMPC algorithm can minimize the path tracking errors and control the autonomous robots tracking exactly on the feasible paths subject to the system’s physical constraints.
ISSN:2299-1093
2299-1093
DOI:10.1515/comp-2016-0015