Fast nonlinear MPC for reference tracking subject to nonlinear constraints via quasi-LPV representations

This paper presents an approach to efficiently implement Nonlinear Model Predictive Control (NMPC) for reference tracking in the presence of nonlinear input and state constraints by making use of quasi-Linear Parameter Varying (quasi-LPV) representations. Using this framework, standard Quadratic Program (QP) solvers can be used for the online optimization problem, making its solution very efficient and viable even for fast plants. This is an extension of a previous result which considered the regulator problem with input constraints. This approach is tested in a simulation study of a 2-DOF robotic manipulator and its efficiency is compared to that of state-of-the-art NMPC approaches.

Subjects

constrained control

efficient algorithms

linear parameter varying systems

Model predictive control

nonlinear systems

DDC Class

600: Technik

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publishedVersion

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https://creativecommons.org/licenses/by-nc-nd/4.0/

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1-s2.0-S2405896317322553-main.pdf

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434.53 KB

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Fast nonlinear MPC for reference tracking subject to nonlinear constraints via quasi-LPV representations