Control Parameters Tuning Method of Nonlinear Model Predictive Controller Based on Quantitatively Analyzing

Tomohiro Henmi

Department of Electro-Mechanical Engineering, National Institute of Technology, Kagawa College
355 Chokushicho, Takamatsu, Kagawa 761-8058, Japan

Received:

March 17, 2016

Accepted:

June 14, 2016

Published:

October 20, 2016

Keywords:

nonlinear model predictive control, analytical tuning method of control parameters, reference trajectory, tracking control

Abstract

The parameter-tuning method we discuss is for an Adaptive Nonlinear Model Predictive Controller (ANMPC). The MPC is optimization-based controller and decides control input to realize system output that tracks a reference trajectory through “optimal computation.” The reference trajectory is ideal trajectory of system output to converge on a desired value, i.e. controlled system performance depends on the reference trajectory. As a MPC controller which applies to the nonlinear systems, our group has already proposed an adaptive nonlinear MPC (ANMPC) for a tracking control problem of nonlinear two-link planar manipulators. This ANMPC uses a new reference trajectory having control parameters that must be tuned based on the desired controlled system’s responses and properties. To reduce troublesome parameter tuning, we propose new parameter-tuning method for ANMPC by a quantitative analysis of the relationship between a system’s behavior and ANMPC parameters. Numerically simulating the two-link nonlinear manipulator’s tracking control under various conditions demonstrates that proposed tuning method tunes the ANMPC effectively.

ANMPC controller

Cite this article as:

T. Henmi, “Control Parameters Tuning Method of Nonlinear Model Predictive Controller Based on Quantitatively Analyzing,” J. Robot. Mechatron., Vol.28 No.5, pp. 695-701, 2016.

Data files:

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