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JRM Vol.28 No.5 pp. 695-701
doi: 10.20965/jrm.2016.p0695
(2016)

Paper:

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

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

ANMPC controller

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.

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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Last updated on Nov. 20, 2018