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

Tomohiro Henmi

doi:10.20965/jrm.2016.p0695

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JRM Vol.28 No.5 pp. 695-701

doi: 10.20965/jrm.2016.p0695

(2016)

Paper:

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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.

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References

[1] J. K. Maciejowski, “Predictive Control with Constraints,” Prentice Hall, New Jersey, 2002.
[2] W. H. Kwon and A. E. Pearson, “On Feedback Stabilization of Time-Varying Discrete Linear Systems,” IEEE Trans. Autom. Control, Vol.23, pp. 479-481, 1978.
[3] V. H. L. Cheng, “A Direct Way to Stabilize Continuous-Time and Discrete-Time Linear Time-Varying Systems,” IEEE Trans. Autom. Control, Vol.24, pp. 641-643, 1979.
[4] C. C. Chen and L. Shaw, “On Receding Horizon Feedback Control,” Automatica, Vol.18, pp. 349-352, 1982.
[5] T. Henmi, T. Ohta, M. Deng, and A. Inoue, “Tracking Control of The Two-link Manipulator using Nonlinear Model Predictive Control,” Proc. of IEEE Int. Conf. on Networking, Sensing and Control, pp. 761-766, 2009.
[6] T. Henmi, M. Deng, and A. Inoue, “Adaptive Control of a Two-link Planar Manipulator using Nonlinear Model Predictive Control,” Proc. of 2010 IEEE Int. Conf. on Mechatronics and Automation, pp. 1868-1873, 2010.
[7] S. Jung and T. Wen, “Nonlinear Model Predictive Control for Swing-UP of a Rotary Inverted Pendulum,” Trans. of the ASME, Vol.126, pp. 666-673, 2004.
[8] D. Q. Mayne and H. Michalska, “Receding Horizon Control of Nonlinear System,” IEEE Trans. Autom. Control, Vol.35, pp. 814-824, 1990.
[9] T. Kobayashi and T. Tani, “Application of Cooperative Control to Petroleum Plants Using Fuzzy Supervisory Control and Model Predictive Multi-variable Control,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.5, No.6, pp. 333-337, 2001.
[10] T. Kayahara and T. Henmi, “Anti-windup compensator for nonlinear model predictive control,” Proc. of 2012 Int. Conf. on Advanced Mechatronic Systems, pp. 406-411, 2012.
[11] A. Matsushita and T. Henmi, “The Performance Validation of an Actuator Fault Detection of a Nonlinear Model Predictive Controller in using Approximate Differentiation,” Proc. of 5th Int. Symposium on Advanced Control of Industrial Processes, pp. 397-402, 2014.
[12] S. Watanabe and M. Harada, “Optimal Tracking Control of a Micro Ground Vehicle,” J. of Robotics and Mechatronics, Vol.27, No.6, pp. 653-659, 2015.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] J. K. Maciejowski, “Predictive Control with Constraints,” Prentice Hall, New Jersey, 2002.

[2] [2] W. H. Kwon and A. E. Pearson, “On Feedback Stabilization of Time-Varying Discrete Linear Systems,” IEEE Trans. Autom. Control, Vol.23, pp. 479-481, 1978.

[3] [3] V. H. L. Cheng, “A Direct Way to Stabilize Continuous-Time and Discrete-Time Linear Time-Varying Systems,” IEEE Trans. Autom. Control, Vol.24, pp. 641-643, 1979.

[4] [4] C. C. Chen and L. Shaw, “On Receding Horizon Feedback Control,” Automatica, Vol.18, pp. 349-352, 1982.

[5] [5] T. Henmi, T. Ohta, M. Deng, and A. Inoue, “Tracking Control of The Two-link Manipulator using Nonlinear Model Predictive Control,” Proc. of IEEE Int. Conf. on Networking, Sensing and Control, pp. 761-766, 2009.

[6] [6] T. Henmi, M. Deng, and A. Inoue, “Adaptive Control of a Two-link Planar Manipulator using Nonlinear Model Predictive Control,” Proc. of 2010 IEEE Int. Conf. on Mechatronics and Automation, pp. 1868-1873, 2010.

[7] [7] S. Jung and T. Wen, “Nonlinear Model Predictive Control for Swing-UP of a Rotary Inverted Pendulum,” Trans. of the ASME, Vol.126, pp. 666-673, 2004.

[8] [8] D. Q. Mayne and H. Michalska, “Receding Horizon Control of Nonlinear System,” IEEE Trans. Autom. Control, Vol.35, pp. 814-824, 1990.

[9] [9] T. Kobayashi and T. Tani, “Application of Cooperative Control to Petroleum Plants Using Fuzzy Supervisory Control and Model Predictive Multi-variable Control,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.5, No.6, pp. 333-337, 2001.

[10] [10] T. Kayahara and T. Henmi, “Anti-windup compensator for nonlinear model predictive control,” Proc. of 2012 Int. Conf. on Advanced Mechatronic Systems, pp. 406-411, 2012.

[11] [11] A. Matsushita and T. Henmi, “The Performance Validation of an Actuator Fault Detection of a Nonlinear Model Predictive Controller in using Approximate Differentiation,” Proc. of 5th Int. Symposium on Advanced Control of Industrial Processes, pp. 397-402, 2014.

[12] [12] S. Watanabe and M. Harada, “Optimal Tracking Control of a Micro Ground Vehicle,” J. of Robotics and Mechatronics, Vol.27, No.6, pp. 653-659, 2015.