Model Predictive Control of Gantry Crane with Input Nonlinearity Compensation

This paper proposed a nonlinear model predictive control (MPC) method for the control of gantry crane. One of the main motivations to apply MPC to control gantry crane is based on its ability to handle control constraints for multivariable systems. A pre-compensator is constructed to compensate the input nonlinearity (nonsymmetric dead zone with saturation) by using its inverse function. By well tuning the weighting function matrices, the control system can properly compromise the control between crane position and swing angle. The proposed control algorithm was implemented for the control of gantry crane system in System Control Lab of University of Technology, Sydney (UTS), and achieved desired experimental results.

Authors:

• Steven W. Su
• Hung Nguyen
• Rob Jarman
• Joe Zhu
• David Lowe
• Peter McLean
• Shoudong Huang
• Nghia T. Nguyen
• Russell Nicholson
• Kaili Weng

Keywords:

• Model Predictive Control
• Control constraints
• Input nonlinearity compensation
• Overhead gantry crane.

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