Input nonlinearities compensated model predictive control and its control of gantry crane

Abstract EN

This paper proposed a novel 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 especially for multivariable systems.

We constructed a pre-compensator to compensate the input nonlinearity (relay with dead zone) by using its inverse function.

By well tuning the weighting function matrix, the control system can properly compromise the control between crane position and swing angle.

The proposed control algorithm was implemented for the gantry crane system in System Control Lab of University of Technology, Sydney (UTS), and achieved excellent experimental results.

Especially, when multiple control aims (multi-objective) are considered, the proposed MPC controller can wisely balance the control efforts to achieve compromised system performance.

This optimal control based approach made an impressive impression for the engineering students.