Robust Fuzzy Model Predictive Control of an Overhead Crane

Abstract

The method of controlling an overhead crane with respect to the variation of operating conditions and control constraints is developed using a model predictive control (MPC) and fuzzy interpolation applied in linear parameter varying (LPV) approach to crane dynamic modelling. The proposed control approach is based on the assumption that operating conditions vary within the known range of scheduling variables, and the parameters of a crane dynamic model can be interpolated by a quasi-linear fuzzy model designed through utilizing the P1-TS fuzzy theory. Hence, a crane dynamic is approximated through interpolation between a set of local linear models determined through identification experiments at the local operating points selected within the bounded intervals of scheduling variables. For the modelling assumptions, the control algorithm is developed based on a generalized predictive control (GPC) procedure taking into consideration the constraints on sway angle of a payload and control signal. Feasibility and applicability of the proposed control technique were confirmed during experiments carried out on a laboratory-scaled overhead crane. The results of experiments are presented and compared with performances of a fuzzy logic-based scheduling control scheme.

An Adjustable Zero Vibration Input Shaping Control Scheme for Overhead Crane Systems

“…Schindele and Aschemann [15] developed a nonlinear MPC to minimize the tracking error at crane rest conditions. Smoczek and Szpytko [16] proposed a soft-constrained MPC to minimize the oscillation of a scaled crane setup. In a recent study, the researchers implemented an MPC-based subcontroller to optimize the velocity of the crane cart throughout the prediction horizon with the desired objective of minimizing the payload oscillation [17].…”

Comparison of Linear and Nonlinear MPC on Operator-In-the-Loop Overhead Cranes

“…The MPC approach for the control of overhead cranes has been demonstrated to yield better performance than standard open-loop techniques in [8]. MPC algorithms have been used for position control of overhead cranes in [9], [10]. In [11], [12] MPC algorithms have been used to track offline computed trajectories, while in [13], [14] energy optimal MPC is applied for point-to-point motion.…”

Model Predictive Control for operator-in-the-loop overhead cranes

“…Most of the research on the application of MPC controllers to gantry cranes has focused on the position control and the sway angle reduction [27], [28]. Some of the proposed MPC applications are based on the tracking of an offline computed trajectory, as in [29] where nonlinear MPC is used for the tracking of trajectories in a 3D environment, using the length of the rope as a gain scheduling parameter.…”