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

Model Predictive Control has been proved to enhance the control performance of overhead cranes. However, in Operator-In-the-Loop (OIL) overhead cranes the trajectory of the payload strongly depends on the runtime decisions of the user and can not be predicted beforehand. Simple assumptions on the future references evolution have therefore to be made. In this paper we investigate the applicability of linear and nonlinear MPC strategies to the case of OIL overhead cranes, based on different assumptions on the future evolution of the length of the hoisting cable.

[1]  Andrey V. Savkin,et al.  A new tracking control approach for 3D overhead crane systems using model predictive control , 2014, 2014 European Control Conference (ECC).

[2]  Ozren Bego,et al.  Model predictive control of gantry/bridge crane with anti-sway algorithm , 2015, Journal of Mechanical Science and Technology.

[3]  Pawel Hyla,et al.  The crane control systems: A survey , 2012, 2012 17th International Conference on Methods & Models in Automation & Robotics (MMAR).

[4]  G. Martin,et al.  Nonlinear model predictive control , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[5]  Ali H. Nayfeh,et al.  Anti-Swing Control of Gantry and Tower Cranes Using Fuzzy and Time-Delayed Feedback with Friction Compensation , 2005 .

[6]  Antonio Visioli,et al.  A dynamic inversion approach for oscillation-free control of overhead cranes , 2015, 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA).

[7]  Zhou Wu,et al.  Energy Efficiency of Overhead Cranes , 2014 .

[8]  Aurelio Piazzi,et al.  Optimal dynamic-inversion-based control of an overhead crane , 2002 .

[9]  J. O'Reilly,et al.  Model predictive control of nonlinear systems: computational burden and stability , 2000 .

[10]  Antonio Visioli,et al.  Simplified input-output inversion control of a double pendulum overhead crane for residual oscillations reduction , 2018, Mechatronics.

[11]  Jan Swevers,et al.  Experimental validation of nonlinear MPC on an overhead crane using automatic code generation , 2012, 2012 American Control Conference (ACC).

[12]  Knut Graichen,et al.  Model predictive control of an overhead crane using constraint substitution , 2013, 2013 American Control Conference.

[13]  Arto Marttinen Pole-Placement Control of a Pilot Gantry , 1989, 1989 American Control Conference.

[14]  Jan Swevers,et al.  Experimental validation of time optimal MPC on a flexible motion system , 2011, Proceedings of the 2011 American Control Conference.

[15]  Jaroslaw Smoczek,et al.  Selected measurement and control techniques : experimental verification on a lab-scaled overhead crane , 2017 .

[16]  Baocang Ding,et al.  An off-line output feedback MPC strategy for nonlinear systems represented by quasi-LPV model , 2018 .

[17]  Janusz Szpytko,et al.  Soft-constrained predictive control for an overhead crane , 2017 .

[18]  Harald Aschemann,et al.  Fast Nonlinear MPC for an Overhead Travelling Crane , 2011 .

[19]  L. Chisci,et al.  Gain‐scheduling MPC of nonlinear systems , 2003 .

[20]  William Singhose,et al.  Command shaping for flexible systems: A review of the first 50 years , 2009 .

[21]  Jaroslaw Smoczek,et al.  Comparision of model predictive, input shaping and feedback control for a lab-scaled overhead crane , 2016, 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR).

[22]  Antonio Visioli,et al.  Model Predictive Control for operator-in-the-loop overhead cranes , 2018, 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA).

[23]  Xiaohua Xia,et al.  Model predictive control for improving operational efficiency of overhead cranes , 2015 .