Scalability of QP solvers for embedded model predictive control applied to a subsea petroleum production system

The performance of two different Quadratic Programming (QP) solvers for embedded Model Predictive Control (MPC), FiOrdOs and qpOASES, is evaluated for a relevant case study from the petroleum industry. Embedded MPC for the considered system is implemented on a PLC (Programmable Logic Controller) using both solvers. The focus is on the computation time and memory requirements of the solvers as the dimensions of the control problem increase. The results show that qpOASES has a superior performance for small systems with respect to computation time. However, qpOASES has a near cubic growth in computation time with respect to the number of system variables, while FiOrdOs only has a near linear growth. FiOrdOs may thus be faster for larger systems. FiOrdOs has a smaller memory footprint than qpOASES for small systems; however, the program size grows faster than with qpOASES, and for the largest system configuration, the program sizes were almost identical. For even larger systems, qpOASES may have smaller program memory requirements than FiOrdOs, though qpOASES requires more data memory for all problem sizes.

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