An efficient algorithm for nonlinear model predictive control of large-scale systems Part II: experimental evaluation for a distillation column

摘要

In the first part of this paper, that appeared in the last issue, an efficient approach for the online optimization in nonlinear model predictive control (NMPC), the so called real-time iteration scheme, was introduced. In the present second part we confirm experimentally the efficiency of the proposed strategy considering the control of a pilot-scale distillation column. In the experiments the column is used for the high-purity separation of a binary mixture of methanol and n-propanol. The differential-algebraic first principles model used for control is stiff and has more than 200 states. Despite the large state dimension the algorithm is able to provide newly optimized control inputs every 20 seconds on a standard PC. Even for rather large disturbances and without the need for much tuning the closed-loop shows good performance and does respect the required constraints. The presented results demonstrate that nonlinear model predictive control can even be applied for the control of large-scale systems and does lead to satisfying performance, if the outlined solution approach is used.