A robust model predictive control with constraint modification for gas lift allocation optimization

Abstract

This paper presents a novel approach for implementing a robust real-time optimization framework under the presence of parametric uncertainty. Conservativeness is an inevitable drawback of a robust control approach. Therefore we aimed to provide a simple and efficient method to mitigate the conservativeness while the robust fulfillment of the constraints is still preserved. The proposed method in this paper is based on the worst-case realization of the uncertainties, however, with constraint modification. The mismatch between measured and predicted output is used directly to modify the active constraint in the optimization problem. The superiority of the method in terms of conservativeness and computational time has been demonstrated in comparison with the other robust optimization counterparts, such as traditional min–max and multi-stage MPC. The promising advantage of the proposed method is that not only it reduces the conservativeness significantly, but also the computational price for this achievement is considerably cheaper than closed-loop optimization methods such as multi-stage MPC.