A data-driven subspace predictive controller design for artificial gas-lift process

The objective of artificial gas-lift technique is to improve the oil production in petroleum industry. However, in open-loop control, the stability issue may arise due to the so-called casing heading phenomenon. Artificial Gas-lift process is a nonlinear multivariable time varying system with slow dynamics. Therefore, model predictive control (MPC) can be considered as a good candidate for closed-loop control of such a process. In this work, we present a new design of subspace predictive controller (SPC) for gas-lift process. The SPC is a data driven algorithm, using linear predictor to predict future output based on process input and output data. The linear prediction model is derived offline. Thereby, the key future of the proposed approach is that precise knowledge of the model and on-line optimization are not required to derive the control law. The effectiveness and superiority of the proposed controller is demonstrated in simulation, and compared with a robust nonlinear model predictive controller (NMPC).