A Data-driven Bilinear Predictive Controller Design based on Subspace Method

Abstract

In this paper, a new data-driven model predictive control (MPC) is considered based on a bilinear subspace method. Being a subclass of nonlinear systems, bilinear system is useful to approximate a class of nonlinear systems and implement predictive control in many circumstances. Therefore, a bilinear predictive control is implemented by exploiting the structural properties of the identified bilinear subspace predictor model. The open-loop optimization problem of MPC that is nonlinear in nature is solved with series quadratic programming (SQP) without any approximations. These improvements in system modeling and optimization solver make the bilinear subspace MPC approach more applicable to real industry processes. Finally, the proposed control approach is illustrated with a simulation of a nonlinear continuously stirred tank reactor (CSTR) system.