A data-driven bilinear subspace predictive controller design

Abstract

In this paper, a new data-driven model predictive control (MPC) is considered based on bilinear subspace identification. The system's nonlinear behavior is described with a bilinear subspace predictor structure in MPC framework. Thus, the MPC formulation results in a fixed structure objective function with constraints regardless of the underlying nonlinearity. 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 and closely integration of modeling and control also eliminate the intermediate design step, which provides a means for data-driven controller design in generalized predictive controller (GPC) framework. Finally, the proposed control approach is illustrated with a simulation of a nonlinear continuously stirred tank reactor (CSTR) system.