Nonlinear identification and control of Organic Rankine Cycle systems using sparse polynomial models

Development of a first principles model of a system is not only a time- and cost-consuming task, but often leads to model structures which are not directly usable to design a controller using current available methodologies. In this paper we use a sparse identification procedure to obtain a nonlinear polynomial model. Since this is a NP-hard problem, a relaxed algorithm is employed to accelerate its convergence speed. The obtained model is further used inside the nonlinear Extended Prediction Self-Adaptive control (NEPSAC) approach to Nonlinear Model Predictive Control (NMPC), which replaces the complex nonlinear optimization problem by a simpler iterative quadratic programming procedure. An organic Rankine cycle system, characterized for presenting nonlinear time-varying dynamics, is used as benchmark to illustrate the effectiveness of the proposed combined strategies.