Robust nonlinear predictor for dead-time systems with input nonlinearities. (March 2015) - Lima, Daniel Martins; Santos, Tito Luís Maia; Normey-Rico, Julio Elias
Abstract : Highlights: A nonlinear predictor for dead-time systems with input nonlinearities is proposed. It is shown that optimal predictors robustness is dependant of the nominal dead-time. It is shown that the proposed predictor is independent of the nominal dead-time. The proposed predictor can also improve robustness if properly tuned. Two examples and a case study are presented to corroborate the theoretical results. Abstract: Since the seminal work of Smith, predictor structures have been used to control processes with dead-time. Predictors allow the control of this type of process with a delay-free nominal model, which simplifies the control design. In this paper, a nonlinear filtered Smith predictor (NLFSP) structure is proposed for systems with input nonlinearities. It is shown that the NLFSP maintains the characteristics of the linear filtered Smith predictor and that with appropriate tuning, it can increase the robustness of the closed-loop system or improve the disturbance rejection response. The NLFSP is applied to a simulated CSTR process to demonstrate these characteristics.