Tuning of PI–PD controller using extended non-minimal state space model predictive control for the stabilized gasoline vapor pressure in a stabilized tower

• A new PI–PD control based on extended state space predictive control optimization is proposed.
• The design is applied to stabilized gasoline vapor pressure in a stable tower.
• Improved control performance is achieved under model/plant mismatches.

Nonlinearities, uncertainties and large time delay widely exist in the industrial processes, which pose great difficulty to traditional proportional-integral-derivative (PID) control. Proportional-integral–proportional-derivative (PI–PD) controller, which is a modified form of PID controller, can get a better performance than PID control. However, due to the fact that PI–PD controller has an extra parameter, the difficulty of parameter adjustment is a major problem. In this paper, a new PI–PD method optimized by the extended non-minimal state space model predictive control (ENMSSMPC) is proposed to get the optimal parameters of PI–PD controller for dealing with the process complexity. The proposed PI–PD controller combines the advantage of ENMSSMPC and the simple structure of the PID controller and is tested on the stabilized gasoline vapor pressure in a stabilized tower. Results show that it outperforms traditional controllers such as PID controller, PI–PD controller, traditional model predictive control based PID and PIPD controllers (TPFC-PID and TPFC-PIPD) and IMC based Robust PID controller (Robust IMC-PID).