Open AccessEnhancement in pneumatic positioning system using nonlinear gain constrained model predictive controller: experimental validation
Author(s) -
Siti Zubaidah Sulaiman,
M. F. Rahmat,
Ahmad Athif Mohd Faudzi,
Khairuddin Osman,
Noorhazirah Sunar
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v23.i3.pp1385-1397
Subject(s) - control theory (sociology) , model predictive control , overshoot (microwave communication) , pneumatic actuator , nonlinear system , controller (irrigation) , control engineering , actuator , engineering , control system , pid controller , system identification , observer (physics) , positioning system , computer science , control (management) , temperature control , artificial intelligence , agronomy , physics , electrical engineering , software engineering , quantum mechanics , data modeling , biology , structural engineering , node (physics)
The issues of inaccurate positioning control have made an industrial use of pneumatic actuator remains restricted to certain applications only. Non-compliance with system limits and properly control the operating system may also degrade the performance of pneumatic positioning systems. This study proposed a new approach to enhance pneumatic positioning system while considering the constraints of system. Firstly, a mathematical model that represented the pneumatic system was determined by system identification approach. Secondly, model predictive controller (MPC) was developed as a primary controller to control the pneumatic positioning system, which took into account the constraints of the system. Next, to enhance the performance of the overall system, nonlinear gain function was incorporated within the MPC algorithm. Finally, the performances were compared with other control methods such as constrained MPC (CMPC), proportional-integral (PI), and predictive functional control with observer (PFC-O). The validation based on real-time experimental results for 100 mm positioning control revealed that the incorporation of nonlinear gain within the MPC algorithm improved 21.03% and 2.69% of the speed response given by CMPC and PFC-O, and reduced 100% of the overshoot given by CMPC and PI controller; thus, providing fast and accurate pneumatic positioning control system.