Predictive Functional Controller (PFC) with Novel Observer Method for Pneumatic Positioning System

Nowadays, the pneumatic system is more complex which leads to the development of an intelligent pneumatic system. Due to the difficulties in controlling the position and force of pneumatic actuators nonlinearities existed. This paper proposes a design of Predictive Functional Control (PFC) using two different types of observers such as full-order and reduced order as a novel method to come out with these issues. The mathematical model of the pneumatic system come from System Identification (SI) method and third order Auto-Regressive with Exogenous Input (ARX) has been chosen as a model structure. Matlab/Simulink has been utilized as the platform and the performance of the controller using both observers have been validated in simulation and real-time experiment. The comparison has been made to identify which observers are more efficient by taking into account the value of Steady State Error (Sse), Percentage of Overshoot (%Os), Settling Time (Ts) and Rise Time (Tr). Real-time experiment results show that the strategy using reduced-order observer is more efficient because this strategy can reduce more Sse