This paper demonstrates the effectiveness of applying constraints in a controller algorithm as a strategy to enhance the pneumatic actuator system’s positioning performance. The aim of the present study is to reduce the overshoot in the pneumatic actuator positioning system’s response. An autoregressive with exogenous input (ARX) model structure has been used to model the pneumatic system, while a model predictive control (MPC) has been employed as a control strategy. The input constraint has been applied to the control signals (on/off valves signals) to ensure accurate position tracking. Results show that the strategy with constraint effectively reduced overshoot by more than 99.0837 % and 97.0596 % in simulation and real-time experiments, respectively. Moreover, the performance of the proposed strategy in controlling the pneumatic positioning system is considered good enough under various loads. The proposed strategy can be applied in any industry that used pneumatic actuator in their applications, especially in industries that involved with position control such as in manufacturing, automation and robotics. The strategy proved to be capable of controlling the pneumatic system better, especially in the real-time environment.
A Novel Adaptive Neural Network Compensator as Applied to Position Control of a Pneumatic System
