Real-time model predictive control for nonlinear gas pressure process plant

Nonlinear behaviour of the systems happens to be a common problem in industrial processes. They cause a large amount of time, resources and efforts to be utilized in order to deal with them. A Major hurdle in Nonlinear Industrial Processes is system modeling. Due to this reason, several methods and techniques have been designed and developed in order to improve the overall control performance in industrial process control. Model based controllers have been developed and implemented on various applications with promising results. Their main benefit is they can identify and tune unknown system parameters in real-time. This paper focuses on real-time controller development and its implementation on Gas Pressure Process Plant using MPC. MPC is considered to be one of the robust and effective controllers due to impressive control performance in different applications previously. MPC makes use of a model for system identification and based upon that, it can dynamically send next control move for the system. This research work incorporates State-Space Model for unknown system-parameter identification. The identified parameters will be utilized by MPC for control law development. The proposed methodology is validated by real-time experimental results on the aforementioned system.