Unconstrained MPC tuning for prediction accuracy in Networked Control Systems

Model Predictive Control (MPC) has received wide attention from both the academic and industrial societies, mainly in the chemical industries, and is fast gaining popularity in Electro-Mechanical industrial systems. Great success stories of applications of MPC to industrial systems have been reported. However, the model based nature of this control methodology raises numerous questions of robustness, mainly towards prediction accuracy. A large amount of research has been conducted in MPC tuning, both mathematically inclined and heuristic. However, most of the works were intended at improving performance and robustness for control systems that are not interconnected over shared networks. In this paper, analysis of the standard tuning parameters of MPC and their effects on prediction accuracy are investigated, implications of such accuracy on Networked Control Systems (NCS) with random data packet dropouts are demonstrated by simulation and experimental studies.