Analytical non-linear model predictive control for hybrid systems with discrete inputs only

Abstract

Model predictive control (MPC) is known as an efficient technique for controlling different industrial processes. However, the computation load remains the main challenge facing the real-time application of the MPC technique especially for complex systems, like, for example, hybrid systems with discrete or binary variables. In this study the authors propose an analytical non-linear model predictive control (NMPC) technique for hybrid systems with discrete inputs only. The proposed controller has lower computation complexity compared to other techniques presented in the literature; as a result real-time implementation is turned to be possible for several systems. The proposed analytical NMPC controller can be applied efficiently for different classes of hybrid systems: switching systems, linear hybrid systems, non-linear hybrid systems and constrained systems. The proposed technique is validated through several examples representing different classes of hybrid systems with discrete inputs.