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

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1 Author(s)
J. Thomas ; Department of Electrical Engineering, Linkoping University, S-581 83 Linkoping, Sweden

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.

Published in:

IET Control Theory & Applications  (Volume:6 ,  Issue: 8 )