Abstract
Model predictive control (MPC) is a very popular controller design method in the process industry. One of the main advantages of MPC is that it can handle constraints on the inputs and outputs. Usually MPC uses linear discrete-time models. Recently, we have extended this framework to max-plus-linear discrete event systems. In this paper, we further explore this topic. More specifically, we focus on implementation and timing aspects, closed-loop behavior and tuning rules for model predictive control of max-plus-linear (MPL) systems.
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