Abstract
This paper presents a tutorial survey of model predictive control for constrained linear plants and nonlinear plants. A streamlined implementation is presented for constrained linear systems. The formulation is shown to be nominally stabilizing in the presence of constraints provided inconsistent state constraints are relaxed. Cases with incomplete state measurement and nonzero set points are also discussed. A constant output disturbance model is shown to provide offset free integral control. The differences between optimal control and model predictive control are illustrated with a stochastic control example. Nominal stability is proved for a class of nonlinear plants. The major topics of current research in the field are summarized.
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