Abstract
This paper provides a review of computationally efficient approaches to nonlinear model predictive control. The methods considered cover the following areas: tailoring of nonlinear programming algorithms to the structure of the online optimization, use of the optimal control formulation of the receding horizon problem, constraint and cost approximations based on state space partitioning, and reparameterization of the degrees of freedom in predictions.
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