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Low-Complexity First-Order Constraint Linearization Methods for Efficient Nonlinear MPC


G. Torrisi, S. Grammatico, D. Frick, T. Robbiani, R. S. Smith, M. Morari

IEEE Conference on Decision and Control, Accepted

In this paper, we analyze first-order methods to find a KKT point of the nonlinear optimization problems arising in Model Predictive Control (MPC). The methods are based on a projected gradient and constraint linearization approach, that is, every iteration is a gradient step, projected onto a linearization of the constraints around the current iterate. We introduce an approach that uses a simple `p merit function, which has the computational advantage of not requiring any estimate of the dual variables and keeping the penalty parameter bounded. We then prove global convergence of the proposed method to a KKT point of the nonlinear problem. The first-order methods can be readily implemented in practice via the novel tool FalcOpt. The performance is then illustrated on numerical examples and compared with conventional methods.


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% Autogenerated BibTeX entry
@InProceedings { TorEtal:2017:IFA_5728,
    author={G. Torrisi and S. Grammatico and D. Frick and T. Robbiani and R. S.
	  Smith and M. Morari},
    title={{Low-Complexity First-Order Constraint Linearization Methods
	  for Efficient Nonlinear MPC}},
    booktitle={IEEE Conference on Decision and Control},
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