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A Sparse and Condensed QP Formulation for Predictive Control

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The computational burden that model predictive control (MPC) imposes depends to a large extent on the way the optimal control problem is formulated as an optimization problem. When employing existing dense formulations the resulting optimization problem is small but the amount of computation grows cubically with the horizon length (one of the parameters of the control problem) due to dense matrix inversions. Existing sparse formulations result in larger problems that can be solved in time linear in the horizon length when exploiting the structure. In this talk, I will present a new formulation that uses a simple mathematical trick to introduce structure into the problem leading to compact and sparse optimization problems. The amount of computation required to solve the resulting optimization problem is smaller than with any of the two existing approaches for almost all control problems.

This talk is part of the CAS Talks series.

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