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Home  >  Volume 21 (2012)

Investigation of Semidefinite Relaxation in Model Predictive Control Formulation by P.E. Orukpe. Vol. 21 pg 41 – 46
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Abstract

Model Predictive Control (MPC) is a control technique that is widely used in the industries. In the problem formulation of MPC, the constrained optimization problem subject to inequality constraints is a standard optimization problem, known as quadratic programming (QP). In this paper, we present a method to exploit semidefinite relaxation in MPC problems. In particular, we use the solution of semidefinite relaxation to solve MPC problems instead of QP. We also use the solution value of the semidefinite relaxation as a bound for the objective function. We implemented our method using Matlab and provide some numerical results for a system using the MPC algorithm we have developed.

Keywords: Model predictive control, linear matrix inequality, quadratic programming, semidefinite  relaxation,  linear systems, discrete-time systems.

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