Fast computation of the quadratic programming subproblem in model predictive control

One of the main drawbacks of model predictive control (MPC) is that large MPC horizon times can cause requirements of excessive computational time to solve the quadratic programming (QP) minimization which occurs in the calculation of the controller at each sampling interval. This motivates the study of finding faster ways for computing the QP problem associated with MPC. In this paper, a new non-feasible active set method is proposed for solving the QP optimization problem that occurs in MPC, which can be some 10× faster than conventional existing active set methods, and to a primal-dual interior point method, using six representative linearized industrial control system examples.