Evaluation of a new algorithm for model predictive control based on non-feasible search directions using premature termination

Model predictive control (MPC) is an attractive control methodology because it can deal with constraints directly. Unfortunately however, it is often not possible to implement the strategy, because large MPC horizon times can cause requirements of excessive computational time in solving the quadratic programming (QP) optimization that is necessary at each sampling interval. This motivates the study of developing more effective algorithms for solving QP problems, and of using approximate solutions to the QP problem associated with MPC. In this paper the premature termination of algorithms which solve the QP subproblem are evaluated experimentally using three different algorithms: a proposed new active set method, a conventional existing active set method and a primal-dual interior point method. Results from three representative linearized industrial control system examples are reported on in this paper. Favorable results are found using the proposed new active set method.