Exploiting Kautz functions to improve feasibility in MPC

This paper develops the recently published Laguerre MPC by proposing an alternative parametrization of the degrees of freedom in order to further increase the feasible region of model predictive control (MPC). Specifically, a simple but efficient algorithm that uses Kautz functions to parameterize the degrees of freedom in Optimal MPC is presented. It is shown that this modification gives mechanisms to achieve low computation burden with good feasibility and good performance. The improvements, with respect to an existing algorithm that uses a similar strategy, are demonstrated by examples.