A move-blocking strategy to improve tracking in predictive control

One of the advantages of model predictive control is its ability to take optimal account of information about future setpoint changes in the design of the control law. However, it is easy to show that the default optimum GPC (Generalized Predictive Control) law that uses this information can lead to a deterioration rather than an improvement in performance. This paper discusses some simple modifications to overcome this problem. Specifically the focus here is on move-blocking approaches which seek to extend the effective input horizon while restricting the number of optimisation variables. A simple algorithm is proposed which allows optimal nominal performance and tracking without the undesirable effects arising from the standard approaches; hence the proposed modification always improves algorithm performance.