  Consider the problem of control selection in complex dynamical and
environmental scenarios where model predictive control (MPC) proves
particularly effective. As the performance of MPC is highly dependent on the
efficiency of its incorporated search algorithm, this work examined hill
climbing as an alternative to traditional systematic or random search
algorithms. The relative performance of a candidate hill climbing algorithm was
compared to representative systematic and random algorithms in a set of
systematic tests and in a real-world control scenario. These tests indicated
that hill climbing can provide significantly improved search efficiency when
the control space has a large number of dimensions or divisions along each
dimension. Furthermore, this demonstrated that there was little increase in
search times associated with a significant increase in the number of control
configurations considered.
