Worst‐case performance analysis of optimal batch control trajectories

In open‐loop optimal control, inputs to a dynamic system are computed that optimize a specified performance criterion. A novel approach is proposed that quantifies the impact of parameter and control implementation inaccuracies on the performance of open‐loop control policies. Such information can be used to decide whether more laboratory experiments are needed to produce parameter estimates of higher accuracy, or to define performance objectives for lower‐level control loops that implement the optimal control trajectory. The novel features of the approach include (1) computational efficiency; (2) the parametric uncertainty description, which is produced by most process identification algorithms; and (3) addressing of control implementation inaccuracies. The merits of the approach are illustrated on a batch crystallization process.