  Economic model predictive control and tracking model predictive control are
two popular advanced process control strategies used in various of fields.
Nevertheless, which one should be chosen to achieve better performance in the
presence of noise is uncertain when designing a control system. To this end, a
sensitivity-based performance assessment approach is proposed to pre-evaluate
the dynamic economic and tracking performance of them in this work. First,
their controller gains around the optimal steady state are evaluated by
calculating the sensitivities of corresponding constrained dynamic programming
problems. Second, the controller gains are substituted into control loops to
derive the propagation of process and measurement noise. Subsequently, the
Taylor expansion is introduced to simplify the calculation of variance and mean
of each variable. Finally, the tracking and economic performance surfaces are
plotted and the performance indices are precisely calculated through
integrating the objective functions and the probability density functions.
Moreover, boundary moving (i.e., back off) and target moving can be
pre-configured to guarantee the stability of controlled processes using the
proposed approach. Extensive simulations under different cases illustrate the
proposed approach can provide useful guidance on performance assessment and
controller design.
