  We propose model-plant mismatch learning offset-free model predictive control
(MPC), which learns and applies the intrinsic model-plant mismatch, to
effectively exploit the advantages of model-based and data-driven control
strategies and overcome the limitations of each approach. In this study, the
model-plant mismatch map on steady-state manifold in the controlled variable
space is approximated via a general regression neural network from the
steady-state data for each setpoint. Though the learned model-plant mismatch
map can provide the information at the equilibrium point (i.e., setpoint), it
cannot provide model-plant mismatch information during the transient state.
Moreover, the intrinsic model-plant mismatch can vary due to system
characteristics changes during operation. Therefore, we additionally apply a
supplementary disturbance variable which is updated from the disturbance
estimator based on the nominal offset-free MPC scheme. Then, the combined
disturbance signal is applied to the target problem and finite-horizon optimal
control problem of offset-free MPC to improve the prediction accuracy and
closed-loop performance of the controller. By this, we can exploit both the
learned model-plant mismatch information and the stabilizing property of the
nominal disturbance estimator approach. The closed-loop simulation results
demonstrate that the developed scheme can properly learn the intrinsic
model-plant mismatch and efficiently improve the model-plant mismatch
compensating performance in offset-free MPC. Moreover, we examine the robust
asymptotic stability of the developed offset-free MPC scheme, which is known to
be difficult to analyze in nominal offset-free MPC, by exploiting the learned
model-plant mismatch information.
