  Model predictive control (MPC) has become the most widely used advanced
control method in process industry. In many cases, forecasts of the
disturbances are available, e.g., predicted renewable power generation based on
weather forecast. While the predictions of disturbances may not be accurate,
utilizing the information can significantly improve the control performance in
response to the disturbances. By exploiting process and disturbance models,
future system behaviour can be predicted and used to optimise control actions
via minimisation of an economical cost function which incorporates these
predictions. However, stability guarantee of the resulting closed-loop system
is often difficult in this approach when the processes are nonlinear. Proposed
in the following article is a contraction-constrained predictive controller
which optimises process economy whilst ensuring stabilisation to operating
targets subject to disturbance measurements and forecasts.
