  Control theory can play a pivotal role in tackling many of the global
challenges currently affecting our society, representing an actionable tool to
help policymakers in shaping our future. At the same time, for this to be
possible, elements of social justice must be accounted for within a control
theoretical framework, so as not to exacerbate the existing divide in our
society. In turn, this requires the formulation of new constraints and control
objectives and their integration into existing or new control design
strategies. Devising a formally sound framework to ensure social fairness can
enable a leap in the comprehension of the meaning of such goals and their
non-trivial relation with the usual notion of performance in control. In this
new and challenging context, we propose Fair-MPC, an economic model predictive
control scheme to promote fairness in control design and model-based
decision-making. Moving beyond traditional measures of performance, Fair-MPC
enables the integration of social fairness metrics, articulated in equality and
equity, allowing the design of optimal input sequences to trade-off fairness
and efficiency, which are often in conflict. By establishing a theoretical link
between Fair-MPC and the standard model predictive control framework (quadratic
cost and linear constraints), we provide insights into the impact of social
justice objectives on the final control action, which is ultimately assessed
along the efficiency of the controller over two numerical examples.
