Model Predictive Control and Structural Sparsity

When designing control systems, some applications require the designed controller to have a minimum number of actuators, sensors, and spent energy to decrease implementation and computational costs. In this work, we present a formulation of the Model Predictive Control (MPC) that includes a structural sparsity-inducing norm that easily allows for the incorporation of such requirements, taking into account knowledge that is not included in the model of the plant about the structural relationships of the variables of the system into the control design process. We explain the concept of structural sparsity in MPC, and present a study case based on temperature control of a four-room system to illustrate the benefits of implementing the proposed methodology.