Fractional Generalized Predictive Control Strategy With Fractional Constraints Handling

The control strategies based on the methodology known as Model–based Predictive Control (MPC) have been developed and widely adopted to control real plants. This is mainly due to their intrinsic ability to handle constrains and their capacity to predict and optimize the future behavior of the process using a dynamical model of the plant. On the other hand, the mathematical tool known as fractional calculus has been currently used for reformulating the predictive control strategies to reach a better performance adding new control parameters. This work extends the use of fractional operators for the constraints in one type of fractional predictive control strategy known as Fractional–order Generalized Predictive Control (FGPC), interpreting and discussing the results. In addition, a new method to soften constraints using fractional operator is proposed and illustrated with examples, even to adjust the final response of the system. A practical tuning of the rest of controller parameters with the help of a well–known mathematical software is also included to make use of the beneficial characteristics of this fractional predictive formulation.