Generalized predictive control with fuzzy soft constraints

This paper investigates the use of fuzzy decision making in predictive control. The use of fuzzy goals and fuzzy constraints in predictive control allows for a more flexible aggregation of the control objectives than the usual weighting sum of squared errors. Both equality and inequality constraints can be handled in a unified form, i.e., fuzzy soft constraints. Thus, the traditional constraints predictive control can be transferred to a standard fuzzy optimization problem. An inexact approach is used in this paper to obtain the fuzzy satisfaction optimal solution, instead of finding an exact unique optimal solution. A family of inexact solution with acceptable membership degree are found. Compared to the standard quadratic objective function, with the fuzzy decision making approach, the designer has more freedom in specifying the desired process behavior. Simulation results show the improvement of this approach when taking into account the constraints on the control or output signals