Comparison of nonlinear predictive control methods for a waste-water treatment benchmark

Three nonlinear predictive control algorithms were applied in simulation to a MIMO waste-water treatment process. Two algorithms are based on instantaneous linearization of the nonlinear prediction model and one is based on a branch-and-bound search technique. The prediction model employed is a fuzzy model of the Takagi-Sugeno type. The performance of the controllers is compared in terms of setpoint tracking, control effort and computational costs.