Experimental testing of an adaptive model predictive controller on a quad-tank system

A recently proposed adaptive model predictive control algorithm is implemented in real time and its performance is verified experimentally on a quad-tank testbed. The algorithm relies on an iterative set membership identification procedure in order to provide a set of possible plant models at each time step. This set is then exploited by the model predictive controller in order to robustly enforce output constraints. The experimental results show that the algorithm provides good reference tracking performance while robustly satisfying output constraints for different operating conditions.