Many control schemes require significant tuning effort to achieve desired performance targets, causing a need for general tools that can perform controller tuning in an automated fashion. This paper represents a continuation of the previous work in which a tuning method capable of designing Explicit Model Predictive Controllers for control of nonlinear systems was developed. Besides demonstrating a broader applicability of the tuning method by applying it here to a non-optimization-based, nonlinear control policy, the primary purpose of this paper is to provide experimental validation of the method by its application to a physical system, as well as to extend the method's practical computational capability in case of multimodel plant uncertainty. The experimental results consist of the method's application to tuning of an anti-windup equipped PID controller which is designed to be robustly stable with respect to multimodel uncertainty in the considered mechanical experimental setup.