**6. Conclusions**

This chapter discussed the design of genetic fuzzy controllers to control an inverted double pendulum. While fuzzy logic by itself works well, tuning the parameters involved to satisfy a specific requirement might need a lot of trial and error to be done by the researchers. Incorporating GA to tune these parameters solves this problem. In this chapter, the objective was to bring the system to its inverted position. The time-integral cost function ensured that the FISs are trained to reduce the settling time.

The genetic fuzzy controller was able to stabilize the double pendulum at the inverted position starting from any initial position. The controller was tuned for two cases: (1) when there is no noise, and (2) when subjected to 5% noise. For each of the two cases, the results were shown for two sub-cases: (a) with zero initial angular velocities and (b) with non-zero initial angular velocities. The controller tuned for 5% has a better performance than the one tuned without noise. Tuning the controller with 5% noise improves the robustness of the system for a larger window of uncertainty. Since a lot of real-life systems suffer from measurement noise, it is important to develop robust controllers that can make decisions even when the inputs are noisy.
