Development of a Genetic Fuzzy Controller and Its Application to a Noisy Inverted Double Pendulum

Abstract

Fuzzy logic is used in a variety of applications due to its universal approximator attribute and non-linear characteristics. The tuning of the parameters of a fuzzy logic system, viz. the membership functions and the rulebase, requires a lot of trial and error. This process could be simplified by using a heuristic search algorithm like genetic algorithm (GA). In this chapter, we discuss the design of such a genetic fuzzy controller that can control an inverted double pendulum. GA improves the fuzzy logic controller (FLC) with each generation during the training process to obtain an FLC that can bring the pendulum to its inverted position. After training, the effectiveness of the FLC is tested for different scenarios by varying the initial conditions. We also show the effectiveness of the FLC even when subjected to noise and how the performance improves when the controller is tuned with noise.

Toward Explainable AI—Genetic Fuzzy Systems—A Use Case

“…The transparency and explainability of that machine learning algorithm must also be of highest importance. Fuzzy logic is transparent, explainable and robust to noise [2]. As explainability in machine learning and artificial intelligence methods become the center of AI discussions, fuzzy logic must be present in solution discussions.…”