ANFIS-based LQR Control for Rotary Double Parallel Inverted Pendulum

Abstract

This article explores two methodologies: Linear Quadratic Regulation (LQR) and the application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) on the Rotary Double Inverted Pendulum in Parallel Type (PRDIP) model. This model belongs to a class of underactuated robots, representing a nonlinear system with a mechanically simplistic configuration yet exhibiting considerable nonlinearity. Therefore, ANFIS is utilized to learn the input-output data, responses, and feedback of LQR. The response of the system's output to both LQR and ANFIS is compared to demonstrate the effectiveness of ANFIS in learning from the principles of LQR. This demonstration is supported through three cases: one simulation case and two experimental cases. Both control strategies are applied to the PRDIP system at the zero and -π positions, where one pendulum remains upright, and the other descends to counteract oscillations. The study presents simulation and experimental results to evaluate the points above comprehensively.