Implementation of Sliding Surface Moving Anfis Based Sliding Mode Control to Rotary Inverted Pendulum

Abstract

This study covers the control of the pendulum angle by taking into account the dynamic equations and motor dynamics of the rotary inverted pendulum system, with the help of state variables in the Matlab program, by using the sliding mode control method with sliding surface moving and the adaptive neural fuzzy inference system together. The sliding mode control method with a changing sliding surface is a part of the control structure. The slope of the sliding surface was calculated using the adaptive neural fuzzy inference technique. The optimum values of the coefficients in the adaptive neural-fuzzy inference system structure have been calculated by genetic algorithm. The finding of the coefficients, the sum of the squares of the errors chosen as the objective function. The input of the adaptive neural fuzzy inference system structure consists of the error of the pendulum and the derivative of the error of the pendulum. The gradient of the sliding surface of the sliding mode control structure is the output of the adaptive neural fuzzy inference system structure. According to the findings, the pendulum angle achieved the appropriate reference value after 1.5 seconds, with an error of around zero. It obtained that the engine torque value reaches up to 50 Nm. From here, it is seen that the motor torque values used in practical applications and the motor torque values as a result of this study overlap.

Enhancing Underwater Robot Manipulators with a Hybrid Sliding Mode Controller and Neural-Fuzzy Algorithm

“…This study addresses the challenges in controlling underwater robotic arms, stemming from the dynamic aquatic environment and the limitations of traditional control methods. It introduces a novel solution combining Sliding Mode Control (SMC) and Fuzzy Neural Network (FNN) to enhance flexibility, precision, and adaptability of the control system [22][23][24]. SMC effectively manages rapid environmental changes, while FNN optimizes the system based on real-time conditions, leading to a more accurate and stable robotic arm control [25] in unpredictable underwater settings.…”