Modelling and Control of 6- Degrees of Freedom movement Using Fuzzy Logic

After the 20th century, the automotive industry is experiencing significant growth. This paper presents the design of a robotic arm capable of emulating the dexterity of the human hand, facilitating object manipulation in laboratory, industrial, or hazardous environments with 6 degrees of freedom (6-DOF). To analyse torque characteristics, a humanoid robot arm model is employed, simulating tasks such as lifting and transferring the objects. Current robotic hands often lack full hand functionality, limiting their use in environments tailored for human interaction. Acquiring high reliability trajectory tracking remains a formidable obitual in the field of industrial robot control, primarily due to nonlinearities and input couplings inherent in robot arm dynamics. In this we are focuses on the modelling and control of a 6-degree of freedom (DOF) robot arm, progressing through five key developmental stages. Initially, a comprehensive computer-aided design (CAD) model of the 6-DOF robot arm is developed. Subsequently, the CAD model is translated into a physical model using Sim Mechanics Link. The core of the paper involves applying a Neuro-Fuzzy Controller to the robot arm, known for its adaptability in handling complex and nonlinear systems. The controller implementation, simulations are conducted using MATLAB/Simulink, a robust platform for dynamic system analysis. The performance evaluation compares the Neuro-Fuzzy controller against a linear controller across key metrics: rise time, percentage overshoot, settling time, and steady-state errors. The findings indicate that the Neuro-Fuzzy controller outperforms the linear controller significantly in all measured characteristics. This underscores its suitability for enhancing trajectory tracking precision in industrial robotic applications.