Abstract
Abstract Robotic analysis uses mathematical modeling and digital techniques to represent and study mechanical, electrical, and computational systems. Combining these models with physical robots enhances their functions, and artificial intelligence adds intelligence, enabling robots to perform more complex tasks. Inverse dynamics is a key component, mapping joint positions, velocities, and accelerations to the torques needed for accurate motion. This study presents a unified framework that integrates inverse dynamics learning, adaptive control, multi objective optimization, stability verification, and sensorless torque estimation for a 6 DOF UR5 manipulator. A structured dataset representing robot motion with controlled noise and uncertainty conditions is used to train three learning models: Deep Neural Networks, Type 2 Fuzzy Systems, and Gaussian Process Regression. The results show that Gaussian Process Regression achieves the highest torque prediction accuracy, with RMSE of 7.87 Nm and R2 of 0.892, closely followed by the Deep Neural Network (RMSE 11.90 Nm, R2 0.753). Integration with an adaptive controller reduces the trajectory tracking error to 0.0473 rad, which is further reduced to 0.0238 rad using Particle Swarm Optimization, alongside a 5.41% reduction in control energy. Stability analysis using Lyapunov and frequency domain methods confirms bounded, stable closed-loop tracking behaviour, while sensorless torque estimation improves prediction accuracy by 70%. The proposed framework provides a clear methodological foundation for learning-based inverse dynamics modelling and its integration with robotic control systems, and future work will extend the framework to real-time implementation on physical robotic systems.