Machine Learning-Based Robotic Control: A Dual Approach Using Linear and Support Vector Regression

Authors

• Rajender Radharam Architect

Abstract

:  The study on Data Robot Implementation explores the development, modeling, and performance evaluation of intelligent robotic systems that combine both traditional control methods and data-driven machine learning approaches. It explores the transition from continuous-time to discrete-time implementations, emphasizing challenges such as stability, computational delays, and measurement effects. The research uses linear regression (LR) and support vector regression (SVR) techniques to model and predict algorithm performance within a robotic system using parameters such as processing speed, sensor accuracy, energy consumption, and algorithm performance. Statistical analysis revealed high model accuracy, with R2 values ​​exceeding 0.98 for both methods, indicating exceptional predictive reliability. LR demonstrated simplicity and interpretability, while SVR demonstrated superior generalization and nonlinear mapping capabilities. Correlation analysis indicates strong positive relationships between system variables, confirming that improved processing capabilities and sensor accuracy significantly improve automation performance. This study underscores the effectiveness of integrating machine learning algorithms into robot control systems to improve automation outcomes, providing a foundation for future implementations in industrial, healthcare, and intelligent manufacturing environments. The results confirm that data-driven modeling provides a robust framework for predicting, adapting, and optimizing robot performance in complex operational environments.