Adaptive Inertia Related Control for High Precision Tracking and Stability in Low-Cost Robot Arms

Abstract

This study presents the implementation and performance evaluation of an Adaptive Inertia-Related Controller (AIRC) designed for the joint-space control of a low-cost robotic manipulator. Addressing the limitations of traditional Proportional-Derivative (PD) controllers, particularly operating in dynamic uncertainty conditions and with low sensor-motor accuracy, the applied AIRC adaptively estimates real-time dynamic model parameters to achieve robust and precise trajectory tracking. The control strategy is implemented within a model-based adaptive framework, with Lyapunov stability analysis ensuring the asymptotic convergence of tracking errors. Advanced simulations and real-time experiments were conducted under the same conditions to evaluate the performance of AIRC compared to a standard PD controller. The experimental results confirm a significant improvement in tracking accuracy; specifically, the AIRC reduced the RMSE by approximately 13.6% and 12.9% for Joints 1 and 2, respectively, while achieving a 36.6% improvement for Joint 3 compared to the PD controller. This quantitative analysis validates the enhanced robustness of the AIRC, highlighting its potential as a cost-effective and high-performance robotic control system for industrial and research applications.