Hybrid Control Strategies for High-Accuracy Robotic Manipulators

Abstract

Robotic manipulators are essential in modern fields such as industrial automation, precision manufacturing, medical robotics, and aerospace. While traditional control methods like PID, computed torque, and adaptive control perform well in structured environments, they struggle with nonlinearities, uncertainties, payload variations, and disturbances. To address these limitations, hybrid control strategies combining conventional and intelligent techniques—such as fuzzy logic, neural networks, and sliding mode control—have emerged as effective solutions. This work focuses on hybrid control approaches that enhance accuracy, robustness, and adaptability. It begins with dynamic modeling using the Euler-Lagrange formulation, highlighting the nonlinear and complex nature of robotic systems. The proposed method integrates computed torque control for trajectory tracking, fuzzy logic for handling uncertainties, and neural networks for adaptive tuning and parameter estimation. Literature indicates that hybrid methods like fuzzy-PID and neural-based adaptive control significantly improve tracking performance, reduce steady-state error, and enhance robustness, though challenges like computational complexity remain. Simulation and experimental results demonstrate that the proposed hybrid controller outperforms traditional methods in terms of tracking accuracy, settling time, and disturbance rejection. In conclusion, hybrid control strategies offer a powerful framework for high-precision robotic manipulation by effectively addressing nonlinearities and uncertainties. Future work will focus on real-time implementation, optimization of hybrid designs, and integration with advanced sensing technologies.