Model-Driven to Intelligence-Augmented: Evolution of Control Algorithms for Industrial Robotic Manipulators

Amidst the global proliferation and sustained expansion of industrial robotics, the advancement of robot performance stands as a paramount scientific and technological pursuit commanding significant international attention. Enhancing robotic capabilities is not only pivotal for augmenting productivity but also constitutes a critical technology underpinning national strategic initiatives and industrial transformation. This paper reviews the enduring challenges in high-precision control of industrial manipulators, specifically examining four key issues: the tension between uncertainty robustness and real-time processing constraints; excessive latency in servo drive responses; inefficient energy recuperation from gravitational potential, which compromises overall system efficiency; and trajectory tracking inaccuracies arising from strong kinematic joint coupling inherent in conventional PID control frameworks. By rigorously examining the mathematical formulations and hardware implementation methodologies underpinning three core control algorithms PD control with gravity compensation, inversion control, and adaptive control this work quantifies the constraint boundaries imposed by these controllers on servo drive parameters. Furthermore, it proposes a holistic Pareto-optimization strategy targeting the energy consumption-precision-real-time performance triad. Finally, the paper outlines the emerging research trajectory of employing digital twin platforms for validating learning-enhanced backstepping control methodologies.