🤖 AI Summary
Traditional stiffness modeling methods struggle to simultaneously achieve accuracy, computational efficiency, and topological adaptability for complex hybrid-configured robotic manipulators featuring closed-loop kinematics, flexible links, rigid/elastic joints, and coupled preload–external load conditions. To address this, this paper proposes a Modular Stiffness Analysis (MSA) framework that integrates matrix structural analysis, screw theory, and finite-element discretization. It is the first work to systematically introduce a modular strategy into stiffness modeling, enabling flexible composition and rapid analytical derivation for diverse configurations—including rigid–flexible coupling and parallel topologies. The resulting global stiffness matrix achieves over 30% higher computational efficiency compared to conventional approaches, with modeling errors bounded by ≤5%. Comprehensive validation across multiple representative hybrid-architecture manipulators demonstrates both high accuracy and strong generalizability.