Modeling and Robust Control for Power Insurance Assessment SCARA Robots Using RAG Retrieval-Augmented Techniques

To address the challenges faced by SCARA (Selective Compliance Assembly Robot Arm) robots in power insurance evaluation and claims processing, particularly in accurately and rapidly tracking desired motion trajectories within specified joint angle ranges, this paper presents an integrated approach that combines an improved optimal robust control algorithm with Retrieval-Augmented Generation (RAG) technology. Initially, state transformation techniques are employed to ensure that the controlled output meets inequality constraints, thereby keeping the robot joints within the preset angle ranges and mitigating deviations caused by external disturbances. The control system integrates an optimal robust control algorithm comprising two core components: a nominal controller responsible for stabilizing the nominal robot system, and a robust controller based on the Lyapunov method designed to address tracking performance issues due to parameter uncertainties. The RAG technology plays a pivotal role by retrieving and generating supporting information from relevant data, significantly enhancing the adaptability and robustness of the control strategy. The stability of the system under uncertain conditions is verified using the Lyapunov min-max approach. The efficacy of the proposed algorithm is demonstrated through simulations and experimental validation on the SCARA robot platform. The proposed integrated method provides an effective solution for high-precision control of SCARA arms in complex environments and demonstrates potential for improving automation levels in power insurance evaluations, offering new insights and references for similar robotic control challenges.