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By combining manual welders (with intelligence and versatility) and automatic welding systems (with accuracy and consistency), an intelligent welding system for human soft tissue welding can be developed in medicine. This paper presents a data-correction control approach to human welder intelligence, which can be used to control the automated human soft tissue welding process. Human soft tissue welding can preconnect the excised tissue, and the shape of the tissue at the junction ensures the recovery of the operative organ function. This welding technology has the advantages of rapid operation, minimal tissue damage, no need for suture materials, faster recovery of the mechanism and properties of the living tissue, and the maintenance of the function of the organs. Model of the welding system is identified from the data; an open-closed-loop iterative learning control algorithm is then proposed to improve the tracking accuracy of the system. The algorithm uses the tracking error of current and previous to update the control law. Meanwhile, to further improve the accuracy under the conditions of external interference, a system correction term is added to the proposed ILC algorithm, which can be adjusted according to the system’s errors and output and improve the capability of the target tracking greatly. A detailed convergence analysis for the ILC law has been given. Simulation results verify the feasibility and effectiveness of the proposed method for GTAW control tasks.

Open-Closed-Loop Iterative Learning Control with the System Correction Term for the Human Soft Tissue Welding Robot in Medicine