Collaborative control of multi-manipulator systems in intelligent manufacturing based on event-triggered and adaptive strategy - Leng Xuefeng (Nonlinear Engineering)
Multi-manipulator systems face challenges in coordinating their movement in complex environments. To enhance the collaborative control stability of multi-manipulator systems in intelligent manufacturing, this study utilizes event-triggered (ET) mechanisms to reduce the signal transmission burden and frequency, and combines adaptive strategy to solve interference factors in complex environments. In addition, the study combines adaptive strategy with neural network structure using adaptive neural network control methods, and adopts ET mechanism to design auxiliary variables. Then, the neural network approximates the nonlinear uncertain model of the system online to cope with external disturbances and improve the robustness. From the results, the maximum fitting error of the multi-manipulator system based on ET and adaptive strategy was 0.59%, which was 2.05 and 3.99% lower than the errors of the other two advanced control systems, respectively. In summary, the research on multi-manipulator systems in intelligent manufacturing on the basis of ET and adaptive strategy effectively improved its control stability in intelligent manufacturing.