IntroductionThe excessive uncertainty of in modern manufacturing systems is caused by machine failures, changes in material information, and other factors. In addition, the organizational production mode conflicts brought about by economic and technological development further exacerbate the perception of workshop interference in manufacturing systems.MethodIn order to further improve the adaptability of manufacturing systems, a control technique based on recursive control structure is proposed, which introduces an immune working mechanism to design the framework network of multi-agent manufacturing systems. Meanwhile, a negative selection algorithm is used to construct an antibody training system that considers perturbation problems.ResultThe results indicate that immune sensing nodes can effectively monitor manufacturing systems, reducing false alarm rates by over 4%. In the scheduling experiment, the completion time and equipment load improvement rate demonstrated by the research model were 3.29% and 12.38%, respectively. The production balance optimization rate exceeded 90%, far exceeding the results of traditional scheduling schemes, greatly improving the adaptive control capability of manufacturing system production.DiscussionThe regulatory approach proposed in this study can provide reference and assistance for improving the level of industrial production intelligence and establishing a sustainable economic system. However, the research results have not been applied to actual production processes, and the autonomy and coordination of intelligent manufacturing units in actual production processes still need to be further improved. In the future, research models and algorithms will be further explored in this area.