A configurable process control method for robotic system-based industrial service improvements

Automation-based robotic solutions are widely employed in innovative industrial manufacturing units to avoid unnecessary process errors and delays in outcomes. The robots' density and controls vary with the process and production demands. This paper introduces a Centralized, Configurable Process Control (CCPC) method for reliable industrial task organization and management. The proposed method's configurable control identifies pre-queuing tasks and automated processing through robotic systems. This method uses state-dependent learning to identify the highly configurable robotic controller to improve the task processing rate. The automated, connected systems improve the task's cooperativeness by concurrent allocation and swapping the industrial tasks through self-decisions. The performance of the automated systems is verified based on state learning and concurrency. The results demonstrated substantial enhancements, with a 12.29% increase in processing rate, a 6.31% drop in mistakes, an 11.1% decrease in process latency, and an 11.68% decrease in pre-queuing ratio compared to conventional methods. These results show that the proposed strategy performs very well and is a cutting-edge option for effective job organization in smart industries. Response: The proposed Adaptive Backstepping Recurrently-Connected Fuzzy-Wavelet-Based Neural Network (ABRFWNN), Task-Level Performance (TLP+SS), and Closed Analytical Form (CAF+DLAM) were among the notable methods compared to prove CCPC's efficacy. The findings prove beyond a reasonable doubt that CCPC performs better than these other approaches, representing a significant improvement in the effectiveness of industrial robots.