## 1 Introduction  
Distributed control systems (DCSs) have been widely adopted in the industrial sector to manage distributed electric power delivery processes. In particular, the DCS is often referred to as the \"brain\" and central control unit of coal-fired power generation systems [1]. The performance of a DCS fundamentally relies on two key factors: reliability and stability. These factors become especially critical in super-critical (SC) and ultra-supercritical (USC) power generation units, where system capacity can exceed 1000 MW. Such high-capacity systems demand enhanced reliability and operational consistency to meet performance requirements involving high parameters and efficiency levels [2]. As a result, operators and engineers routinely conduct preventive maintenance and inspections. However, overload conditions in the central processing unit (CPU)--the microprocessor responsible for core operations--are often difficult to detect through conventional diagnostic tools. Consequently, most manufacturers have historically addressed overload issues by simply expanding memory capacity, as memory shortages and I/O bottlenecks are commonly identified as primary contributors to such problems. For instance, Bailey, Inc., the manufacturer of the INFI-90 DCS system, adopted this approach by increasing memory capacity in its MFP modules [3]. In subsequent versions, the processing unit was rebranded as the Bridge Controller Card (BRC), which introduced additional features such as fiber channel support. Given the increasing complexity and performance demands of modern power generation systems, the role of intelligent control algorithms within the DCS framework has become more critical than ever. While hardware upgrades such as memory expansion and processor enhancements can temporarily mitigate overload issues, they do not address the root causes related to inefficient task scheduling, process prioritization, and data traffic control within the system. A well-designed control algorithm can dynamically allocate computing resources, predict potential overload conditions, and adjust control strategies in real time to maintain system stability. Moreover, in high-capacity environments such as USC power plants, where control response time and accuracy directly affect plant efficiency and safety, adaptive and predictive control algorithms can significantly enhance DCS reliability, responsiveness, and fault tolerance. Table 1 is presented to illustrate the evolution of hardware specifications between two generations of control modules--namely, the MFP and BRC units--used in legacy and modern DCS platforms. By comparing critical parameters such as microprocessor architecture, RAM, NVRAM capacity, and power requirements, the table highlights how hardware upgrades have historically been employed as a primary strategy to address system overload. However, despite these improvements, such enhancements are often reactive and limited in scalability. This comparison sets the stage for advocating the need for proactive solutions, such as the development of advanced overload control algorithms that can complement hardware capabilities by optimizing system behavior at the software level. In this case, the table highlights key differences in memory capacity--both NVRAM and RAM--while showing that both modules share the same microprocessor width and power consumption. The substantial increase in memory resources in the BRC module reflects a hardware-level response to growing system complexity and the need for improved performance in DCS. As a result of these advances in semiconductor processes and foundry technology, as well as the rapid design technology of processors, we have come to realize that they will also impact the design of future control systems, and that we need more advanced technologies to support them, and that we must find a better way. However, the technical requirements and performance of certain fields and some DCSs have become much higher than before.