Self-Healing and Auto-Responsive Coordinated Control Systems: Leveraging AI and Real-Time Data for Optimal Asset Operations

In current times where automation, IoT, and AI are converging, the need for more intelligent, coordinated and resilient systems has become more important than ever. This paper introduces an innovative framework for self- healing, auto-responsive coordinated control systems that utilize real-time and historical data in combination with artificial intelligence to optimize asset management and operations. The proposed system discussed in this research paper consists of a multi-layered architecture, with an edge gate- way layer for localized data collection and instantaneous response, a supervisory layer for predictive analytics and coordinated control, and an enterprise AI layer that offers long-term tactical management. When technologies such as edge analytics, machine learning, and AI-driven decision- making are combined, the systems deploying them can self- diagnose and correct anomalies, predict failures efficiently, and optimize resources autonomously. This process reduces downtime and operational costs by a substantial margin while also increasing overall efficiency. There are several key features to factor in, such as, real- time data collection, anomaly detection, predictive maintenance, and centralized control policies which are deployed across operational layers in a coordinated manner.