PRIME DIRECTIVE RPG STARFLEET SERIAL
A problem faced by conventional digital implementations of intelligent systems is the conflict between a distributed parallel structure on a sequential serial interface functionally degrading bandwidth and response time. Integration of on-board autonomous learning and adaptive control structures within a remote sensing platform architecture would substantially improve the utility of intelligence collection by facilitating real-time optimization of measurement parameters for variable field conditions.
Measurement and signal intelligence demands has created new requirements for information management and interoperability as they affect surveillance and situational awareness. With respect to the current state of the art, the specific outcomes of this project are: (i) algorithms for self-organizing control structure selection by mimicking the functioning of the cortical areas (ii) novel distributed controller design that mimics the ant colony (iii) transformative algorithms that increase robustness and autonomy of power systems through continuous adaptation powered by intelligent monitoring, cognition, and decision capabilities that mimics the immune system (iv) integrated and comprehensive intelligent multi-agent optimization framework for autonomous operation of the entire system that mimics the CNS (v) implementation and testing of the developed biomimetic control system design approach on an IGCC plant with CO 2 capture at an extended scale never attempted before.
The development and implementation of novel biomimetic methodologies for the control system design will contribute to the overall goal of establishing a biomimetic control framework for all levels in advanced power generation systems. Inspired by these distinct characteristics, this project seeks to develop methodologies and algorithms to accomplish: (i) self-organization of the control structure for maximizing the plant’s operating profit by mimicking the function of the cortical areas in the human brain, (ii) distributed and adaptive controllers that mimic the rule of pursuit present in ants, (iii) intelligent monitoring of the controllers powered with cognition and decision capabilities that mimic the artificial immune systems, and (iv) seamless coordination and integration in the control system that mimics the CNS. At the top, the central nervous system (CNS) integrates the information from and coordinates the activities of all parts of the bodies (for bilaterian animals). Self-organization, distributed intelligence, adaptability, intelligent monitoring, cognition, and decision capabilities are some of the powerful more » characteristics of the biological world that can be effectively utilized in process control. A number of characteristics distinguish biological systems from the traditional process control systems. Thus, power plant operations need to be agile and should adapt quickly to the dynamic requirements. The power plant operators are being pushed to the edge due to the penetration of renewable energy derived power to the grid, the demand for high efficiency, ever-tightening environmental regulations, and requirements for increased plant availability. In addition, the business models of today, especially for power plants, are rapidly changing. Thus, the knowledge learnt during process operation is lost or remains underutilized. While it is possible to adapt the process model based on available data, the control structure and the controllers rarely change. A number of variables are manipulated to accomplish disturbance rejection and/or servo control performance.
Traditionally, control systems have been designed based on assumed a priori knowledge of the process system. The objective of the project entitled “Development of Integrated Biomimetic Framework with Intelligent Monitoring, Cognition, and Decision Capabilities for Control of Advanced Energy Plants” is to develop algorithms and methodologies for designing a biomimetic control system for optimal control of advanced energy plants.
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