Belle R Upadhyaya

Advanced automation concepts for large-scale systems

An automatic control system for large-scale systems that integrates methods in artificial intelligence, signal processing, and nonlinear control to provide fast and efficient diagnostics and reliable control is presented. The integrated system reduces the procedural load and facilitates the operator tasks by creating a condensed representation of plant status. Operator tasks are emulated by building computer-based algorithms which validate sensor signals, strategies, commands, and performance tracking and which generate reliable decisions and control actions. The advanced concepts on which the system is based are discussed. Also discussed are fault tolerance, signal and command validation, nonlinear control, and the system executive module. An application of the integrated control system to the Experimental Breeder Reactor-II (EBR-II) is described. The simulation results show that the advanced concepts yield efficient control strategies, including reactor control during startup.<<ETX>>

Expert system driven fuzzy control application to power reactors

The authors describe a fuzzy logic application to nuclear reactor control, namely, the automated start-up control of the Experimental Breeder Reactor-II. A rule-based expert system supervises the fuzzy controller (which also uses a rule-based representation). The overall system successfully emulates the behavior of the actual power plant operator. The operator control strategy is based on linguistic statements which translate into fuzzy productions describing heuristic control rules. The developed methodology is verified through computer simulations using a valid nonlinear model of the reactor. The necessary heuristic decisions, which are vitally important for the implementation of fuzzy control in the actual plant, were identified.<<ETX>>