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Ugolini; Yoshikawa, Shinji; Ozawa, Kenji
PNC TN9410 95-253, 13 Pages, 1995/10
This report presents the implementation of the a model reference adaptive control system based on the artificial neural network technique (MRAC
) in a fast breeder reactor (FBR) building block type (BBT) simulator representing the Monju prototype reactor. The purpose of this report is to improve the control of the outlet steam temperature of the three evaporators of the Monju prototype reactor. The connection between the MRAC system and the BBT simulator is achieved through an external shared memory accessible by both systems. The MRAC system calculates the demand for the position of the feedwater valve replacing the signal of a PID controller collocated inside the heat transport system model of the Monju prototype reactor. Two series of simulation tests havc been performed, one with one loop connected to the MRAC system (leaving the remaining two connected to the original PID controller), and the other with three loops connected to the MRAC system. In both simulation tests the MRAC system performed better than the PID controller, keeping the outlet steam temperature of the evaporators closer to the required set point value through all the transients.
Ugolini; Yoshikawa, Shinji; Ozawa, Kenji
PNC TN9410 95-210, 11 Pages, 1995/09
The proper control of the outlet steam temperature of the evaporator is of major importance for improving the overall performance of the balance of plant of a nuclear power reactor. This report presents a predictive and an anticipatory control algorithms based on the artificial neural network (ANN) technique. The two control algorithms are embedded on a model reference adaptive control system based on the ANN technique, defined as MRAC. It has already been illustrated that nonlinear dynamical systems such as the evaporator of a nuclear power plant can be controlled by an MRAC system. However, little attention has been devoted on exploiting the forecasting potential of the ANN technique for enhancing the accuracy and improving the efficacy of the control action of the MRAC system. The improved MRAC system has been tested to simulate the behavior of a fast breeder reactor (FBR) evaporator and to control its outlet steam temperature. The simulation results indicate that the performance of the MRAC system substantially improves when the predictive and the anticipatory control algorithms are activated.
Yoshikawa, Shinji; Ozawa, Kenji; ; Odo, Toshihiro
PNC TN9410 95-160, 18 Pages, 1995/06
This paper presents a study on knowledge dependency of plant operator behavior. The ultimate purpose of this study is to establish a methodology to support human operators in forming an integral understanding (i.e., mental model) of target plants, and thus to enhance potential performance in unexpected situations and in non routine cognitive tasks. The authors conducted a series of experiments to acquire behavioral data of two plant anomalies not included in the training curriculum. A formulation methodology of operator protocols has been proposed from the observations of the acquired behavioral data. It has been concluded that engaged plant operators have sufficient knowledge about physical phenomena of the major components, and that possible improvements of operators' cognitive performance can be expected mainly by knowledge enhancement about utilization strategy of physical information.
Muramatsu, Toshiharu
PNC TN9410 88-065, 123 Pages, 1988/06
The adaptive control system with SOC (Self-Organizing Controller) using the Fuzzy theory has been developed and implemented to the single-phase three-dimensional themal-hydraulic analysis code AQUA. The system constructs automatically the control rules and membership functions using evaluation of control performance. Therefore an efficiency of calculation is rapidly improved as decrease of calculation times. In a steady-state problem, total CPU time needed for a steady-state solution has been reduced by 0.7 times compared with the original Fuzzy controller without SOC. In a transient problem, total CPU time needed for simulation has been reduced by 0.9 times compared with the original Fuzzy controller without SOC. From above results, it has confirmed that the learning function using SOC is efficient construct for best tuning of adaptive control system using the Fuzzy theory.
Muramatsu, Toshiharu; Maekawa, I.*
PNC TN9410 87-130, 156 Pages, 1987/08
The adaptive control system using the Fuzzy theory has been developed and implemented to the single-phase three-dimensional themal-hydraulic analysis code AQUA. The system controls automatically the user specified values, like a time step size 
t and relaxation factor for matrix calculation using iterative solution into best ones in term of stability, accuracy and cost during an execution of the code. In control of a time step size t, the Fuzzy controller can give better results in accuracy and cost compared with the case of user specified value. For example, Total CPU time needed for a steady-state calculation has been reduced by 2/3 times under the fully implicit scheme. In control of relaxation factor 
, the Fuzzy controller can give better results in convergence and cost Compared with cases of user specified value and theoretical value using eigenvalue of coefficient matrix of a pressure equation. From above results, It has confiremed that the adaptive control system using the Fuzzy theory is efficient measure for best tuning calculation using a multi-dimensional thermal-hydraulic analysis code.
JAERI-M 4870, 71 Pages, 1972/07
no abstracts in English
