A Neural-Network-Based Adaptive State-Observer for Pressurized Water Reactors

Pressurized water reactor (PWR) is now the most widely utilized nuclear reactor, and its safe, stable and efficient operation is meaningful to the current rebirth of nuclear fission energy industry. Power-level control is a crucial technique which can deeply affect the operation stability and efficiency of PWRs. Comparing with the classical power-level control, the advanced power-level control may strengthen both the closed-loop stability and control performance by feeding back internal state-variables. However, not all of the internal state variables of a PWR can be measured directly. To implement advanced power-level control laws, it is necessary to develop a state-observer to reconstruct the unmeasurable state-variables. Since a PWR is naturally a complex nonlinear system with its parameters varying with the power-level, fuel burnup, Xenon isotope production, control rod worth and etc, it is meaningful to design a nonlinear observer for the PWR with adaptability to system uncertainties. Due to this and the strong learning capability of the multilayer artificial neural network (MNN), an MNN-based nonlinear adaptive observer is given for PWRs. Based on Lyapunov stability theory, it is proved theoretically that this newly-built observer can provide bounded and convergent state-observation. This observer is then applied to the state-observation of a special PWR, i.e. the nuclear heating reactor (NHR), and numerical simulation results not only verify its feasibility but also show the relationship between the performance and parameter values of this newly-built observer.