Design of multi-input multi-output hybrid adaptive neuro-fuzzy intelligent system for primary pressure control system of pressurized heavy water reactor

A new multi-input multi-output hybrid intelligent (mimo-hi) system is designed for primary pressure control system (ppcs) of a highly non-linear pressurized heavy water reactor (phwr)-type nuclear power plant (npp). the mimo hybrid intelligent system is a nonlinear control system which is synthesized using adaptive feedforward neural network (afnn) and mamdani-type fuzzy inference system (mfis). in the proposed mamdani adaptive neuro-fuzzy system (manfs),a supervised learning technique has been adopted using afnn for learning the design parameters of membership functions. a clustering algorithm (ca) is used to select the initial number and type of membership functions with each variable. a reduced order nonlinear primary pressure control system is developed based on neural optimization for rule reduction. the mimo- hi system is designed based on five multi-input single-output (miso) adaptive neuro-fuzzy systems in the framework of parallel learning and parallel data processing. these five miso adaptive neuro-fuzzy systems are designed for intelligent predictions of two feed valves,two bleed valves and one spray valve positions of a ppcs of an operating phwr-type npp in pakistan. a severe transient has been imposed in training,validation and testing phases with proposed mimo-hi system. the proposed mimo-hi system is designed in matlab and a graphical user interface (gui) is developed for variables transfer and simulations in visual basic. the designed mimo-hi system is observed to be extremely efficient,robust,with reduced oscillations and better transient and steady state characteristics. the performance of the designed mimo-hi system is tested and the estimated results are in agreement with conventional pidtype ppcs of phwr. © pakistan academy of sciences.