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66 Arshad H. Malik and Feroza Arshadcontrol systems [3].Different types of neuro-fuzzy systems areused for different applications of identification,modeling, control, fault detection and expertsystems purposes. An Adaptive Network FuzzyInference System (ANFIS) neuro-fuzzy systemhas been reported in [1] for designing themaritime structures. In [3], an ANFIS-basedmodeling and feedforward control system hasbeen developed for shape memory alloyactuators. An ANFIS-based PID controller hasbeen developed for robot manipulator [4]. Aradial basic function neural network basedANFIS neuro-fuzzy controller has beenidentified for robotic manipulator application[5]. Another application of neuro-fuzzycontroller for the navigation of mobile robotusing artificial neural network based ontrigonometric series has been reported [6].Computational accuracy of ANFIS and ANN asnonlinear models and Auto-RegenerativeIntegrated Moving Average (ARIMA) as linearmodel for forecasting applications have beenanalyzed and presented [7].Neural networks have been extensivelyinvestigated in the context of systemidentification and simulation for nuclear researchreactor [8-10]. Recurrent type ANN has beenused for the prediction of flux and core power indifferent types of Pressurized Water Reactors(PWRs) [11, 12]. Ann attempt has been made fordesigning an intelligent Multi-Input-Multi-Output (MIMO) PWR core power controllerusing nuclear reactor code, recurrent type ANNand fuzzy system with fuzzifier and defuzzifier[13]. An improved intelligent MIMO PWRcontroller for axial off-set control usingrecurrent type ANN, fuzzy system withSingleton Fuzzifier, a Product Inference Engineand a Center of Gravity Defuzzifier (SF-PIE-CGD) has been reported [14]. A prediction ofChannel Power Distribution (CPD) for an IndianPHWR has been proposed [15]. Theidentification of nonlinear dynamics of PHWRhas been recently investigated using AdaptiveFeedforward Neural Network (AFNN) [16]; and,thus, a research effort has been put fordeveloping nonlinear models of different reactorsystem using feedforward neural networkoptimized by generalized delta rule withmomentum weight and bias learning rules. APPCS has been designed for Indian PHWR usingfuzzy logic controller [17]. This fuzzy controlleris a simple controller designed for feed andbleed system using seven triangular membershipfunctions only.In this research work, a new design approachis attempted for the successful replacement ofprimary pressure control system of a differentPHWR-type nuclear power plant. Our proposeddesign methodology is one step ahead fromresearch work reported [16, 17]. In this paper, anew MIMO hybrid intelligent adaptive neurofuzzysystem is designed based on theintegration of Adaptive Feedforward NeuralNetwork [16] and Mamdani-type FuzzyInference System (MFIS) in the frame work ofparallel learning of 24 each of triangular andtrapezoidal membership functions for inputs andforty five triangular and sixteen trapezoidalmembership functions for outputs. Therefore, aparallel learning and parallel data processing hasbeen adopted with clustering algorithm for theselection of membership functions, advancedintelligent soft computing and neuraloptimization for fuzzy rule reduction for adifferent PHWR-type nuclear power plant. TheMamdani adaptive neuro-fuzzy intelligentsystem is better than ANFIS in expression ofconsequent part and intuitive fuzzy reasoning.In this MOMO-HI system, an operator andimplication operator is product and aggregateoperator is sum and defuzzification operator iscentroid of area. Therefore, a compositeinference method is evolved which results inexcellent ability of learning because ofdifferentiability while soft intelligentcomputing. It resolves some difficulties thatare associated with other intelligent inferencesystems by providing multi-parametercomputational facility and easy weightassigning facility to each input and fuzzyrules. Thus, it has a great flexibility withmulti-parameter synthetic evaluation which isa main issue of T-S fuzzy inference system.The unique aspect of this control system is thehybrid intelligent critic which simulates anexpert’s operation in reality. Thischaracteristic increases the degree ofintelligence and robustness of the system andresults in self tuning and adaptive controllerdesign. This MIMO-HI has advantages innonlinear modeling, membership functions inconsequent parts, scale of training intelligentdata and amount of adjusted parameters. Thepower of proposed MIMO-HI system is it’sintuitive reasoning, widespread acceptanceand it’s well suited to human cognition whichare not achievable in ANFIS model.
Hybrid Neuro-Fuzzy 67MATERIALS AND METHODSPrimary Pressure Control System (PPCS)The primary pressure control system is one ofthe most important critical control system ofPHWR-type nuclear power plant [2]. Thepurpose of primary heat transport system is toremove generated heat from the nuclear fuel.The design philosophy behind this heat removalsystem is to avoid fuel meltdown in case of coredepressurization below 1427 psig and to avoidpiping system burst out in case of overpressurization above 1581 psig. Therefore, thepurpose of primary pressure control system is tomaintain pressure of the reactor coolant system( P P ) around the pressure set-point ( P Pset ) of1500 psig. There are two types of set-pointsinvolved in this multivariable control loop. Onefor primary pressure regulation ( P Pset ) based onPID control system and other for surge tanklevel control ( L set ) based on ON-OFF control.The main pressure control system is a feed-bleedsystem used to increase and decrease pressure ofprimary heat transport system. Feed-bleedcontrol system consists of two feed valves( FV 1 , FV2), two bleed valves ( BV 1 , BV2) andone spray valve ( SV ). Both feed and bleedvalves have split range control i.e. controltransfer from one valve to other valve in a bumplessmanner. Feed valves are provided toincrease the system pressure in case ofdepressurization while bleed valves are providedto decrease the system pressure in case of overpressurization. Spray valve is also known assecondary bleed. Spray valve is operated in caseof severe transients. Surge tank level controlsystem is a support control system of main feedbleedsystem. Surge tank is provided forpressure cushioning by regulating the surge tanklevel ( L V ). Surge tank ON-OFF control systemconsists of three heaters. These three heaters turnON-OFF at different threshold points. The blockdiagram of MIMO primary pressure controlsystem is shown in Fig. 1. The dynamics ofnuclear power plant is highly nonlinear innature. Therefore, a nonlinear control synthesisis a better solution for the replacement of aconventional control system of PHWR. Thus, ahybrid adaptive neuro-fuzzy system is selectedas an alternate modern design solution havingnonlinear structure. All the nonlinearities anduncertainties due to system ageing effects andparametric variations are embedded in the plantclosed loop dynamic operational data.Fig. 1. Block diagram of MIMO primary pressuresystem.In this paper, an adaptive neural network andMamdani fuzzy inference system are combinedto form Mamdani integrated adaptive neurofuzzysystem for control synthesis of primarypressure control of a PHWR-type nuclear powerplant. The basic set-up of integrated neuro-fuzzysystem is shown in Fig. 2.Fig. 2. Basic setup of integrated neuro-fuzzy system.Hybrid Intelligent System MethodologyMamdani adaptive neuro-fuzzy algorithmMamdani adaptive neuro-fuzzy intelligentsystem is an adaptive network based onMamdani fuzzy inference system. Mamdaniadaptive neuro-fuzzy intelligent system uses asupervised learning technique. The supervisedlearning is accomplished by adaptivefeedforward backpropagation learningalgorithm. The algorithm of adaptivefeedforward backpropagation neural networkis adopted from [16]. The Mamdani neurofuzzysystem consists of five layers. TheMIMO-HI adaptive neuro-fuzzy system isdesigned by decomposing the original MIMOsystem into five MISO adaptive neuro-fuzzysystems. The detailed architecture of two inputsand one output MISO Mamdani adaptive neurofuzzysystem is shown in Fig. 3. The designphilosophy of two inputs and one output MISOneuro-fuzzy intelligent system is explainedbelow:Form of ReasoningGeneralized Modus PonensPremise: u 1 is A 1Implication: if u 1 is A 1 then u 2 is B 1Consequence: y is C 1
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