Fuzzy Inference Mechanism Research Articles

Data-driven ergonomic assessment of construction workers

Automating ergonomic assessment improves both objectivity and cost-effectiveness. However, existing ergonomic scales remain susceptible to inaccuracy and insensitivity when assessing diversified construction activities. This susceptibility stems from the unreliable ranges, sharp boundaries, and oversimplified binary rules within conventional joint-level assessment rules, coupled with the restricted transformation rules to integer input/output. This paper presents a data-driven ergonomic assessment method grounded in statistical data from Construction Motion Data Library (CML) dataset, comprising: 1) Developing joint-level scoring models to primarily improve accuracy through leveraging Heuristics Gaussian Cloud Transformation (H-GCT), and 2) Designing fuzzy inference mechanism to enhance sensitivity through retaining risk information during transformation across levels. Subsequently, a three-step validation confirms accurate risk identification, sensitive risk feedback, and positive correlation with previous methods, contributing to occupational health and safety management. Future research could enhance the method by expanding the pose dataset, recruiting more participants, and refining the 3D pose estimation.

Light but fruitful: enhanced fuzzy inference via weight-guided selection of rules with attribute weights

Inference using fuzzy rules enables decision-making that is supported with imprecise knowledge. Unlike conventional fuzzy reasoning approaches which directly perform pattern-matching in response to an input observation, recent techniques have integrated rule-firing-based and rule interpolating-based inference methods. This is in order to address challenging issues where observations are of different matching degrees to the rules within a given rule base, including unmatched ones. While applied generally, such a unified inference mechanism may become too complex to exploit the entire rule base for deriving a reasonable conclusion. In practice, only a small number of ‘appropriate’ rules are selected to accomplish the required inference. This paper presents an enhanced integrated fuzzy inference mechanism, which is fed with fewer rules returned by a weight-guided selection procedure. In particular, the weights of rule attributes are utilised in a dual manner: guiding the selection of appropriate rules for rule firing and determining the nearest neighbouring rules for rule interpolation. The resulting mechanism is applied to a real-world problem, empirically demonstrating its significant efficacy.

Hybrid coordination scheme based on fuzzy inference mechanism for residential charging of electric vehicles

The charging of electric vehicles (EVs) at residential premises is orchestrated through either centralized or decentralized control mechanisms. The former emphasizes adherence to power grid constraints, employing demand management techniques to restrict EV charging when the aggregated demand exceeds a predetermined threshold, which may result in user discontentment. Conversely, the latter endows EV users with the authority to self-regulate their charging behavior to optimize cost, allowing a multitude of interconnected EVs to charge during the same off-peak window. However, this decentralized approach gives rise to the herding problem, wherein a simultaneous surge in EV charging during off-peak periods burdens the power grid, leading to potential system overloads. This paper presents a hybrid coordinating scheme that integrates a fuzzy inference mechanism to synergistically blend the merits of centralized and decentralized coordinations. The proposed hybrid coordination scheme aims to minimize peak load, alleviate herding, and optimize charging costs while ensuring adherence to EV users’ charging obligations at the lowest feasible expense. The problem is formulated with the introduction of a novel fuzzy objective function and subsequently resolved through the fuzzy inference mechanism. The fuzzy inference encapsulates independent and uncertain price profiles, consumption load patterns, and state-of-charge data collected from the power grid, households, and EV domains, which are effectively integrated into weighted variables for the requesting EVs. The proposed hybrid coordinating scheme leverages weighted variables to optimize the objective function, enabling the determination of an optimal charging schedule that satisfies the charging requirements of the requesting EVs, while adhering to stringent power grid operational constraints and minimizing charging costs. To assess the efficacy of the hybrid coordination scheme, we conducted two meticulous case studies employing the IEEE 34 bus system as a testbed, thoroughly evaluating performance metrics encompassing charging cost, load profile impact, and peak-to-average ratio. The results demonstrate the superior performance of the proposed hybrid coordination scheme compared to alternative charging strategies, including uncoordinated charging, standard-rate charging, time-of-use charging, and two-layer decentralized approaches.

Integrating multivariate fuzzy neural networks into fuzzy inference system for enhanced decision making

In this paper, we introduce a novel family of operators for single-hidden layer feed-forward neural networks that operate on multivariate fuzzy-valued functions. These operators are based on the fractional mean value of the approximating function. The construction of these operators involves the use of sigmoidal functions, providing multiple choices for their implementation. We demonstrate the modeling capabilities of our operators employing a novel combined neural network system that incorporates the basic fuzzy inference mechanism and fuzzy neural networks. This system represents the connection strength among the output neurons using multivariate fuzzy-valued functions, enhancing decision-making processes. Additionally, we prove the convergence properties of these operators with respect to the Pompeiu-Hausdorff metric. By controlling the asymptotic decay of the sigmoidal function, we achieve accurate Jackson-type estimations using the modulus of continuity of fuzzy-valued functions.

OVERCOMING THE BARRIERS TO THE DIGITAL TRANSFORMATION OF INDUSTRIAL ENTERPRISES THROUGH THE BUSINESS MODEL SELECTION MECHANISM

The article describes the main barriers to digital transformation faced by industrial enterprises in various industries, such as: lack of appropriate funding, information security risks, insufficient digital skills of employees, insufficient maturity of current processes, internal resistance to change, insufficient awareness of managers, lack of certainty over the future of digital standards. To analyse the barriers, the author used a three-step approach, including a literature review, a primary research with representatives of the companies, and a qualitative comparative analysis that are based on the Kruskal – Wallis test and used to identify differences between groups of enterprises. To overcome the barriers identified by the author it was offered to use a mechanism of a business model selection, which takes into account the assessment of obtained competitive advantages (improvement of operational, financial and technical efficiency), digital maturity (digital culture level, staff qualifications, the quality of business process organisation and access to digital infrastructure) and risks (non-receipt of expected income from business model implementation, information security, reputational and personnel). The novelty of the proposed business model selection mechanism for an industrial enterprise is to improve the classification and develop a multi-criteria mechanism for choosing a business model, which would be implemented using a knowledge-based system incorporating a fuzzy inference mechanism.

Finding the combined effect of academic and non-academic performance on management students’ placement: A fuzzy logic approach

The academic performance (scores/credentials) of a student during an educational program is generally linked to his/her employability (including the placement package he/she receives) (Cheong et al., 2018). However, in a program like MBA non-academic performance like extracurricular activities, interpersonal skills, etc., of a student is equally or more important as it could fetch him/her a better job with a higher package. Moreover, MBA graduates are expected to have not only academic knowledge but also practical skills such as leadership, attitude, efforts, and communication. This study relates both academic and non-academic performances of students to the respective packages they get and proposes a fuzzy expert system (FES) for the evaluation of students’ performances and its combined effect on placement (outcome). A suitable rule-based fuzzy inference mechanism (FIM) has been formed and discussed. The principles behind fuzzy logic in evaluating student academic as well as non-academic performances have been discussed and how these principles could be applied in measuring its impact on the placement packages (historical) is illustrated. Results (achieved placement performance/package of students) were also compared with the existing (traditional) statistical method. The fuzzy logic system was assessed as a more practical method of evaluation than the traditional method. This research uses a sample of 56 students in the MBA program who graduated in 2021 from the management department (b-school) of a premier private university in India. The research found that an increase in non-academic performance had a greater impact on placement outcomes than an increase in academic performance alone.

APPLICATION OF FUZZY LOGIC IN THE ROBOT CONTROL FOR MECHANICAL PROCESSING

Robot application in mechanical machining is growing day by day because it has many advantages over conventional machines such as high flexibility, large working space, and high repeatability. Many degrees of freedom of motion give robots the ability to perform complex technological operations, but also because of that, methods of controlling robots based on dynamic models have difficulties. Applying fuzzy logic to robot control can partially or completely exclude the calculation of the robot's dynamic model as well as overcome other uncertainties of the whole technological system. The variables and parameters of the fuzzy logic-based controller are modeled in a linguistic form, called linguistic variables, and are defined by the linguistic semantic values. Fuzzy rules are an important basis for the performance of operations defining the control quantities of the controller. Fuzzy rules are constructed by natural human inference and are based on expert intelligence. The main tasks of applying fuzzy logic control include “Fuzzification” to determine fuzzy parameters in the form of fuzzy sets of input-output data; “Fuzzy Rules and Fuzzy Inference Mechanism” to perform fuzzy operations defining control quantities, and finally “Defuzzification” to convert control quantities from linguistic values to physical values for controller operation. There have been many types of research on applying fuzzy logic to control robots in general, but the percentage of fuzzy control research work for mechanical machining robots is still limited.
 The article is based on published works on fuzzy control for robots in general and mechanical processing robots to analyze the applicability of fuzzy control for mechanical machining robots. The article provides detailed information on fuzzy controller design, on determining input and output variables, proportional mapping to determine the number of fuzzy sets and the corresponding type of membership function. The construction of fuzzy rule base system and fuzzy inference mechanism is presented, and finally defuzzification. Prospects for the use of methods to perfect and develop fuzzy control systems for mechanical machining robots are also presented. Collectively, the information in this document is intended to guide the implementation of fuzzy logic-based controller designs for application to machining robots, as well as to general robot control.

Forecasting development trends in the information technology industry using fuzzy logic

The IT industry occupies a significant place in the economy of the world. Trends in its development directly affect changes in various sectors of the economy. This paper considers the dynamics of IT industry under conditions of increased entropy of the functioning environment caused by crises, military conflicts, non-standard events. The mathematical basis of the study is an apparatus of fuzzy logic. The proposed technique for analyzing and forecasting the state and dynamics of the IT sector makes it possible to take into account the influences of a set of factors of different nature and direction of action, which contributes to obtaining a reasonable assessment under conditions of inaccurate information. The software platform for implementing the methodology is the authentically designed fuzzy expert system (ES) FuzzyKIDE. The conceptual foundations of the platform structure have been defined. A modification of the fuzzy inference mechanism has been proposed, which, unlike existing samples, makes it possible to eliminate saving intermediate results and reduces the load on the database. The composition and structure of the knowledge base (KB) for ES have been proposed. Fuzzy rules are based on predetermined relationships between key factors of influence. The technology of ES operation is represented using simulated scenarios with obtaining predictive results. The analysis of the final data of expert consultations proves the ES operability, providing for the possibility of KB significant expansion in the process of industrial operation. The use of ES is aimed at forming a holistic view of possible directions of IT sector development. Maintaining the actual state of ES KB is a condition for early warning of the emergent negative/crisis phenomena. The FuzzyKIDE expert consulting system is proposed as a tool to support management decision-making based on the analysis and forecasting of the state and dynamics of IT sector under conditions of high uncertainty

Data analysis using system modeling

Expert systems are increasingly being used to format safe operations. But the functions of expert systems can perform not only assistance in making decisions, but also analyze processes and help at various stages. These actions are possible when considering a system with fuzzy data. The work is devoted to solving the problem of fuzzy inference knowledge in intelligent systems based on the use of fuzzy logic. The scheme of construction of continuous logic, the computation of values of membership functions of linguistic variables of output knowledge. The proposed approach is based on the use of continuous logic, which allows for a more accurate representation of fuzzy data compared to traditional logic. The construction of the continuous logic scheme involves the use of fuzzy sets for both input and output variables, which are then used to compute the values of membership functions of linguistic variables of output knowledge. In this approach, the expert system is able to analyze processes and assist at various stages by making use of fuzzy inference knowledge. The fuzzy inference mechanism is based on the use of fuzzy logic, which allows for a more nuanced understanding of complex systems and processes. The expert system is able to analyze data from various sources and make informed recommendations based on the available information. Overall, the use of expert systems based on fuzzy logic is becoming increasingly popular in a variety of industries. By improving the accuracy of data analysis and decision-making, these systems can help to ensure safe and efficient operations.

USER ENGAGEMENT COMBINED ASSESSMENT SYSTEM

The article discusses methods for assessing engagement as a characteristic of human activity. Existing systems for assessing engagement that are designed to assess either the activities of a software user or the quality of the software itself with which this user interacts are described. A method for the combined assessment of the engagement of software users based on heterogeneous data obtained during the user’s interaction with the software is described. Engagement indicators are proposed, defined as numerical interpretations of oculographic data, data on the emotional state of the user, and web analytics data. Due to the subjective nature of any interpretation, the combined assessment of engagement is defined using a fuzzy inference mechanism. Three linguistic variables characterizing each indicator are defined. A base of fuzzy production rules for a fuzzy inference system and a method for combining them based on fuzzy rules have been developed. The proposed method is implemented in a system that includes subsystems for collecting and sending user data and evaluating engagement, interacting with each other through the API. An experiment conducted to evaluate the effectiveness of the proposed method using the developed system is described. The experiment consisted in assessing the engagement of the software user, first by each indicator separately, then by their combination, and analyzing the degree of compliance of these assessments with the assessments received from an external expert and the user himself (self-assessment). An analysis of the results of the experiment showed that 63 % of the ratings obtained by the combined assessment method coincided with at least one of the assessments received from an external expert or the user himself, compared with 55 % of coincidences obtained using only the oculographic indicator, and 17 % of coincidences obtained using the “emotional involvement” indicator. Thus, the proposed method provides a higher degree of confidence in the results of the engagement assessment for making a decision based on this assessment than methods for assessing involvement that use each indicator separately.

Autonomous learning for fuzzy systems: a review

As one of the three pillars in computational intelligence, fuzzy systems are a powerful mathematical tool widely used for modelling nonlinear problems with uncertainties. Fuzzy systems take the form of linguistic IF-THEN fuzzy rules that are easy to understand for human. In this sense, fuzzy inference mechanisms have been developed to mimic human reasoning and decision-making. From a data analytic perspective, fuzzy systems provide an effective solution to build precise predictive models from imprecise data with great transparency and interpretability, thus facilitating a wide range of real-world applications. This paper presents a systematic review of modern methods for autonomously learning fuzzy systems from data, with an emphasis on the structure and parameter learning schemes of mainstream evolving, evolutionary, reinforcement learning-based fuzzy systems. The main purpose of this paper is to introduce the underlying concepts, underpinning methodologies, as well as outstanding performances of the state-of-the-art methods. It serves as a one-stop guide for readers learning the representative methodologies and foundations of fuzzy systems or who desire to apply fuzzy-based autonomous learning in other scientific disciplines and applied fields.

Technical research of the Intelligent substation’s secondary maintenance safety measure system

Major changes have taken place during the secondary equipment maintenance and isolation of the intelligent substation and conventional substation in that the physical connection of the conventional cable has become invisible and not to be touched. This change, no undoubtedly, has added the difficulty for the relay protection staff to operate. The secondary maintenance safety measure system of the intelligent substation described in this thesis can automatically complete the writing and implementation of the secondary safety measure sheet by adopting Groovy rules and self-learning fuzzy inference mechanism. The safety measure system expert data base has been continuously improved through manual correction and self-learning technology, which has greatly improved the work efficiency of the site operators, and reduced the impact of the wrong implementation of the secondary safety measures on the normal operation of the power system.

W-Infer-polation: Approximate reasoning via integrating weighted fuzzy rule inference and interpolation

Fuzzy rule-based inference systems facilitate approximate reasoning for decision-making under circumstances where knowledge is imprecisely characterised. Compositional rule of inference (CRI) and fuzzy rule interpolation (FRI) are two typical types of technique to implement fuzzy systems. Questions of when to apply which of these two fuzzy inference techniques is often addressed by checking whether there exist certain rules that can match given observations. Both techniques have been widely investigated to improve the performance of approximate reasoning, with increasingly more attention being paid to the development of systems where rule antecedent attributes are associated with measures of their relative significance or weights. Unfortunately, they are mostly done in a separate manner within their own fields, making it difficult to achieve accurate reasoning when both techniques are required simultaneously. This paper presents an innovative fuzzy inference mechanism, W-Infer-polation, which organically integrates both enhanced versions of CRI and FRI that are equipped with rule attribute weights. In particular, the individual weights of rule antecedents and consequent are generated using the given unweighted rules only, creating an attribute weighted rule base. From this, W-Infer-polation exploits the learned weights to guide the selection of weighted rules for rule firing in CRI and that of nearest weighted rules for FRI. A systematic comparative experimentation is also presented, demonstrating the efficacy of W-Infer-polation.

The Scalable Fuzzy Inference-Based Ensemble Method for Sentiment Analysis.

Internet environments such as social networks, news sites, and blogs are the platforms where people can share their ideas and opinions. Many people share their comments instantly on the internet, which results in creating large volumes of entries. It is important for institutions and organizations to analyze this big data in an efficient and rapid manner to produce summary information about the feelings or opinions of individuals. In this study, we propose a scalable framework that makes sentiment classification by evaluating the compound probability scores of the most widely used methods in sentiment analysis through a fuzzy inference mechanism in an ensemble manner. The designed fuzzy inference system makes the sentiment estimation by evaluating the compound scores of valance aware dictionary, word embedding, and count vectorization processes. The difference of the proposed method from the classical ensemble methods is that it allows weighting of base learners and combines the strengths of each algorithm through fuzzy rules. The sentiment estimation process from text data can be managed either as a 2-class (positive and negative) or as a 3-class (positive, neutral, and negative) problem. We performed the experimental work on four available tagged social network data sets for both 2-class and 3-class classifications and observed that the proposed method provides improvements in accuracy.

An Efficient Approach for Line-Following Automated Guided Vehicles Based on Fuzzy Inference Mechanism

Recently, there has been increasing attention paid to AGV (Automated Guided Vehicle) in factories and warehouses to enhance the level of automation. In order to improve productivity, it is necessary to increase the efficiency of the AGV, including working speed and accuracy. This study presents a fuzzy-PID controller for improving the efficiency of a line-following AGV. A line-following AGV suffers from tracking errors, especially on curved paths, which causes a delay in the lap time. The fuzzy-PID controller in this study mimics the principle of human vehicle control as the situation-aware speed adjustment on curved paths. Consequently, it is possible to reduce the tracking error of AGV and improve its speed. Experimental results show that the Fuzzy-PID controller outperforms the PID controller in both accuracy and speed, especially the lap time of a line-following AGV is enhanced up to 28.6% with the proposed fuzzy-PID controller compared to that with the PID controller only.

A Computational Model for Driver Risk Evaluation and Crash Prediction Using Contextual Data from On-board Telematics

Introduction: Vehicle crashes can be hazardous to public safety and may cause infrastructure damage. Risky driving significantly raises the possibility of the occurrence of a vehicle crash. As per statistics by the World Health Organization (WHO), approximately 1.35 million people are involved in road traffic crashes resulting in loss of life or physical disability. WHO attributes events like over-speeding, drunken driving, distracted driving, dilapidated road infrastructure and unsafe practices such as non-use of helmets and seatbelts to road traffic accidents. As these driving events negatively affect driving quality and enhance the risk of a vehicle crash, they are termed as negative driving attributes. Methods: A multi-level hierarchical fuzzy rules-based computational model has been designed to capture risky driving by a driver as a driving risk index. Data from the onboard telematics device and vehicle controller area network is used for capturing the required information in a naturalistic way during actual driving conditions. Fuzzy rules-based aggregation and inference mechanisms have been designed to alert about the possibility of a crash due to the onset of risky driving. Results: On-board telematics data of 3213 sub-trips of 19 drivers has been utilized to learn long term risky driving attributes. Furthermore, the current trip assessment of these drivers demonstrates the efficacy of the proposed model in correctly modeling the driving risk index of all of them, including 7 drivers who were involved in a crash after the monitored trip. Conclusions: In this work, risky driving behavior has been associated not just with rash driving but also other contextual data like driver’s long-term risk aptitude and environmental context such as type of roads, traffic volume and weather conditions. Trip-wise risky driving behavior of six out of seven drivers, who had met with a crash during that trip, was correctly predicted during evaluation. Similarly, for the other 12 drivers, the model accurately predicted safe driving behavior as these drivers did not meet with any vehicle crash. The proposed model can be used as an alert mechanism to indicate potential crash scenarios to the driver. The current study did not study lane changing behavior in detail due to difficulty in capturing road lanes in the Indian context.

FLC-ROS: A generic and configurable ROS package for developing fuzzy logic controllers1

Fuzzy logic controllers can handle complex systems by incorporating expert’s knowledge in the absence of formal mathematical models. Further, fuzzy logic controllers can effectively capture and accommodate uncertainties that are inherent in real-world controlled systems. On the other hand, Robot Operating System (ROS) has been widely used for many robotic applications due to its modular structure and efficient message-passing mechanisms for the integration of system’s components. For this reason, Robot Operating System is an ideal tool for developing software stacks for robotic applications. This paper develops a generic and configurable Robot Operating System package for the implementation of fuzzy logic controllers, particularly type-1 and interval type-2, which are based on either Mamdani or Takagi-Sugeno-Kang fuzzy inference mechanisms. This is achieved by employing a systematic object-oriented approach using the Unified Model Language (UML) to implement the fuzzy inference system as a single class that is composed of fuzzifier, inference, and defuzzifier classes. The deployment of the developed Robot Operating System package is demonstrated by implementing an interval type-2 fuzzy logic control of an Unmanned Aerial Vehicle (UAV).

Robust-adaptive-behavior strategy for human-following robots in unknown environments based on fuzzy inference mechanism

PurposeTo follow and maintain an appropriate distance to the selected target person (STP), the mobile robot is required to have capabilities: the human detection and tracking and an efficient following strategy with a smooth manner that does not appear threatening to the STP and surroundings. The efficient following strategy must integrate the STP position and the obstacle information to achieve smooth and safe human-following behaviors, especially in unknown environments where robot does not have understandings in advance. The purpose of this study is to propose a robust-adaptive-behavior strategy for mobile robots.Design/methodology/approachThis paper presents a robust-adaptive-behavior strategy (RABS) based on the fuzzy inference mechanism to help the robot follow the STP effectively in various unknown environments with the real-time obstacle avoidance, both indoor and outdoor and on different robot platforms. In which, the traversability of robots’ unknown surrounding environments is analyzed by using the STP position and the obstacle information obtained from the two dimensional laser scan, whose purpose is to choose the highest-traversability-score direction (HTSD) and an adaptive-safe-following distance (ASFD). Then, the HTSD, the ASFD and the current velocity of the robot are considered as inputs of the fuzzy system to adjust its velocity smoothly.FindingsThe proposed RABS is verified by a set of experiments using a real big-heavy autonomous mobile robot (BH-AMR), with the dimension 0.8 × 1.2 (m), weight 150 (kg), full-load 500 (kg), aiding smart factories. The obtained results have shown that the proposed RABS equips the BH-AMR with the ability to follow the STP smoothly and safely even when the robot is moving at the maximum speed 1.5 (m/s).Research limitations/implicationsIn this paper, the autonomous mobile robot considers all environments as unknown even when it is working in mapped environments. This limitation is presented clearly in the future works section.Practical implicationsThis proposed method can be used to help the autonomous mobile robot support persons in factories, hospitals, restaurants, supermarkets or at the airports.Originality/valueThis paper presents a RABS, including three new features: a fuzzy-based solution to help human-following robots maintain an appropriate distance to the STP safely and smoothly with the maximum velocity 1.5 (m/s); the proposed fuzzy-based solution, an adaptive vector field histogram and a new approach for the STP tracking is combined to follow the STP and avoid the collision simultaneously in unknown indoor and outdoor environments; the proposed RABS is considered for BH-AMRs (with the dimension 0.8 × 1.2 (m), weight 150 (kg), full-load 500 (kg)) to serve real tasks in smart factories.

Fuzzy Semantic Models of Fuzzy Concepts in Fuzzy Systems

The fuzzy properties of language semantics are a central problem towards machine-enabled natural language processing in cognitive linguistics, fuzzy systems, and computational linguistics. A formal method for rigorously describing and manipulating fuzzy semantics is sought for bridging the gap between humans and cognitive fuzzy systems. The mathematical model of fuzzy concepts is rigorously described as a hyperstructure of fuzzy sets of attributes, objects, relations, and qualifications, which serves as the basic unit of fuzzy semantics for denoting languages entities in semantic analyses. The formal fuzzy concept is extended to complex structures where fuzzy modifiers and qualifiers are considered. An algebraic approach is developed to manipulate composite fuzzy semantic as a deductive process from a fuzzy concept to the determined semantics. The denotational mathematical structure of fuzzy semantic inference not only explains the fuzzy nature of human semantics and its comprehension, but also enables cognitive machines and fuzzy systems to mimic the human fuzzy inference mechanisms in cognitive linguistics, cognitive computing, and computational intelligence.

Robust fuzzy gain scheduling power control of a generation IV nuclear reactor

Gen-IV nuclear reactors are deemed to actively participate in the load following operations in order to meet the ever increasing energy demands. Robust fuzzy gain scheduling is proposed in this work as an advanced and efficient control strategy for the power cycling of the BREST design of lead cooled fast reactors (LFR). To this end, a reasonably accurate coupled thermo-neutronic simulator is developed at first. System identification techniques are employed thereafter to extract a multi-model set at various operating levels relying on the data obtained from this simulator. Uncertainties incurred as a result of the statistically based identification schemes are moreover accounted for using a narrow band (99%) confidence level for the determined model parameters. A set of robust controllers are contrived through a mixed sensitivity analysis framework and are blended afterwards by way of a fuzzy inference mechanism to appropriately schedule this regulation practice with the reactor power level. Certain load following scenarios are finally examined wherein the closed loop system exhibits an efficient and quite feasible control attempt.