Model Expert Knowledge Research Articles

How far back shall we peer? Optimal air handling unit control leveraging extensive past observations

Heating, Ventilation, and Air Conditioning (HVAC) systems play a critical role in ensuring occupant comfort in buildings. Traditional Rule-Based Feedback Control (RBFC) systems, while widely deployed for their simplicity, suffer from low adaptability. Recent improvements, such as Model Predictive Control (MPC) methods demand complex mathematical modeling and substantial expert knowledge, creating a high barrier for system design and optimization. Reinforcement Learning (RL) emerges as a promising solution with its adaptability and model-free nature, albeit challenged by sample inefficiency and suboptimal convergence. Given the intrinsic delayed effects from previous actions and prolonged thermal inertia in the HVAC systems, this study introduces an innovative deep RL framework that can leverage historical observations to refine RL agent performance. By incorporating a state-of-the-art (SOTA) Transformer model, we capture the temporal patterns in HVAC data and build a more precise RL training environment. Implemented on high-resolution, real-world HVAC datasets, our framework showed superior performance in both HVAC system modeling and RL control performance. Specifically, compared to the two baseline models—Bidirectional Long Short-Term Memory (Bi-LSTM) and vanilla Transformer, our proposed RL environment model achieved an average of 30.5% and 35.8% prediction accuracy improvement, respectively. Moreover, our optimal past observable RL agent swiftly delivers 35.3% of electricity saving and 54.4% of thermal comfort improvement over traditional RBFC. These results show the effectiveness of the pioneering integration of extensive historical observations for HVAC operation optimization.

P0956 Predicting Clinical Remission in Crohn’s Disease: A Comparative Study of Expert-Generated and Computer-Generated Bayesian Networks

Abstract Background In Crohn’s disease (CD), artificial intelligence (AI) may improve treatment optimization and aid in clinical decision-making 1. The aim of the study is to compare the results of Bayesian Networks (BNs) of both Expert Knowledge Model (EKM) and Computer Algorithm Generated Model (CAGM) in predicting corticosteroid-free clinical remission at 52 weeks after introducing ustekinumab and vedolizumab treatment in patients with CD. Methods Data were extracted from the Dutch Initiative on Crohn and Colitis (ICC) registry. Observations were conducted on patients with CD (n = 440) based on remission criteria including Harvey Bradshaw Index (HBI<5) assessment and no corticosteroid use. Data were divided into training (70%, N = 309) and validation (30%, N = 131) subsets. Based on these, two Bayesian network models were developed. Expert knowledge gathered through three consultation rounds with 12 specialists was used to generate an EKM using a Directed Acyclic Graph and the K2-scoring method to refine and accurately represent node relationships in the model. Furthermore, the Hill Climbing search algorithm was applied to build CAGM 2. Finally, node elimination was applied to reduce complexity and further optimize the performance of both EKM and CAGM. Results The EKM contained 21 expert-defined variables and 38 edges between them. There were ten direct parent nodes in the EKM final round of node elimination, including age at diagnosis, disease duration, gender, smoking status, calprotectin, C-reactive protein (CRP), thrombocytes, prior surgery, previous medication, and perianal disease. The EKM showed AUC, accuracy, sensitivity, and specificity values of 0.70, 0.64, 0.15, and 0.92, respectively. On the other hand, the CAGM contained 14 variables and 17 edges. Six direct parents were left in the final round, including previous medication, prior surgery, CRP, perianal disease, thrombocytes, and age at diagnosis. The CAGM showed an AUC, accuracy, sensitivity, and specificity values of 0.59, 0.62, 0.17, and 0.88, respectively. Consequently, age at diagnosis, previous medication, thrombocytes, and perianal diseases were common nodes left in both models. Conclusion In this comparative study, after rounds of node elimination, the expert-generated model has a higher AUC than that of the computer-generated model. The findings indicate that expert knowledge models provide a deeper, more comprehensive influence on Crohn’s remission outcomes compared to computer-generated models. With BN’s capacity to incorporate expert information and surpass algorithm-only methods in remission prediction, they may provide physicians with a potent, interpretable tool for individualized decision-making and enhancing diagnostic precision. References Sandborn WJ, Rutgeerts P, Gasink C, et al. Long-term efficacy and safety of ustekinumab for Crohn’s disease through the second year of therapy. Aliment Pharmacol Ther. 2018;48(1):65-77. doi:10.1111/apt.14794 Park E, Chang H jae, Nam HS. A Bayesian Network Model for Predicting Post-stroke Outcomes With Available Risk Factors. Front Neurol. 2018;9. doi:10.3389/fneur.2018.00699

Development of a Flexible Information Security Risk Model Using Machine Learning Methods and Ontologies

This paper presents a significant advancement in information security risk assessment by introducing a flexible and comprehensive model. The research integrates established standards, expert knowledge, machine learning, and ontological modeling to create a multifaceted approach for understanding and managing information security risks. The combination of standards and expert insights forms a robust foundation, ensuring a holistic grasp of the intricate risk landscape. The use of cluster analysis, specifically applying k-means on information security standards, expands the data-driven approach, uncovering patterns not discernible through traditional methods. The integration of machine learning algorithms in the creation of information security risk dendrogram demonstrates effective computational techniques for enhanced risk discovery. The introduction of a heat map as a visualization tool adds innovation, facilitating an intuitive understanding of risk interconnections and prioritization for decision makers. Additionally, a thesaurus optimizes risk descriptions, ensuring comprehensiveness and relevance despite evolving terminologies in the dynamic field of information security. The development of an ontological model for structured risk classification is a significant stride forward, offering an effective means of categorizing information security risks based on ontological relationships. These collective innovations enhance understanding and management of information security risks, paving the way for more effective approaches in the ever-evolving technological landscape.

Model-Centric Integration of Uncertain Expert Knowledge into Importance Sampling-Based Parameter Estimation

This study presents a model-based parameter estimation method for integrating and validating uncertainty in expert knowledge and simulation models. The parameters of the models of complex systems are often unknown due to a lack of measurement data. The experience-based knowledge of experts can substitute missing information, which is usually imprecise. The novelty of the present paper is a method based on Monte Carlo (MC) simulation and importance sampling (IS) techniques for integrating uncertain expert knowledge into the system model. Uncertain knowledge about the model parameters is propagated through the system model by MC simulation in the form of a discrete sample, while IS helps to weight the sample elements regarding imprecise knowledge about the outputs in an iterative circle. Thereby, the consistency of expert judgments can be investigated as well. The contributions of this paper include an expert knowledge-based parameter estimation technique and a method for the evaluation of expert judgments according to the estimation results to eliminate incorrect ones. The applicability of the proposed method is introduced through a case study of a Hungarian operating waste separation system. The results verify that the assessments of experts can be efficiently integrated into system models, and their consistency can be evaluated.

Hybrid Anomaly Detection in Time Series by Combining Kalman Filters and Machine Learning Models.

Due to connectivity and automation trends, the medical device industry is experiencing increased demand for safety and security mechanisms. Anomaly detection has proven to be a valuable approach for ensuring safety and security in other industries, such as automotive or IT. Medical devices must operate across a wide range of values due to variations in patient anthropometric data, making anomaly detection based on a simple threshold for signal deviations impractical. For example, surgical robots directly contacting the patient's tissue require precise sensor data. However, since the deformation of the patient's body during interaction or movement is highly dependent on body mass, it is impossible to define a single threshold for implausible sensor data that applies to all patients. This also involves statistical methods, such as Z-score, that consider standard deviation. Even pure machine learning algorithms cannot be expected to provide the required accuracy simply due to the lack of available training data. This paper proposes using hybrid filters by combining dynamic system models based on expert knowledge and data-based models for anomaly detection in an operating room scenario. This approach can improve detection performance and explainability while reducing the computing resources needed on embedded devices, enabling a distributed approach to anomaly detection.

Task graph offloading via deep reinforcement learning in mobile edge computing

Various mobile applications that comprise dependent tasks are gaining widespread popularity and are increasingly complex. These applications often have low-latency requirements, resulting in a significant surge in demand for computing resources. With the emergence of mobile edge computing (MEC), it becomes the most significant issue to offload the application tasks onto small-scale devices deployed at the edge of the mobile network for obtaining a high-quality user experience. However, since the environment of MEC is dynamic, most existing works focusing on task graph offloading, which rely heavily on expert knowledge or accurate analytical models, fail to fully adapt to such environmental changes, resulting in the reduction of user experience. This paper investigates the task graph offloading in MEC, considering the time-varying computation capabilities of edge computing devices. To adapt to environmental changes, we model the task graph scheduling for computation offloading as a Markov Decision Process (MDP). Then, we design a deep reinforcement learning algorithm (SATA-DRL) to learn the task scheduling strategy from the interaction with the environment, to improve user experience. Extensive simulations validate that SATA-DRL is superior to existing strategies in terms of reducing average makespan and deadline violation.

How representative are air transport functional complex networks? A quantitative validation.

Functional networks have emerged as powerful instruments to characterize the propagation of information in complex systems, with applications ranging from neuroscience to climate and air transport. In spite of their success, reliable methods for validating the resulting structures are still missing, forcing the community to resort to expert knowledge or simplified models of the system's dynamics. We here propose the use of a real-world problem, involving the reconstruction of the structure of flights in the US air transport system from the activity of individual airports, as a way to explore the limits of such an approach. While the true connectivity is known and is, therefore, possible to provide a quantitative benchmark, this problem presents challenges commonly found in other fields, including the presence of non-stationarities and observational noise, and the limitedness of available time series. We explore the impact of elements like the specific functional metric employed, the way of detrending the time series, or the size of the reconstructed system and discuss how the conclusions here drawn could have implications for similar analyses in neuroscience.

Climate tipping point interactions and cascades: a review

Abstract. Climate tipping elements are large-scale subsystems of the Earth that may transgress critical thresholds (tipping points) under ongoing global warming, with substantial impacts on the biosphere and human societies. Frequently studied examples of such tipping elements include the Greenland Ice Sheet, the Atlantic Meridional Overturning Circulation (AMOC), permafrost, monsoon systems, and the Amazon rainforest. While recent scientific efforts have improved our knowledge about individual tipping elements, the interactions between them are less well understood. Also, the potential of individual tipping events to induce additional tipping elsewhere or stabilize other tipping elements is largely unknown. Here, we map out the current state of the literature on the interactions between climate tipping elements and review the influences between them. To do so, we gathered evidence from model simulations, observations, and conceptual understanding, as well as examples of paleoclimate reconstructions where multi-component or spatially propagating transitions were potentially at play. While uncertainties are large, we find indications that many of the interactions between tipping elements are destabilizing. Therefore, we conclude that tipping elements should not only be studied in isolation, but also more emphasis has to be put on potential interactions. This means that tipping cascades cannot be ruled out on centennial to millennial timescales at global warming levels between 1.5 and 2.0 ∘C or on shorter timescales if global warming surpassed 2.0 ∘C. At these higher levels of global warming, tipping cascades may then include fast tipping elements such as the AMOC or the Amazon rainforest. To address crucial knowledge gaps in tipping element interactions, we propose four strategies combining observation-based approaches, Earth system modeling expertise, computational advances, and expert knowledge.

Analysis of models and development of a software package for assessing the impact of vibration effects of vehicles on urban infrastructure

The direction associated with the construction and research of models that can be used to predict the impact of vibration effects of vehicles on surrounding infrastructure facilities is relevant given the increasing pace of civil engineering in large cities and taking into account the expansion of transport networks. The development of models for evaluating vibration effects and for subsequent use in monitoring and diagnostic systems requires the use of modern methods of nonlinear analysis, discrete mathematics, as well as tools for the development of expert systems and artificial intelligence technologies. The purpose of the article is to obtain the results of the analysis of a complex mathematical model of spatial interactions, the development of algorithmic support and a description of the structure of a software package for assessing the impact of vibration effects of vehicles on urban infrastructure. A model of spatial interactions is proposed to assess the levels of vibration exposure with the construction of a graph of the placement of objects connected by vibration propagation channels and using the mathematical apparatus of modeling hybrid dynamic systems, expert knowledge and fuzzy rules of logical inference. Examples of constructing a model of spatial interactions taking into account three objects of vibrational observation are considered. The VibCalcAlg algorithm has been developed to calculate the states of a three-component model. The structure of the VibCalc software package is described, which includes a block of fuzzy logic, a block of model construction and a block of visualization. The results can be used in solving mathematical modeling problems related to the assessment and prediction of vibration impacts in the field of transport and civil engineering, as well as in solving environmental protection problems. The results are aimed at improving intelligent monitoring systems and decision support systems in the design, construction and operation of transport facilities.

Extraction and Association Analysis of Risk Factors in Gas Pipeline Network Emergencies Through Fusing Expert Knowledge and Pre-Trained Model

Extraction and Association Analysis of Risk Factors in Gas Pipeline Network Emergencies Through Fusing Expert Knowledge and Pre-Trained Model

Expert knowledge modelling software design based on Signed Directed Graph with the application for PWR fault diagnosis

Online monitoring and Fault Diagnosis and Detection (FDD) can help the operator to enhance the situation awareness. The Signed Directed Graph (SDG) has the significant advantages that SDG-based FDD not only infers the incipient faults and reveals fault propagation paths but also comprehensively explains causes of failure. In this study, an expert knowledge modelling software is designed and developed based on SDG method. Firstly, the SDG-based process monitoring and FDD algorithms are derived by coupling the threshold method and the quality trend analysis method. Secondly, the expert knowledge modelling software is designed and developed not only considering the convenience of the engineer but also the effectiveness of the SDG-based algorithms. Finally, a Pressurized Water Reactor (PWR) is applied to demonstrate the SDG modelling and verify the SDG models. The case study demonstrates that the developed expert knowledge modelling software can effectively diagnose the incipient faults and infer variable impact as the fault propagation paths

Semantic understanding and prompt engineering for large-scale traffic data imputation

Intelligent Transportation Systems (ITS) face the formidable challenge of large-scale missing data, particularly in the imputation of traffic data. Existing studies have mainly relied on modeling network-level spatiotemporal correlations to address this issue. However, these methods often overlook the rich semantic information (e.g., road infrastructure, sensor location, etc.) inherent in road networks when capturing network-wide spatiotemporal correlations. We address this limitation by presenting the Graph Transformer-based Traffic Data Imputation (GT-TDI) model, which imputes missing values in extensive traffic data by leveraging spatiotemporal semantic understanding of road networks. The proposed model leverages semantic descriptions that capture the spatial and temporal dynamics of traffic across road networks, enhancing its capacity to infer comprehensive spatiotemporal relationships. Moreover, to augment the model’s capabilities, we employ a Large Language Model (LLM) and prompt engineering to enable natural and intuitive interactions with the traffic data imputation system, allowing users to query and request in plain language, without requiring expert knowledge or complex mathematical models. The proposed model, GT-TDI, utilizes Graph Neural Networks (GNN) and Transformer architectures to perform large-scale traffic data imputation using deficient observations, sensor social connectivity, and semantic descriptions as inputs. We evaluate the GT-TDI model on the PeMS freeway dataset and benchmark it against cutting-edge models. The experimental evidence demonstrates that GT-TDI surpasses the cutting-edge approaches in scenarios with intricate patterns and varying rates of missing data.

Spatio-Temporal Knowledge Graph-Based Research on Agro-Meteorological Disaster Monitoring

Currently, there is a wealth of data and expert knowledge available on monitoring agro-meteorological disasters. However, there is still a lack of technical means to organically integrate and analyze heterogeneous data sources in a collaborative manner. This paper proposes a method for monitoring agro-meteorological disasters based on a spatio-temporal knowledge graph. It employs a semantic ontology framework to achieve the organic fusion of multi-source heterogeneous data, including remote sensing data, meteorological data, farmland data, crop information, etc. And it formalizes expert knowledge and computational models into knowledge inference rules, thereby enabling monitoring, early warning, and disaster analysis of agricultural crops within the observed area. The experimental area for this research is the wheat planting region in three counties in Henan Province. The method is tested using simulation monitoring, early warning, and impact calculation of the past two occurrences of dry hot wind disasters. The experimental results demonstrate that the proposed method can provide more specific and accurate warning information and post-disaster analysis results compared to raw records. The statistical results of NDVI decline also validate the correlation between the severity of wheat damage caused by dry hot winds and the intensity and duration of their occurrences. Regarding remote sensing data, this paper proposes a method that directly incorporates remote sensing data into spatio-temporal knowledge inference calculations. By integrating remote sensing data into the regular monitoring process, the advantages of remote sensing data granted by continuous observation are utilized. This approach represents a beneficial attempt to organically integrate remote sensing and meteorological data for monitoring, early warning, and evaluation analysis of agro-meteorological disasters.

A Tool to Combine Expert Knowledge and Machine Learning for Defect Detection and Root Cause Analysis in a Hot Strip Mill

Width-related defects are a common occurrence in the Hot Strip Mill process which can lead to extra processing, concessions, or scrapping. The detection and Root Cause Analysis of these defects is a largely manual process and is vulnerable to several negative factors including human error, late feedback, and knock-on effects in successive steel strip products. Automated tools which utilize Artificial Intelligence and Machine Learning for defect detection and Root Cause Analysis in hot rolling have not yet been adopted outside of surface defect detection and roller force optimization. In this paper, we propose an automated tool for the detection and Root Cause Analysis of width-related defects in the hot rolling process which utilizes a combination of expert knowledge and several Machine Learning models. Through this, we aim to increase the scope, and encourage further development, of Machine Learning applications within the Hot Strip Mill process. Both classical algorithms and Computer Vision methods were used for the Machine Learning component of the tool, namely, classification trees and pre-trained convolutional neural networks. The tool is trained and validated using data from an existing hot rolling mill and thus the challenges of collecting and processing real-world legacy data are highlighted and discussed. The Machine Learning models used are shown to perform optimally by validation performance metrics. The tool is found to be suitable for the specified purpose and would be further improved with more training data.

Machine learning for predicting the outcome of terminal ballistics events

Machine learning (ML) is well suited for the prediction of high-complexity, high-dimensional problems such as those encountered in terminal ballistics. We evaluate the performance of four popular ML-based regression models, extreme gradient boosting (XGBoost), artificial neural network (ANN), support vector regression (SVR), and Gaussian process regression (GP), on two common terminal ballistics’ problems: (a) predicting the V50 ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments, and (b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness. To achieve this we utilise two datasets, each consisting of approximately 1000 samples, collated from public release sources. We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range. Although extrapolation is not advisable for ML-based regression models, for applications such as lethality/survivability analysis, such capability is required. To circumvent this, we implement expert knowledge and physics-based models via enforced monotonicity, as a Gaussian prior mean, and through a modified loss function. The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models, providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not. The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types, target materials and thicknesses, and impact conditions significantly more diverse than that achievable from any existing analytical approach. Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster. We provide some general guidelines throughout for the development, application, and reporting of ML models in terminal ballistics problems.

Proposal and extensive test of a calibration protocol for crop phenology models

A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.

Gaussian Process Regression and Cooperation Search Algorithm for Forecasting Nonstationary Runoff Time Series

In the hydrology field, hydrological forecasting is regarded as one of the most challenging engineering tasks, as runoff has significant spatial–temporal variability under the influences of multiple physical factors from both climate events and human activities. As a well-known artificial intelligence tool, Gaussian process regression (GPR) possesses satisfying generalization performance but often suffers from local convergence and sensitivity to initial conditions in practice. To enhance its performance, this paper investigates the effectiveness of a hybrid GPR and cooperation search algorithm (CSA) model for forecasting nonstationary hydrological data series. The CSA approach avoids the premature convergence defect in GPR by effectively determining suitable parameter combinations in the problem space. Several traditional machine learning models are established to evaluate the validity of the proposed GPR-CSA method in three real-world hydrological stations of China. In the modeling process, statistical characteristics and expert knowledge are used to select input variables from the observed runoff data at previous periods. Different experimental results show that the developed GPR-CSA model can accurately predict nonlinear runoff and outperforms the developed traditional models in terms of various statistical indicators. Hence, a CSA-trained GPR model can provide satisfying training efficiency and robust simulation performance for runoff forecasting.

An in silico workflow for assessing the sensitisation potential of extractables and leachables

As part of a wider toxicological risk assessment to ensure patient safety, extractables and leachables (E&Ls) which are observed above the relevant qualification threshold need to be assessed for their sensitisation potential. This study sought to investigate whether in silico toxicity models could be used to predict the sensitisation hazard and potency potential of E&Ls. An extensive dataset of relevant chemicals was collated by combining and standardising two lists of E&Ls previously published by ELSIE and the PQRI, resulting in a dataset of 790 unique materials. Sensitisation data was then located where possible, resulting in 290 chemicals being associated with dermal sensitisation hazard information, 106 chemicals with dermal sensitisation potency information, and 47 chemicals with respiratory sensitisation information. Existing expert knowledge, in the form of structural alerts within Derek Nexus, was able to accurately predict both the dermal and respiratory sensitisation potential of the E&Ls. 75 different statistical models were also built, using several algorithms and descriptors, and trained on the available dermal sensitisation data. A number of these models proved able to accurately predict the sensitisation potential of the E&Ls, which were found to occupy the same chemical space as the training sets. Finally, hybrid approaches combining expert knowledge and statistical models were investigated, including a tiered system where the skin sensitisation alerts in Derek Nexus provided a hazard prediction, followed by a potency prediction resulting from an alert-based k-nearest neighbours model. The inclusion of the Dermal Sensitisation Thresholds as default, worst-case scenario predictions in cases where similar chemicals were lacking ensured that a prediction was provided for every chemical. It is hoped that this novel workflow, which combines expert knowledge, a statistical model and existing toxicity thresholds, will aid toxicologists when assessing the sensitisation potential of E&Ls administered by any route of administration.

Reinforcement learning control strategy for differential pressure setpoint in large-scale multi-source looped district cooling system

Large-scale multi-source looped district cooling (MLDC) systems are expected to be a promising solution to support decarbonization goals and combat climate change owing to their advanced structure. Differential pressure control is one of the most common methods to improve the energy and hydraulic performance of MLDC systems. Traditional rule-based control (RBC) and model-based control may not be appropriate for large-scale systems because of the need for expert knowledge and accurate models. Reinforcement learning control (RLC) has attracted considerable research attention owing to its efficiency and flexibility. However, very little is known about RLC in large-scale heating, ventilation, and air conditioning (HVAC) systems and complex issues. Therefore, this study employs a reinforcement learning technique to optimize the differential pressure setpoints of multiple cold sources, which can achieve energy savings and fulfill hydraulic head demands simultaneously. In this study, a Modelica model of a large-scale MLDC system in Beijing was developed as a virtual environment. The Modelica-Python co-simulation testbed for RLC was then implemented. The results show that RLC can save up to 12.99% of the annual distribution energy consumption while achieving a good hydraulic performance. As an information base for a variety of stakeholders, this study offers a reinforcement learning solution that can improve the operating performance of large-scale HVAC systems.

Accelerating the Performance of Fuzzy-FPGA Based Control in LabVIEW for Trajectory Tracking Problems

Fuzzy Logic (FL) is well-known as an intuitive framework to tackle imprecision and uncertainty while allowing to model expert's knowledge in rule-based control. The objective of this study is to investigate the design of embedded fuzzy control systems combining fast execution and parallel processing capabilities provided by Field Programmable Gate Arrays (FPGAs) and Reconfigurable Inputs/Outputs (RIO) boards. We implemented a suitable fixed-point FL to realize a computationally efficient control framework on an FPGA target. As such, the paper provides a concise method to deploy Fuzzy Logic Control (FLC) using RIO-FPGA technology. We also provide a technique for implementing the three stages that constitute the FLC structures in LabVIEW environment using fixed math operations. The experimental work involves tracking the trajectory of a mobile robot and shows the feasibility and the efficiency of the proposed FLC-FPGA controller in comparison to the traditional PID-FPGA controller, along with the added benefits of fuzzy control frameworks.