5,286 research outputs found

    An Optimized Type-2 Self-Organizing Fuzzy Logic Controller Applied in Anesthesia for Propofol Dosing to Regulate BIS

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    During general anesthesia, anesthesiologists who provide anesthetic dosage traditionally play a fundamental role to regulate Bispectral Index (BIS). However, in this paper, an optimized type-2 Self-Organizing Fuzzy Logic Controller (SOFLC) is designed for Target Controlled Infusion (TCI) pump related to propofol dosing guided by BIS, to realize automatic control of general anesthesia. The type-2 SOFLC combines a type-2 fuzzy logic controller with a self-organizing (SO) mechanism to facilitate online training while able to contend with operational uncertainties. A novel data driven Surrogate Model (SM) and Genetic Programming (GP) based strategy is introduced for optimizing the type-2 SOFLC parameters offline to handle inter-patient variability. A pharmacological model is built for simulation in which different optimization strategies are tested and compared. Simulation results are presented to demonstrate the applicability of our approach and show that the proposed optimization strategy can achieve better control performance in terms of steady state error and robustness

    The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

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    Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

    Computational intelligence techniques for HVAC systems: a review

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    Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

    Target Localization With Fuzzy-Swarm Behavior

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    In this paper describes target localization using deliberates fuzzy and swarm behavior. Localization is the process of determining the positions of robots or targets in whole swarms environment. To localize the target in real environment, experiment is conducted utilize three identical robots with different color. Every robot has three infrared sensors, two gas sensors, 1 compass sensor and one X-Bee. A camera in the roof of robot arena is utilized to determine the position of each robot with color detection methods. Swarm robots are connected to a computer which serves as an information center. Fuzzy and swarm behavior are keeping the swarm robots position and direction with a certain distance to the target position. From the experimental results the proposed algorithm is able to control swarm robots, produce smooth trajectory without collision and have the ability to localize the target in unknown environmen

    Intelligent Torque Vectoring Approach For Electric Vehicles With Per-Wheel Motors

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    Transport electrification is currently a priority for authorities, manufacturers, and research centers around the world. The development of electric vehicles and the improvement of their functionalities are key elements in this strategy. As a result, there is a need for further research in emission reduction, efficiency improvement, or dynamic handling approaches. In order to achieve these objectives, the development of suitable Advanced Driver-Assistance Systems (ADAS) is required. Although traditional control techniques have been widely used for ADAS implementation, the complexity of electric multimotor powertrains makes intelligent control approaches appropriate for these cases. In this work, a novel intelligent Torque Vectoring (TV) system, composed of a neuro-fuzzy vertical tire forces estimator and a fuzzy yaw moment controller, is proposed, which allows enhancing the dynamic behaviour of electric multimotor vehicles. The proposed approach is compared with traditional strategies using the high fidelity vehicle dynamics simulator Dynacar. Results show that the proposed intelligent Torque Vectoring system is able to increase the efficiency of the vehicle by 10%, thanks to the optimal torque distribution and the use of a neuro-fuzzy vertical tire forces estimator which provides 3 times more accurate estimations than analytical approaches.The research leading to these results has been supported by the ECSEL Joint Undertaking under Grant agreement no. 662192 (3Ccar). This Joint Undertaking receives support from the European Union Horizon 2020 research and innovation program and the ECSEL member states

    Intelligent Torque Vectoring Approach for Electric Vehicles with Per-Wheel Motors

    Get PDF
    Transport electrification is currently a priority for authorities, manufacturers, and research centers around the world. The development of electric vehicles and the improvement of their functionalities are key elements in this strategy. As a result, there is a need for further research in emission reduction, efficiency improvement, or dynamic handling approaches. In order to achieve these objectives, the development of suitable Advanced Driver-Assistance Systems (ADAS) is required. Although traditional control techniques have been widely used for ADAS implementation, the complexity of electric multimotor powertrains makes intelligent control approaches appropriate for these cases. In this work, a novel intelligent Torque Vectoring (TV) system, composed of a neuro-fuzzy vertical tire forces estimator and a fuzzy yaw moment controller, is proposed, which allows enhancing the dynamic behaviour of electric multimotor vehicles. The proposed approach is compared with traditional strategies using the high fidelity vehicle dynamics simulator Dynacar. Results show that the proposed intelligent Torque Vectoring system is able to increase the efficiency of the vehicle by 10%, thanks to the optimal torque distribution and the use of a neuro-fuzzy vertical tire forces estimator which provides 3 times more accurate estimations than analytical approaches.The research leading to these results has been supported by the ECSEL Joint Undertaking under Grant agreement no. 662192 (3Ccar).This Joint Undertaking receives support from the European Union Horizon 2020 research and innovation program and the ECSEL member states

    Machine Model Based Speed Estimation Schemes for Speed Encoderless Induction Motor Drives: a Survey

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    Speed Estimation without speed sensors is a complex phenomenon and is overly dependent on the machine parameters. It is all the more significant during low speed or near zero speed operation. There are several approaches to speed estimation of an induction motor. Eventually, they can be classified into two types, namely, estimation based on the machine model and estimation based on magnetic saliency and air gap space harmonics. This paper, through a brief literature survey, attempts to give an overview of the fundamentals and the current trends in various machine model based speed estimation techniques which have occupied and continue to occupy a great amount of research space
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