다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Differential-Difference Equations. By Richard Bellman and Kenneth L. Cooke. Pp. xvi, 465. 1963. (Academic Press)

Adaptive neuro-fuzzy maximal power extraction of wind turbine with continuously variable transmission

Solar photovoltaic technology is one of the most important resources of renewable energy. However, the current solar photovoltaic systems have significant drawbacks, such as high costs compared to fossil fuel energy resources, low efficiency, and intermittency. Capturing maximum energy from the sun by using photovoltaic systems is challenging. Several factors that affect the energy output of such systems include the photovoltaic material, geographical location of solar irradiances, ambient temperature and weather, angle of sun incidence, and orientation of the panel. This study reviews the principles and mechanisms of photovoltaic tracking systems to determine the best panel orientation. The tracking techniques, efficiency, performance, advantages, and disadvantages of simple tracking systems are compared with those of state-of-the-art tracking systems. Diverse types of solar tracking systems based on their technologies and driving methods will be presented and categorized.The future trends of tracking systems are also highlighted.

Advances in solar photovoltaic tracking systems: A review

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult @?@? control using conventional techniques. Here, a novel design of an adaptive neuro fuzzy inference strategy (ANFIS) for controlling input displacement of a new adaptive compliant gripper is presented. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Since the conventional control strategy is a very challenging task, fuzzy logic based controllers are considered as potential candidates for such an application. Fuzzy based controllers develop a control signal which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS controller, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.

Adaptive neuro fuzzy controller for adaptive compliant robotic gripper

The aim of this work is to demonstrate the usefulness of Adaptive Neuro Fuzzy Inference System (ANFIS) for tracking Maximum Power Point (MPP) in stand-alone photovoltaic system. Maximum Power Point Tracking (MPPT) is one of approaches which boost efficiency of PhotoVoltaic (PV) cells by the load matching between the PV cells and the load. The key problem is that maximum power is not achieved because PV cells power is affected by weather conditions such as the solar irradiation and the temperature, thus, the MPP is changed during daylight hours and year seasons. Therefore, it is necessary to design an appropriate controller based on one of techniques to track MPP. These techniques are based on true or estimated searching mechanism to MPP. True searching mechanism based techniques like incremental conductance method and perturb and observe method are efficient but they are less stable, more oscillatory about MPP and sensitive to a high frequency noise. Generally, estimated searching mechanism based techniques like constant voltage method and fractional open circuit voltage method are less efficient, but they are stable and no sensitive to a high frequency noise. In this paper, the ANFIS-reference model method in addition to the incremental conductance method and constant voltage method have been studied, designed and implemented using Field Programmable Gate Array (FPGA) board to compare the performance of each method. The proposed ANFIS-reference model controller is efficient since it has been trained offline using Matlab tool with practical data sets. Based on our knowledge, this paper is the first paper which introduces practical implementation of ANFIS-reference model based MPPT for photovoltaic system using FPGA board. The results reveal that the ANFIS-reference model controller has more efficient and better dynamic response than the incremental conductance method and constant voltage method.

Design and implementation of ANFIS-reference model controller based MPPT using FPGA for photovoltaic system

The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as much control objectives, as possible. Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road. The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order (FOPID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.

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Design an intelligent controller for full vehicle nonlinear active suspension systems

An optimal Fractional Order PID (FOPID) controller is designed for a full vehicle nonlinear active suspension system. The optimal values of FOPID controller parameters for minimizing the cost function are tuned using an Evolutionary Algorithm (EA), which offers an optimal solution to a multidimensional rough objective function. The fitness parameters of FOPID controller (proportional constant P, integral constant I, derivative constant D, integral order and derivative order ) are selected from ranges of reliable values, depending on survival-to-the-fitness principle used in the biology science. A full vehicle nonlinear active suspension model including hydraulic actuators, nonlinear dampers and nonlinear springs has been proposed with structural and analytical details. The nonlinear frictional forces due to rubbing of piston seals with the cylinders wall inside the actuators are taken into account to find the real supply forces generated by the hydraulic actuator. The results of the full vehicle nonlinear suspension system using the FOPID controller are compared with the corresponding passive suspension system (system without controller). The controlled suspension system has been investigated under typical vehicle maneuvers: cruising on rough road surface, sharp braking and cornering. The results have clearly shown the effectiveness and robustness of the proposed controller.

Design of Fractional Order controller based on Evolutionary Algorithm for a full vehicle nonlinear active suspension systems

The main objectives of designing the controller for vehicle suspension systems are to reduce the discomfort sensed by passengers that arises from road roughness and to increase the road handling associated with the pitching and rolling movements. This necessitates a very fast and accurate controller to meet as many control objectives as possible. This paper deals with an artificial intelligent neurofuzzy (NF) technique to design a robust controller. The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers. The approach of the proposed controller is to minimize the vibration on each corner of the vehicle by supplying control forces to the suspension system when travelling on a rough road. The other purpose of using the NF controller for the vehicle model is to reduce the body inclinations that are made during intensive maneuvers, including braking and cornering. A full vehicle nonlinear active suspension system is introduced and tested. The robustness of the proposed controller is being assessed by comparison with an optimal proportional-integral-derivative (PID) controller. The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function.

A neurofuzzy controller for full vehicle active suspension systems

The main objective of designed the controller for a vehicle suspension system is to reduce the discomfort sensed by passengers which arises from road roughness and to increase the ride handling associated with the pitching and rolling movements This necessitates a very fast and accurate controller to meet as much control objectives, as possible Therefore, this paper deals with an artificial intelligence Neuro-Fuzzy (NF) technique to design a robust controller to meet the control objectives The advantage of this controller is that it can handle the nonlinearities faster than other conventional controllers The approach of the proposed controller is to minimize the vibrations on each corner of vehicle by supplying control forces to suspension system when travelling on rough road The other purpose for using the NF controller for vehicle model is to reduce the body inclinations that are made during intensive manoeuvres including braking and cornering A full vehicle nonlinear active suspension system is introduced and tested The robustness of the proposed controller is being assessed by comparing with an optimal Fractional Order PIλDμ (FOPID) controller The results show that the intelligent NF controller has improved the dynamic response measured by decreasing the cost function

/pdf/adaptive-neuro-fuzzy-inference-controller-for-full-vehicle-1gtjusec05.pdf

Ammar A. Aldair

Papers

Design and implementation of ANFIS-reference model controller based MPPT using FPGA for photovoltaic system

Design an intelligent controller for full vehicle nonlinear active suspension systems

Design of Fractional Order controller based on Evolutionary Algorithm for a full vehicle nonlinear active suspension systems

A neurofuzzy controller for full vehicle active suspension systems

Adaptive Neuro Fuzzy inference controller for full vehicle nonlinear active suspension systems