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329 results about "Fuzzy model" patented technology
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Fuzzy model. [¦fəz·ē ′mäd·əl] (mathematics) A finite set of fuzzy relations that form an algorithm for determining the outputs of a process from some finite number of past inputs and outputs.
Intelligent robust control system for motorcycle using soft computing optimizer
A Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a motorcycle is described. In one embodiment, a simulation model of the motorcycle and rider control is used. In one embodiment, the simulation model includes a feedforward rider model. The SC optimizer includes a fuzzy inference engine based on a Fuzzy Neural Network (FNN). The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and teaching signal selection and generation. The user selects a fuzzy model, including one or more of: the number of input and / or output variables; the type of fuzzy inference; and the preliminary type of membership functions. A Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal. The GA produces a near-optimal FNN. The near-optimal FNN can be improved using classical derivative-based optimization procedures. The FIS structure found by the GA is optimized with a fitness function based on a response of the actual plant model of the controlled plant. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.
Owner:YAMAHA MOTOR CO LTD
Resource allocation using relational fuzzy modeling
InactiveUS20070192261A1Easy to explainValidate easilyResource allocationDigital computer detailsResource allocation systemsNormalized systems
A system and method for allocating computing resources among a plurality of units in a distributed network. A system is provided that includes: a relational fuzzy modeling system for providing a relational fuzzy model based on historical resource parameter data and historical system parameter data, and for predicting and allocating resource requirements for each unit based on the relational fuzzy model; a normalization system for ensuring that a sum of the predicted resource requirements is no more than a total available amount of actual resources available on the plurality of units; and an extra resource allocation system for allocating extra resources when the sum of the predicted resource requirements is less than the total available amount of resources available on the plurality of units.
Owner:IBM CORP
Intelligent electronically-controlled suspension system based on soft computing optimizer
InactiveUS20060293817A1Near-optimal FNNMaximises informationDigital data processing detailsAnimal undercarriagesInput/outputSoft computing
A Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a suspension system is described. The SC optimizer includes a fuzzy inference engine based on a Fuzzy Neural Network (FNN). The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and teaching signal selection and generation. The user selects a fuzzy model, including one or more of: the number of input and / or output variables; the type of fuzzy inference model (e.g., Mamdani, Sugeno, Tsukamoto, etc.); and the preliminary type of membership functions. A Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal. The GA produces a near-optimal FNN. The near-optimal FNN can be improved using classical derivative-based optimization procedures. The FIS structure found by the GA is optimized with a fitness function based on a response of the actual suspension system model of the controlled suspension system. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.
Owner:YAMAHA MOTOR CO LTD
Systems and methods for diagnosing the cause of trend shifts in performance data
InactiveUS7031878B2Improve performanceFunction increaseVehicle testingAnalogue computers for vehiclesData setAlgorithm
Automated systems and methods for diagnosing the cause of a trend shift in performance data associated with a system including selecting a fuzzy model describing how the mean associated with a parameter shifts when a predetermined problem occurs and receiving performance data associated with the system. The diagnostic systems and methods also including applying a preferred set of piecewise regressions. The diagnostic systems and methods further including selecting a start date and selecting data sets near the start date and an end date that have a relatively large size without violating normal scatter. The diagnostic systems and methods still further including measuring the mean shift between a plurality of samples using one or more statistical tests and combining the results with the fuzzy model to achieve a diagnosis.
Owner:GENERAL ELECTRIC CO
Super-short-term prediction method of photovoltaic power station irradiance
ActiveCN103559561AUltra-short-term forecasting is effectively completedForecast effectively doneForecastingAlgorithmShort terms
The invention discloses a super-short-term prediction method of photovoltaic power station irradiance. The method includes the steps that irradiance data are extracted from a history database, data of a night time quantum are removed, corresponding extraterrestrial theoretical irradiance is calculated, data abnormal detection is carried out based on the preceding operations, and the data are normalized in the difference value ratio method of an extraterrestrial irradiance theoretical value and practical irradiance; a training sample set is extracted according to input and output dimensionality of a model; a model of an irradiance time sequence is built through an ANFIS, a the rule quantity and an initial parameter of the ANFIS model are determined in a subtractive clustering method, and a fuzzy model parameter is optimized in a counter propagation algorithm and a least square method; a prediction sample is input, and a prediction value is obtained through calculation; the prediction value is added to form a new sample set, and multiple steps of prediction are achieved in a cycling mode; counter normalization processing is carried out on the prediction value. Super-short-term prediction of the irradiance can be achieved only by means of a history irradiance time sequence, prediction accuracy is good and the method is easy to carry out.
Owner:SHANGHAI ELECTRICGROUP CORP
Satellite fault diagnosis and fault-tolerant control method based on T-S fuzzy model and learning observer
ActiveCN103488092AGuaranteed reliabilityEnsure safetyAttitude controlAdaptive controlAviationMathematical model
The invention relates to a satellite fault diagnosis and fault-tolerant control method based on a T-S fuzzy model and a learning observer. The satellite fault diagnosis and fault-tolerant control method based on the T-S fuzzy model and the learning observer is used for solving the problems that a conventional fault diagnosis method cannot be used for effectively treating the influence caused by space disturbance torque and ensuring the robustness of a fault diagnosis method, and a conventional fault-tolerant control method has a poor fault-tolerant property. The satellite fault diagnosis and fault-tolerant control method based on the T-S fuzzy model and the learning observer comprises the following steps: step 1. establishing a mathematical model of a non-linear satellite attitude control system; step 2. establishing the T-S fuzzy model of the satellite attitude control system by using results obtained in the step 1; step 3. designing a T-S fuzzy learning observer by using the results obtained in the step 2 so as to realize the robust fault detection, isolation and fault reconstruction of a satellite attitude angular velocity estimation and executing mechanism; 4. designing a state feedback fault-tolerant controller by using the results obtained in the step 3 so as to ensure that a closed loop of a satellite attitude control T-S fuzzy system is stable. The satellite fault diagnosis and fault-tolerant control method based on the T-S fuzzy model and learning observer can be applied to an aerospace field.
Owner:HARBIN INST OF TECH
T-S fuzzy model-based flexible spacecraft multi-objective integrated control method
ActiveCN104483835APreserve complex dynamicsResolve inhibitionAdaptive controlRobustificationDynamic models
The invention provides a T-S fuzzy model-based flexible spacecraft multi-objective integrated control method. According to the method, a T-S fuzzy dynamic model of a flexible spacecraft is established, and the universal approximation of the T-S fuzzy model of the spacecraft is proved; the uncertainty of the inertia of the spacecraft which is caused by relative movement of flexible parts, and various kinds of space disturbance torques are considered; a control-performance LMI description and multi-objective integrated LMI method is adopted; and a robust H-infinity state feedback controller which can make a closed-loop system meet pole constraints and control input constraints is designed based on the T-S fuzzy model of the spacecraft. As indicated by numerical value simulation results, the designed state feedback control system has the advantages of short dynamic adjustment time, fast response, small overshoot and high steady-state accuracy, and can effectively inhibit the vibration of the flexible parts caused by attitude variation, and has high robustness and adaptability for the uncertainty of the model of the spacecraft.
Owner:CHINA ACAD OF LAUNCH VEHICLE TECH
Wind energy converting system sliding mode control method and device based on T-S fuzzy model
ActiveCN105179164AStrong approach abilityGood model foundationWind motor controlMachines/enginesRobustificationEngineering
The invention provides a wind energy converting system sliding mode control method and device based on a T-S fuzzy model. To solve the problem of the fault of an actuator in a wind energy converting system, the T-S fuzzy model is utilized for describing a nonlinear wind energy converting system with uncertain actuator fault information, the approximation accuracy of a controlled object is improved, and a good model foundation is established for sliding mode control. Meanwhile, by means of a sliding mode controller designed based on the linear matrix inequality technology, the stability of the wind energy converting system is guaranteed, and the robustness and fault tolerance of the wind energy converting system are improved. The precise tracking of the rotating speed of a power generator and the electromagnetic torque can be achieved when the uncertain actuator fault exists in the wind energy converting system, and the maximum wind energy capturing of the wind speed below the rated value is achieved, and a valuable reference scheme is provided for efficient and stable running of the wind converting system.
Owner:JIANGSU UNIV OF SCI & TECH
Intelligent repair method of injection molding during plastic injection process and injection molding machine
InactiveCN1851715AAutomatically eliminate defectsDefect quick fixFuzzy logic based systemsSpecial data processing applicationsFuzzy ruleFuzzy reasoning
The present invented method consists of 1, establishing expert knowledge base and blur rule base; 2, based on said knowledge base and blur rule base, utilizing fuzzy reasoning technology establishing fault modification fuzzy model in injection process; 3, according to fed die setting process technological parameter, jet molding piece fault type and fault level, reasoning and calculating to obtain technological parameter correction. The present invention also obtains a jet molding unit. Said invention has advantages of quick adjusting technological parameter, fast modifying jet molding piece fault, and raising jet molding piece quality.
Owner:宁波海太塑料机械有限公司 +2
Lithium battery charge state online estimating method
ActiveCN105954679ARealize online real-time estimationImprove accuracyElectrical testingSimulationState of charge
The invention discloses a lithium battery state of charge (SOC) online estimating method. The online estimating method is characterized in that based on an expanded Kalman filtering method and combined with a TS fuzzy principle, a lithium battery real-time parameter open-circuit voltage UOC is precisely estimated, and the precise estimation of the lithium battery SOC is further realized. The method comprises the steps of: establishing a lithium battery improved dual-RC equivalent circuit model, utilizing an online TS fuzzy model to precisely calculate the battery open-circuit voltage UOC, and utilizing the expanded Kalman filtering method to estimate the lithium battery SOC in real time. Based on the estimation of the lithium battery SOC, the precision of the lithium battery SOC estimation meets predetermined requirements, the application of the TS fussy model also improves the precision of the lithium battery SOC estimation, and the high speed and the timeliness of the online estimation are also ensured.
Owner:HUAZHONG UNIV OF SCI & TECH
Electric locomotive adhesion control method and device
ActiveCN103183037AAchieving AdaptivenessRealize intelligenceLocomotivesAdaptive controlDrive wheelDigital filter
The invention provides an electric locomotive adhesion control method and a device. The method comprises the following steps: collecting rotating speed information of locomotive wheel shafts at real time; performing digital filtering processing of the rotating speed information, then converting into digital signals; determining whether the locomotive is in a coasting state according to the digital signals, if yes, calculating a wheel diameter correction coefficient according to the rotating speed information of two driving wheels when receiving a wheel diameter compensation control instruction, and if no, calculating a corresponding adhesion control coefficient according to the collected rotating speed information based on an established adhesion control type-II fuzzy model; and adjusting a correction coefficient of a traction motor torque given value according to the wheel diameter correction coefficient and the adhesion control coefficient based on a preset correction rule. The invention realizes the intelligentization of adhesion control, effectively improves the adhesion utilization rate between wheel tracks, and realizes optimization utilization of available adhesion strength of electric locomotive.
Owner:CHINA CNR CORPORATION
Predicative control method for modeling and running speed of adaptive network-based fuzzy inference system (ANFIS) of high-speed train
ActiveCN103019267ARun on timeGuaranteed uptimeSpeed/accelaration controlAdaptive controlAutomatic controlSimulation
The invention provides a generalized predicative control method of a high-speed train based on an adaptive network-based fuzzy inference system (ANFIS) model. The method utilizes a data-driven modeling method to build the ANFIS model in a running process of the high-speed train according to acquired high-speed train running data; adopts subtractive clustering to determine rule number and initial parameters of a fuzzy model, and adopts a back-propagation algorithm and a least square method to optimize parameters of the fuzzy model. The predictive tracking control method of electric multiple unit running speed on the basis of the ANFIS model obtains accurate controlled quantity through multistep predication and circular rolling so as to change blindness of adjustment by experience, enables the high-speed train running speed to track a target curve accurately, solves the problem of large lag, achieves on-schedule, safe and effective running of the train, and guarantees safety of passengers. The method is simple, practical, capable of achieving automatic drive control of the high-speed train and suitable for on-line monitoring and automatic control of a running process of the high-speed train.
Owner:EAST CHINA JIAOTONG UNIVERSITY
Robust fuzzy predictive fault-tolerant control method for interval time-varying-delaying system
The invention relates to a robust fuzzy predictive fault-tolerant control method for an interval time-varying-delaying system. The robust fuzzy predictive fault-tolerant control method includes the following steps that firstly, a T-S fuzzy model of a nonlinear system is constructed; secondly, the constructed T-S fuzzy model is converted into an expanded T-S fuzzy model; thirdly, according to the constructed expanded T-S fuzzy model, a fault-tolerant controller that satisfies a control law is designed; and fourthly, a gain of the fault-tolerant controller is solved in a form of linear matrix inequality and the robust fuzzy predictive fault-tolerant control law is calculated. According to the robust fuzzy predictive fault-tolerant control method for the interval time-varying-delaying system,the robust fuzzy predictive fault-tolerant control method with time-delay dependence is provided according to the fact that a type of the industrial process has the characteristics such as nonlinearity, uncertainty, unknown disturbance, interval time-varying-delaying and partial actuator failure, thus the industrial process operates more smoothly and efficiently, the performance of a system is improved, and the fault tolerance of the system is increased.
Owner:LIAONING UNIVERSITY OF PETROLEUM AND CHEMICAL TECHNOLOGY
Soft computing optimizer of intelligent control system structures
InactiveUS7219087B2Near-optimal FNNTelevision system detailsChaos modelsEngineeringInternal combustion engine
The present invention involves a Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a plant such as, for example, an internal combustion engine or an automobile suspension system. The SC optimizer includes a fuzzy inference engine based on a Fuzzy Neural Network (FNN). The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and teaching signal selection and generation. The user selects a fuzzy model, including one or more of: the number of input and / or output variables; the type of fuzzy inference model (e.g., Mamdani, Sugeno, Tsukamoto, etc.); and the preliminary type of membership functions. A Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal. The GA produces a near-optimal FNN. The near-optimal FNN can be improved using classical derivative-based optimization procedures. The FIS structure found by the GA is optimized with a fitness function based on a response of the actual plant model of the controlled plant. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.
Owner:YAMAHA MOTOR CO LTD
Fault detection method of nonlinear network control system based on event triggering mechanism
ActiveCN108667673AIncreased failure sensitivityTroubleshooting fault detection issuesElectric testing/monitoringData switching networksEvent triggerSystem failure
The invention provides a fault detection method of a nonlinear network control system based on an event triggering mechanism, and relates to the technical field of network system fault detection. Themethod comprises the following steps: firstly, establishing a T-S fuzzy model of the nonlinear network control system, setting an event triggering condition, establishing a fuzzy fault detection filter model, establishing a fault weighting system, and then establishing a fault detection system model; selecting an appropriate residual evaluation function and a detection threshold according to the fault detection system model, and detecting whether a fault of the nonlinear network control system occurs; and finally, further designing a parameter matrix and an event triggering matrix of a fault detection filter according to the stability of the fault detection system and sufficient conditions of existence of the fault detection filter. By adoption of the fault detection method of the nonlinear network control system based on the event triggering mechanism provided by the invention, the robustness to external disturbance and communication delay is greatly improved, and the limited networkresources and computing resources can be saved by the application of the event triggering mechanism.
Owner:NORTHEASTERN UNIV
Event triggering-based control method for T-S fuzzy network system
InactiveCN109062041AReduce communication loadLow costAdaptive controlFeedback controllerEvent trigger
The invention provides an event triggering-based control method for a T-S fuzzy network system. Stability and H<infinity> performance analysis is conducted on nonlinear NCSs based on an event triggering mechanism. A system model is built through a T-S fuzzy model; a transmission strategy of the event triggering mechanism (ETM) is provided by adopting a relative error threshold idea on the basis ofthe system model; a corresponding state feedback controller is designed by introducing a parallel distribution compensation (PDC) technology; and by constructing an appropriate Lyapunov-Krasovskii function method and combining a linear matrix inequality (LMI) idea, a sufficient condition for ensuring the mean square stability of the system and an index for ensuring the system to meet the certainH<infinity> performance are obtained.
Owner:HUZHOU TEACHERS COLLEGE
Self-checking test generator for partially-modeled processors by propagating fuzzy states
InactiveUS7483824B1Semiconductor/solid-state device testing/measurementElectronic circuit testingAlgorithmChoice test
A self-checking test generator program creates a self-checking test program that can test a device under test (DUT). The self-checking test generator selects instructions for a test. Selected instructions are executed on a software DUT model to generate results that can be self-checked by other instructions such as compare and branch instructions. The software DUT model has fuzzy models and unknown models for blocks in the DUT. Fuzzy models generate expected outputs for a block of the DUT. Fuzzy models may propagate unknown data from their inputs to their outputs. Unknown models do not predict expected outputs. Instead, unknown models always output unknown (X). Over time, as more of the DUT logic is modeled, unknown models may be replaced with fuzzy models.
Owner:AZUL SYSTEMS
System for soft computing simulation
InactiveUS20060218108A1Near-optimal FNNGenetic modelsDigital computer detailsFuzzy inferenceAlgorithm
The present invention involves a Soft Computing Optimizer (SCOptimizer) for designing a Knowledge Base (KB) to be used in a control system for controlling a plant. The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and training signal selection and generation. The user selects a fuzzy model, including one or more of: the number of input and / or output variables; the type of fuzzy inference model (e.g., Mamdani, Sugeno, etc.); and the preliminary type of membership functions. A Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal.
Owner:YAMAHA MOTOR CO LTD
Clearness processing method for defocus blurred image
The invention discloses a defocus blurred image sharpness processing method, which is implemented according to the following steps: firstly, the mean value and the variance of the edge width is calculated according to gradient information of an image; an initial parameter of a fuzzy model is obtained according to the statistical data; secondly, the blurred image is divided into concentric circles around the center of the image and is broken down into sub-images, wherein, the number of the concentric circles is k and the number of the sub-images is k+1; the corresponding blurred initial semidiameters are distributed to the sub-images; the optimal blurred semidiameter is found through an iterative mode, and the sharpness processing of all sub-images is performed by adopting a frequency domain inverse-filtering manner; finally, all the sub-images are added to synthesize the whole sharpness image. The defocus blurred image sharpness processing method overcomes the limitations in the existing circular disc function modeling recovery method based on the fixed semidiameter, and serves the purpose for restoring the sharp image.
Owner:XIAN UNIV OF TECH
A quadrotor attitude control method based on a T-S fuzzy model
The invention discloses a quadrotor attitude control method based on a T-S fuzzy model. According to the invention, a T-S fuzzy model is derived by a simplified attitude dynamic equation; a fuzzy state feedback controller is designed by using a parallel step-by-step compensation technique; while the deviation between the simplified model and an actual model is compensated, stable control of an attitude angle of a quadcopter is realized. According to the invention, compared with a quadcopter attitude dynamical system having strong nonlinearity and strong coupling performance, a more reasonable simplified model is brought forward; the simplified model derived from small angle assumption carried out only on roll angles is more accurate than the model derived from small angle assumption carried out both on the roll angles and pitch angles simultaneously; the model deviation in designing of a controller can be reduced as much as possible; the whole control structure is relatively simple; the calculating efficiency is high; and the convergence speed is fast.
Owner:成都优艾维智能科技有限责任公司
Sparse characteristics based video image de-blurring method
ActiveCN106204472ASolve for stabilityGood deblurring effectImage enhancementDiagonal matrixVideo image
The invention provides a sparse characteristics based video image de-blurring method, comprising the following steps: a modeling step of establishing a de-fuzzy model of weighted total variation regularization constraint described in the figure wherein / x represents a restored clear image, B a fuzzy kernel, y a generated blur image, [miu] an adjustable parameter, W a diagonal matrix of weights, and Vx includes the first-order differentials in the transverse, longitudinal and diagonal directions; and a solving step where an alternating iterative method is used to obtain the updated pixel weights of the / x and repetitive iterations are performed until the / x is converged. According to the invention, the de-blurring of a blurred image can be realized for a restored clear image. Moreover, the non-convex constraint model is improved through the method of weight assignment, and the convex optimization model is established to make the first-order differential constraint model of the image sparser and the possibility for rapid solution for the model.
Owner:PEKING UNIV
Servo system controller with self-adapting fuzzy frictional compensation
InactiveCN101510072AHigh precisionStrong nonlinear approximation capabilityAdaptive controlControl theorySelf adaptive
The invention discloses a servo system controller with self-adaptive fuzzy friction compensation. The servo system controller is used for improving the output tracking precision and fast response of a motor servo system, and is particularly applicable to a precision motor servo system which requires high precision and fast response. The invention comprises a parameter self-adaptive adjustment module, a fuzzy friction compensator and a robust control module. By adopting a fuzzy model to approximate a friction force model, the online assessment of the friction force value is realized by a self-adaptive adjustment of the fuzzy model parameters, and then a friction compensation is carried out so as to eliminate the adverse effects of the friction force on the output tracking precision and fast response of the servo system; and the adjustment of the fuzzy model parameters adopts a composite self-adaptive law and simultaneously uses the information relative to the system output error and the parameter evaluation error for carrying out the parameter adjustment so as to improve the parameter convergence speed. As the controller can realize fast and accurate friction model evaluation and friction compensation, the output tracking precision and fast response of the servo system can be greatly improved.
Owner:BEIJING INSTITUTE OF TECHNOLOGYGY
Control method applicable for high speed switched reluctance motor position-less sensor
InactiveCN101604946ASave memorySuitable for position detectionConverting sensor output electrically/magneticallyElectronic commutatorsInternal memoryLocation detection
The invention discloses a control method applicable for a high speed switched reluctance motor position-less sensor, belonging to the technical field of switched reluctance motor control. The invention, according to the characteristics of the switched reluctance motor, simplifies the rotor position detection into detection of commutation position. A fuzzy model of the non-linear relation with respect to the magnetic flux linkage (reference magnetic flux linkage) of the commutation position and the magnetic flux linkage of the alignment position of the stator and the rotor is established, so that the reference magnetic flux linkage at the reasonable commutation position thetah given at will and selected by comprehensively considering torque and output efficiency can be obtained only by storing the magnetic flux linkage-current feature curve of the alignment position of the stator and the rotor. By comparing the magnetic flux linkage detected in real time with the reference magnetic flux linkage, a corresponding commutation signal can be obtained. The invention not only has the advantages small required internal memory, simple and rapid algorithm, no need of hardware addition and the like, but also can optimally select the reasonable commutation position according to the features of the motor running, thus being very suitable for position detection during high speed running.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
Intelligent robust control system for motorcycle using soft computing optimizer
The invention describes a vehicular intelligent touch control system using a soft computing (SC) optimizer, and the SC is used to design a knowledge base (KB) used in the vehicular control system. A user selection fuzzy model comprises one or a plurality of input and / or output variable numbers, fuzzy inference models, and the primary models of membership functions. A genetic algorithm (GA) is used to optimize language variable parameters and an input-output training mode. The GA is used to optimize a rule base through the fuzzy model, the optimized language variable parameters and teaching signals. The GA produces an approximately optimal fuzzy nerve network (FNN). The approximately optimal FNN can be improved through examples based on a derivative optimization procedure. The fuzzy inference systems (FIS) constructed by the GA can be optimized through the adaptation function of a controlled-shop-based real shop model response. The touch KB produced by the SC optimizer is usually smaller than the KB produced by the prior art.
Owner:YAMAHA MOTOR CO LTD
Multi-fault diagnosis and fault-tolerant control of wind turbine system
ActiveCN106014877AGuaranteed accuracyEngine fuctionsMachines/enginesActive faultWind energy conversion
The invention discloses a multi-fault diagnosis and fault-tolerant control method of a wind turbine system. The method comprises the following steps: firstly, establishing a global fuzzy model of the wind turbine system by utilizing the T-S fuzzy algorithm; converting an actuator fault into a sensor fault by utilizing the characteristic of combining the sensor hardware redundancy technology with the actuator fault, and establishing a multi-fault diagnosis logical table to realize multi-fault detection; then, introducing a filter, converting the sensor fault into the actuator fault, establishing a virtual actuator fault, and realizing simultaneous reconstitution of two faults through reconstitution of the virtual actuator fault; finally, revising input and output of a controller based on a fault reconstitution value to realize active fault-tolerant control. The method has the following advantages: diagnosis and reconstitution of the wind turbine system with concurrent actuator and sensor faults can be simultaneously realized, accurate fault information is obtained in a real-time and on-line manner, fault-tolerant control is realized, the capacity of processing unknown faults of the system is enhanced, and the wind energy conversion efficiency under the fault is improved.
Owner:国电崇礼和泰风能有限公司
Feedback control method of wind energy converting system based on fuzzy performance estimator
The invention discloses a feedback control method of a wind energy converting system based on a fuzzy performance estimator and designs a T-S fuzzy state feedback control system including the fuzzy performance estimator specifically to characteristics of strong nonlinearity and parameter uncertainty of the wind energy converting system, belonging to the feedback control field. Firstly, a nonlinear model and a T-S fuzzy model of the wind energy converting system are provided; secondly, the fuzzy performance estimator of the system is designed based on the T-S fuzzy model of the wind energy converting system; then, a linear controller is designed in each linear local model respectively; and a fuzzy state feedback controller of a global model is constituted by dint of membership function. The method provided by the invention has the advantages of a controller is designed based on the fuzzy performance estimator; the feedback control method ensures that a closed-loop control system has good tracking performance; simultaneously, preferable dynamic performance and stable-state performance can be acquired.
Owner:JIANGNAN UNIV
Wind power generation T-S fuzzy robust scheduling fault-tolerant control method
InactiveCN110566403AReasonable designRealize scheduling fault-tolerant control functionWind motor controlEngine fuctionsMathematical modelFuzzy scheduling
Owner:TIANJIN UNIVERSITY OF SCIENCE AND TECHNOLOGY
Random fuzzy power flow algorithm for distributed wind power, photovoltaic power generation and other uncertain energy sources connected to power system
ActiveCN105633948AData processing applicationsAc network circuit arrangementsVoltage amplitudePower flow
The invention belongs to the technical field of a random fuzzy power flow algorithm for distributed wind power, photovoltaic power generation and other uncertain energy sources connected to a power system, and discloses a power flow algorithm considering the fact that the power of the uncertain energy sources connected to the power system has random fuzzy characteristics. The load of the uncertain energy sources connected to the power system is taken as a random fuzzy variable, and the node load power is randomly simulated in a fuzzy manner; the node load power is embedded to Newton-Raphson power flow calculation to obtain voltage amplitude values and phase angle data of the corresponding nodes of the system; the probability distribution characteristics of the node voltage amplitude values and the phase angles are subjected to extraction and statistics; a probability distribution model suitable for fitting the node voltage amplitude values and the phase angles, and the parameter fuzzy characteristics are analyzed and determined; and the random fuzzy model for the node voltages and phase angles is established. According to the power flow algorithm, the influences on the node voltages of the power distribution network from the uncertainties of the distributed type power supply outputs can be more comprehensively analyzed, so that corresponding guiding evidences can be provided for power generation plan arrangement and dispatching for a large number of distributed wind power, photovoltaic power generation and other uncertain energy sources connected to the power system in the future.
Owner:CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
Intelligent robust control system for motorcycle using soft computing optimizer
Owner:YAMAHA MOTOR CO LTD
Image fuzzy model parameter analysis method based on depth learning
InactiveCN104408692AHigh precisionGood estimateImage enhancementDeep belief networkPattern recognition
The invention designs a fuzzy model parameter analysis method based on depth learning. The method is characterized by including: step 1, adopting a depth belief network structure for extracting and classifying fuzzy features, namely using a semi-supervised depth belief network to project an input image block to a difference feature space, and then classifying each feature; step 2, recognizing parameters of a fuzzy kernel, namely extracting from the edge of a transform domain to guarantee that the depth belief network structure confirms fuzzy parameters with high accuracy. By the image fuzzy model parameter analysis method based on depth learning, the fact that the depth belief network structure confirms the fuzzy parameters with high accuracy can be guaranteed; according to repeated trial verification in multiple image databases such as Berkeley segmentation, Pascal VOC 2007 and the like, favorable performances superior to those of the existing best fuzzy estimation method are achieved.
Owner:NANJING UNIV OF INFORMATION SCI & TECH
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