106 results about "Fuzzy logic controller" patented technology

An ultrasound scanner is equipped with one or more fuzzy control units that can perform adaptive system parameter optimization anywhere in the system. In one embodiment, an ultrasound system comprises a plurality of ultrasound image generating subsystems configured to generate an ultrasound image, the plurality of ultrasound image generating subsystems including a transmitter subsystem, a receiver subsystem, and an image processing subsystem; and a fuzzy logic controller communicatively coupled with at least one of the plurality of ultrasound imaging generating subsystems. The fuzzy logic controller is configured to receive, from at least one of the plurality of ultrasound imaging generating subsystems, input data including at least one of pixel image data and data for generating pixel image data; to process the input data using a set of inference rules to produce fuzzy output; and to convert the fuzzy output into numerical values or system states for controlling at least one of the transmit subsystem and the receiver subsystem that generate the pixel image data.

A wind turbine with a sensor that measures the out-of-plane deflection of the blades and a controller that uses the signal from the sensor to determine the risk of a tower strike. The controller takes any necessary action to prevent a tower strike when it determines that the risk of a strike is high. The sensor can include strain gages or accelerometers mounted on the blades or it can include a fixed sensor mounted on the side of the tower to measure tower clearance as the blade passes by. The control action taken can include pitching blades, yawing the nacelle, or stopping the turbine. The controller is preferably a fuzzy logic controller.

A wind turbine with a sensor that measures the out-of-plane deflection of the blades and a controller that uses the signal from the sensor to determine the risk of a tower strike. The controller takes any necessary action to prevent a tower strike when it determines that the risk of a strike is high. The sensor can include strain gages or accelerometers mounted on the blades or it can include a fixed sensor mounted on the side of the tower to measure tower clearance as the blade passes by. The control action taken can include pitching blades, yawing the nacelle, or stopping the turbine. The controller is preferably a fuzzy logic controller.

A fuzzy logic control arrangement is provided for an impedance match network of the type that is typically employed between a source of RF power at a given impedance, e.g., 50 ohms, and a non-linear load whose impedance can vary in magnitude and phase, e.g., an RF plasma. The fuzzy logic controller fuzzifies the phase and the magnitude error signals. The error signals are applied to a fuzzy logic interference function based on a number of fuzzy sets. The values of the error signals enjoy some degree of membership in one or more fuzzy sets. Fuzzy logic rules are applied to the phase and magnitude error signals. In a defuzzification stage, drive signal values are obtained for moving the tuning elements of the variable impedances. The drive signal values are weighted according to respective fuzzy inference functions for which the error signals enjoy membership. Then the weighted drive signal values are combined to produce output drive signals.

The invention discloses a key target identification method for an automobile cruising system. Based on the fuzzy control theory, a multi-target automobile lane changing fuzzy logic controller is designed, a dangerous lane changing automobile is prejudged, and effective identification of a straight lane key target is determined. The tilting motion influence of automobile bend running is considered, an extension Kalman filtering on-line real-time estimation of an automobile mass center side slip angle and a road curvature is achieved, the bend key target identification method is provided, a key target judgment basis is given, and effective identification of the bend key target is achieved.

A method and apparatus for LCL resonant converter control utilizing Asymmetric Voltage Cancellation is described. The methods to determine the optimal trajectory of the control variables are discussed. Practical implementations of sensing load parameters are included. Simple PI, PID and fuzzy logic controllers are included with AVC for achieving good transient response characteristics with output current regulation.

The system includes following parts: image buffer; image analyzer in multiple scales connected to the image buffer; fuzzy logic controller (FLC) connected to the image analyzer in multiple scales; edge enhancement processor connected to image buffer and FLC; noise remover connected to connected to image buffer and FLC; synthesizer connected to image buffer, noise remover, edge enhancement processor, and FLC. The method includes following steps: based on fetched original image data to obtain edge information of original image; based on edge information of original image to obtain variance of gain and noise of original image; based on calculated data of edge enhanced image and noise removed image to obtain final processed data. Using edge analysis in multiple scales, under control of fuzzy logic, the invention realizes self-adaptive noise reduction process and edge enhancement process for images.

A method of managing power between the multiple components of a hybrid electrical energy storage system (HESS) that includes providing at least two power storage elements, and at least one renewable power source. The method further includes managing the power flow among the at least two power storage elements with a fuzzy logic controller. The fuzzy logic controller uses a hardware processor that is configured to increase or decrease current to each of the at least two power storage elements using a fuzzy rule base that is dependent upon at least one of a state of charge for each of the at least two power storage elements, and a requested power demand of the hybrid electrical storage system.

The invention is directed to the technological field of bowl mill ore-feeding amount control. Specifically speaking, the invention relates to a bowl mill ore-feeding amount intelligent control apparatus, comprising a PLC provided with a PID controller and a fuzzy logic controller, an input parameter sensor, a hand-operated/ automatic change over switch, an ore-feeding amount setting apparatus, wherein the fuzzy logic controller, the input parameter sensor, the hand-operated/ automatic change over switch and the ore-feeding amount setting apparatus are respectively connected to the input interface of the PLC. The input interface of the PLC is connected with a bowl mill ore-feeding disc frequency converter. The fuzzy logic controller is an ore-feeding fuzzy logic controller. The input parameter sensor comprises a bowl mill ear, a bowl mill load sensor, a cyclone pressure transducer, a cyclone overflow granularity sensor and a belt electronic scale. The input parameter interface is connected with the fuzzy logic controller. According to the invention, the problem of different ore-drawing granularities caused by an inconsistent control on the ore-feeding belt is solved, the efficiency of the bowl mill is maximally improved and the output increases.

The invention relates to an energy flow controller for fuel battery electric motor car, and the controlling system is composed of pedal, entire controller ECU, fuel battery engine, nickel hydrogen battery package and driving system. The invention is to implement reasonable distribution of energy source between fuel battery and fuel storage battery and enhance and optimize power performance of the whole car. Its character: there is also an energy flow controller connected between the ECU and the driving system, where the energy flow controller is a module in the ECU, its input end is connected with the output end of a fuzzy logic controller (FLC), and its output end is connected with the input end of an electric motor controller in the driving system. It synthesizes advanced techniques in the fields of CAN field bus, fuzzy control, communication, etc.

A method of selection and/or handover for multi radio access technologies in a 5G cellular network. The method includes: 1) collecting, by a user terminal, load information of a candidate Access Point (AP) or an Evolved Node (H)eNB via a local entity of Access Network Discovery and Selection Function (L-ANDSF); 2) checking, by the user terminal, a Received Signal Strength (RSS) value of the candidate AP or (H)eNB, a Reference Signal Received Quality (RSRQ) and a Service Sensitivity to Latency; 3) based on the above information, evaluating suitability of an available radio access technology; 4) triggering, by a specific triggering event, a fuzzy logic controller, to select a most appropriate radio access technology for each session; and 5) establishing a new session to perform admission control or handover.

Apparatus for controlling a land vehicle which is self-driving or partially self-driving, which apparatus comprises a coarse tuning assembly (1, 2, 3) and a fine tuning assembly (4), the coarse tuning assembly (1, 2, 3) being such that It comprises: a. a sensor interface (1) which measures kinematic parameters including speed and braking, b. fuzzy descriptions to model guidance, navigation and control of the vehicle, the fuzzy descriptions including: (i) driver behaviour and driving dynamics, (ii) uncertainties due to the environment including weather, road conditions and traffic, and (iii) input faults including mechanical and electrical parts, and c. an adaptive fuzzy logic controller (3) for nonlinear MIMO systems (2) with subsystems which comprise fuzzification, inference, and output processing, which comprise both type reduction and defuzzification, and which provide stability of a resulting closed-loop system, the adaptive fuzzy logic controller (3) including: (i) inference engine identifying relationships using a rule base and outputs as 'fuzzy sets' to a type reducer, and (ii) output control demands including torque actuators to the fuzzyfier 'fuzzyfiying' the signal, and the fine tuning assembly (4) being such that it comprises: a. inputs from the coarse tuning assembly (1, 2, 3), b. precognition horizons determining how many future samples the objective function considers for minimization and the length of the control sequence computed, c. a linearized MIMO regression model extracted from the adaptive fuzzy logic controller (3) at each time step providing the 'fine' tuning parameters, and d. a non-linear dynamic linearized regression controller (4a) providing: (I) a crisp output signal feeding into APACC synthesis (4b) computing the optimal future vehicle guidance, navigation and control sequence, and (II) reduced set output and APACC synthesis (4b) feeding into the APACC linear logic system.

The invention introduces fuzzy logic in the adaptive power control of the wireless sensor network node, thus reducing the node energy consumption and prolonging the network lifetime. The system consists of a transmitting node and a receiving node which are shown in an appended drawing, wherein the transmitting node contains a transmitting module 101, a historical power module 102 and an adjusted value receiving module 103; the transmitting module transmits data packets according to the reference transmitting power provided by the historical power module; the adjusted value receiving module is used to update the historical power level in real time; the receiving node contains a receiving module 104, a QoS extraction module 105, a fuzzy computing module 106 and an adjusted value feedback module 107; the receiving module works in the physical layer and is used to receive signals; the QoS extraction module is used to collect the QoS parameter of a receiving terminal; the fuzzy computing module uses a fuzzy logic controller to calculate the adjusted value and submits the calculation result for the adjusted value feedback module; and the adjusted value feedback module is used to return the power adjusted value to the transmitting node.

The invention discloses a design method of a fuzzy logic motion controller of a forklift. The design method comprises the following steps: carrying out kinematical modeling of a forklift; obtaining a relation of error variables and designing optimized data; designing a fuzzy rule base by using a Lyapunov method; and carrying out designing and optimization of a membership function parameter. According to the invention, on the basis of a system stability theory in motion control, stability and stationarity of the controller are guaranteed; optimized design data are obtained based on a forklift ideal motion module; a membership function parameter in a fuzzy logic controller is optimized and designed by using a Newton iteration method. Therefore, the control precision is improved; the field debugging and testing time of the engineering staff is reduced; the efficiency is improved; and the cost is lowered. The design method has advantages of high reliability and practicability and high control precision and adaptability.

The invention discloses a method for optimizing two-layer embedded resistance layout by adopting a fuzzy genetic algorithm. The method comprises the following steps: performing genetic operation on upper and lower layer resistance respectively; performing initial population generation, crossover, variation and evolution reversion operation in sequence; sharing crossover probability Pc and variation probability Pm output by a fuzzy logic controller; during calculation of a fitness function and local catastrophe, evaluating and updating by taking two populations as a whole; after population update, judging once again; if a certain condition is satisfied, performing local catastrophe on the populations. Compared with a method for performing thermal layout optimization by adopting a standard genetic algorithm, the method has the advantages that remarkable advantages are achieved on the aspects of keeping population variety and searching a globally optimal solution, and ANSYS temperature field distribution and the thermal imaging result of an experimental sample plate indicate that the maximum temperature and the maximum temperature difference are reduced greatly. A two-layer electronic element thermal layout rule obtained according to an optimization result is suitable for embedded resistance, and plays a guidance role in the thermal layout design of other electronic elements.

The present invention discloses a photovoltaic cell MPPT simulation method based on fuzzy logic control. The method comprises design of a fuzzy logic controller and fuzzy logic control simulation; wherein the design of the fuzzy logic controller comprises: determining input/output amount and the structure of the controller, determining the fuzzy subset and the domain of the input/output amount, determining the subordinating degree function of the input/output amount and determining the fuzzy control rule, etc. Through combination of the control idea of the perturbation and observation method and the fuzzy logic control theory, the photovoltaic cell MPPT simulation method based on fuzzy logic control is provided; the simulation model is built to compared with and simulate the perturbation and observation method and the conductance increment method; according to the simulation result, the fuzzy logic control is very suitable for the MPPT control of the photovoltaic system so as to rapidly and accurately lock the MPP range, and the fluctuation is little after the system is stable.

The invention discloses a fuzzy hysteresis current control system and method for a permanent magnet synchronous motor. Speed error and error variation are taken as inputs of a fuzzy logic controller,an output G is matched with an output signal of the hysteresis controller, PWM (pulse width modulation) is controlled to generate six-channel PWM signals with corresponding duty cycles, and six powerswitching tubes of an inverter are controlled to enable the motor to run stably. Compared with the traditional method, the response speed and steady-state precision of current tracking control are better.

An apparatus for mitigating GIC (geomagnetically induced current) effects through a fuzzy logic controlled variable resistor. Under GIC conditions (or any unbalanced fault current condition), the GIC or unbalanced fault current flows through the neutral of a power transformer. It is detected by the fuzzy logic controller, which sends a signal to a switch to open. The resistor is in the circuit and impedes the flow of current through the neutral, thereby protecting the transformer from getting overheated.

The invention discloses an intelligent expressway tunnel ventilation device and an intelligent expressway tunnel ventilation method. The intelligent expressway tunnel ventilation device comprises a detection module, a forecasting module, a warning module and a control module, wherein the detection module comprises smoke VI, CO, particulate matter concentration and traffic flow TC detection elements; the forecasting module comprises a traffic flow forecasting module and a pollutant forecasting module; the warning module is a dynamic CO/VI warning module; the control module adopts an FLC (Fuzzy Logic Controller); the forecasting module is connected with the detection module; the warning module and the detection module are used as input ends; and the control module is connected with an output end and connected with a ventilation device in a tunnel. Compared with conventional ventilation device, an information fusion technology is fused in the intelligent expressway tunnel ventilation device, the ventilation device is adjusted by setting early warning parameters, calculating ventilation volume, distinguishing traffic state and controlling a decision module through a pollutant forecasting model, the tunnel can be subjected to ventilation control timely and effectively, and energy consumption can be reduced.

The invention discloses a method of micro grid energy scheduling based on dynamic adaptive fuzzy logic. The method includes the steps of taking one-day prediction information of a micro grid as a training set for generating an optimal fuzzy controller; taking the obtained prediction information as an analog input, and with the dual optimization goal of minimizing the operation cost of the micro grid and minimizing the power fluctuation of a public grid, performing dynamic adaptive design on two aspects, including the shape of a membership function and a fuzzy inference rule, of a fuzzy interference system through a new heuristic optimization algorithm; and further using the obtained optimal fuzzy controller for real-time control of the micro-grid energy management. For one thing, the adaptive optimal controller considers the running information for a period of time and adjusts its own fuzzy inference system according to different environments so that the optimal scheduling of the global time can be realized; and for another, since the fuzzy logic inference has good fault tolerance towards its own imprecise information, the method can effectively avoid the interference of the uncertainty caused by a prediction error.

The invention provides an FPGA (Field Programmable Gate Array) platform-based online evolution optimization system of a fuzzy logic controller, wherein a fuzzy relation calculation IP (Internet Protocol) core, a sensor control IP core, a genetic algorithm module, a fuzzy controller module and a sensor control module are comprised and the rule of a genetic algorithm optimized fuzzy logic controller is used. When the system operates, the control process of the fuzzy logic controller is divided into two stages, and an individual adaptive value of the genetic algorithm is divided into two parts to carry out partial substitution according to service conditions when individual substitution is carried out, therefore, the on-line evolution of the fuzzy logic controller is realized. In the invention, the on-line evolution optimization of the fuzzy logic controller is realized by adopting a software and hardware cooperative work manner, therefore, the burden of a CPU (Central Processing Unit) is lightened, and the operation speed is accelerated.

The invention discloses a method for controlling a hybrid energy storage system based on a fuzzy logic controller. The method mainly comprises a low pass filter, the fuzzy logic controller and a PSO (particle swarm optimization) algorithm, wherein the low pass filter is used for decomposing a power fluctuating signal into a high-frequency signal and a low-frequency signal; the fuzzy logic controller is used for processing the inputted low-frequency signal and an SOC (system on chip) signal of a supercapacitor through a logic relationship so as to obtain a control signal for charging and discharging of the supercapacitor; the PSO algorithm is used for optimizing the parameters of a membership function of the fuzzy logic controller. The method has the advantages that the depth discharging and peak current of a storage battery can be reduced, and the working efficiency of the hybrid energy storage system is improved.