63results about How to "Enable real-time estimation" patented technology

The invention relates to a power battery management system of an electric automobile. In order to improve the estimating precision, the invention provides a method for estimating a charge state of a power battery of the electric automobile. The method comprises the following steps: acquiring the terminal voltage, the charging and discharging current and the battery surface temperature of the power battery; according to a corresponding relationship between the open-circuit voltage and the state of charge of the power battery, analyzing to obtain an open-circuit voltage model, and according to an input matrix, a parameter matrix and an output matrix of a dynamic voltage system of the power battery, establishing a dynamic voltage model of the dynamic voltage system; updating time and measurement of a state observer to obtain priori estimated values and posteriori estimated values of a system state as well as a covariance matrix of a system state estimating error at a time point k, and circulating the updating operation till the end of estimation to obtain the state of charge (SoC) of the power battery. By the method, the estimating precision of the SoC can be improved to be within 5%, and when a set value of the SoC of the power battery is deviated from a reference value of the SoC of the power battery, the SoC is rapidly converged to the reference value.

The invention provides a wind turbine set gearbox remote online state monitoring and life assessment method. The method comprises the following steps: (1) a remote center computer collects a real-time operation parameter, temperature data and vibration data of each wind turbine through SCADA; (2) through analyzing the characteristic values of signal time domain and frequency domain, whether a vibration sensor has a fault or not is diagnosed; (3) the data is subjected to normalization process, a difference between data is reduced, and the diagnosis precision is raised; (4) when the fault of a wind generator set gearbox is diagnosed, different filtering modes are employed for different algorithms to extract the corresponding characteristic; (5) the collected data is subjected to characteristic extraction; (6) the fault diagnosis is carried out to provide the fact that whether the fault exists or not and the degree of the fault; (7) the residual life of the failed gearbox is estimated through a gray theory model, and an autoregression model is established for the residual error of the predication result to raise the predication precision. According to the method, the real-time estimation of the residual life is realized, and a basis is provided for the planned maintenance of the wind turbine set.

The invention relates to the technical field of automatic control, in particular to a four-rotor aircraft attitude control method and system based on sliding-mode control law and ESO. The method includes: using a sliding-mode control method to the three attitude angle loops of four rotors; using the ESO to estimate total system disturbance in real time; combining the sliding-mode control with the ESO to achieve attitude control of a four-rotor aircraft. Compared with an existing attitude control method, the method has the advantages that attitude stability of the four-rotor aircraft can be achieved, good tracking performance on attitude angle instructions is achieved, and high disturbance resistance is achieved as compared with common sliding-mode control.

The invention belongs to the technical field of automobile brakes, in particular to a method for estimating the brake pressure of an automobile and the peak value of the road adhesion coefficient, more particularly, the invention intends to provide a method for estimating the brake pressure of an automobile and the peak value of the road adhesion coefficient on the basis of the conditions of the wheel speed and pressure control under the condition that no component for measuring the brake pressure is introduced. The method comprises the following steps: grading the peak value of the road adhesion coefficient and the brake pressure; and estimating the peak value of the road adhesion coefficient and the initial brake pressure of a wheel cylinder. By estimating the peak value of the road adhesion coefficient and the initial brake pressure of the wheel cylinder, the invention is capable of estimating the brake pressure of the automobile and the peak value of the road adhesion coefficient under the condition that no component for measuring the brake pressure is introduced, thus improving the breaking efficiency of an ABS (anti-lock braking system) and providing strong support for the fail-safe function. According to the tests, the maximum relative error between the estimated value and the measured value of the pressure of the wheel cylinder of the brake is lower than 10%, thus meeting the requirements for ABS control, reducing the cost and facilitating the popularization of the ABS in the field of braking and being beneficial to the improvement on the traffic safety.

The invention relates to an underwater acoustic locating method based on equivalent sound velocity. The underwater acoustic locating method is characterized by including the following steps of step1, initialization, specifically setting an initial position of an underwater target source, and placing n sensors and receivers underwater at random, step 2, measuring time difference of arrival, step 3, computing the equivalent sound velocity, and step 4, locating the target source through a Kalman filter so as to reduce influences of background noise and improve underwater locating accuracy. Compared with the prior art, the underwater acoustic locating method based on the equivalent sound velocity has the advantages of being capable of achieving synchronous three-dimensional locating and real-time velocity computation, small in locating error, high in accuracy and the like.

The invention discloses a vehicle quality estimation method considering gear shifting and road slope factors. The vehicle quality estimation method comprises the following steps that 1, data are collected, specifically, (11), the vehicle driving state data are collected through a data collecting device, and (12), parameters required by a model are calculated and obtained by combining vehicle intrinsic parameters with the vehicle driving state data; 2, based on the obtained vehicle driving state data, a vehicle longitudinal dynamic model is established; 3, a vehicle quality estimation model considering the gear shifting factor is established; 4, a transmission system mechanical efficiency estimation model is established, and the transmission system mechanical efficiency estimation model and the vehicle quality estimation model which considers the gear shifting factor and is provided in the third step jointly form a vehicle quality estimation model which considers the gear shifting and road slope factors simultaneously; 5, the use condition of a vehicle quality real-time estimation system is determined; and 6, the collected vehicle driving state data and relevant model parameters are input into the vehicle quality real-time estimation system to estimate the real-time vehicle quality.

The invention discloses a multi-information-fused real-time wind speed estimation method for a multi-rotor aircraft. The method comprises that data of an airborne sensor and rotating speed data of rotors are collected; a dynamic model of the multi-rotor aircraft is constructed, and a relation between resistance and wind speed of the multi-rotor aircraft is obtained; a state equation and a measurement equation of a multi-information-fused navigation system based on characteristics of the dynamic model of the multi-rotor aircraft, inertia sensor information and GPS information are established; and navigation information is updated via Kalman filtering, and the wind speed is estimated in real time. According to the invention, disadvantages of present wind speed estimation methods are overcome, the characteristic that the dynamic resistance of the dynamic model is sensitive to the wind speed is utilized fully, a Kalman filter is constructed on the basis of an inertia device and GPS, and the wind speed is estimated accurately in real time.

The invention discloses a target tracking new algorithm based on a fractional fuzzy function under stably distributed noise, belonging to the technical field of computer application. At first, a new signal model is proposed, and in the fractional order Fourier transform domain, the instantaneous estimation of doppler frequency is realized by the peak point searching of FLOS_FAF. And then a projection approximation subspace angle tracking algorithm of the fractional order fuzzy function based on fraction low order statistics is proposed, and the real-time estimation formula of azimuth and pitch angle in the impulsive noise environment can be realized.

The invention provides a fuel cell water content estimation method. The method comprises the following steps: constructing a cathode two-cavity model, and defining a state variable, an input and an output; constructing an unscented Kalman filter based on the cathode two-cavity model; according to the acquired average current density, average voltage, cathode air supply flow, cathode inlet and outlet voltage difference and cathode back pressure of the fuel cell, calculating an estimated value of a state variable of the cathode two-cavity model by using the unscented Kalman filter; and determining the water content of the fuel cell according to the estimated value of the state variable. In the embodiment of the invention, when the cathode two-cavity model is established, the difference between the gas concentration and the current density along the flow channel direction is considered, which is closer to the real working state, and the estimation precision is favorably improved. And themethod only needs to measure parameters such as voltage, cathode inlet and outlet voltage difference, average current density, cathode air supply flow, cathode back pressure and the like, so that themeasurement workload of the system is reduced.

The invention provides a combined autonomous navigation method of a mars exploration approach section. The combined autonomous navigation method comprises the following steps: the step 1 of using a spacecraft-mounted sun sensor to measure the viewing direction of the sun and using a spacecraft-mounted solar-spectrum velocity-measurement navigation sensor to measure the relative movement velocity of a mars explorer to the sun; the step 2 of using a wireless responder to obtain distance and relative velocity information with a mars surrounding device; the step 3 of establishing an orbital dynamics equation and establishing a navigation system model under a J2000.0 heliocentric ecliptic inertial coordinate system and using a corresponding navigation filtering algorithm to obtain autonomous navigation position and velocity information. The combined autonomous navigation method disclosed by the invention avoids depending on ground wireless information, can achieve autonomous navigation of a spacecraft in a flying process by only needing optical information from the sun and the wireless information from the mars surrounding device, can establish a system equation of an autonomous navigation system of the explorer, utilizes different navigation filter algorithms to achieve real-time estimation of navigation information of the explorer and is simple, easy, practical and free of time delay.

The invention provides a polyethylene (PE) multi-trademark product quality index and operational constraint on-line estimation system for gas phase fluidized bed production technology. The system comprises a field process instrument which is connected with a gas phase PE object, a field analysis instrument, a distributed control system (DCS), a laboratory analysis data server and an advanced control server. The advanced control server includes an OPC client, a data interface module, a multi-trademark PE production process quality indicator and operational constraint model prediction module, a PE quality index prediction model parameter adaptive correction module, and a PE quality index robustness suboptimal filter estimation and quality indicator and operational constraint display module. The invention also provides a gas phase PE quality index and operational constraint on-line estimation method. The invention provides the real-time online estimation system and method of multi-trademark gas phase PE product quality index melt index and density, gas phase PE production operating constraint yield, and recycle gas dew point temperature.

The invention discloses an optical communication system performance analysis method based on a convolutional neural network, and the method comprises the steps: selecting an appropriate convolutionalneural network according to a to-be-tested optical communication system; The method comprises the following steps: constructing a debugging optical communication system, continuously adjusting a sending end modulation mode, a modulation code rate, an optical signal-to-noise ratio, channel dispersion and polarization mode dispersion of the debugging optical communication system, obtaining two-dimensional amplitude information under different channel conditions by utilizing an asynchronous delay sampling method, and training a convolutional neural network by utilizing the two-dimensional amplitude information; placing the convolutional neural network obtained through training into a to-be-tested optical communication system; and obtaining two-dimensional amplitude information describing theperformance of the to-be-tested optical communication system by using an asynchronous delay sampling method, and analyzing the two-dimensional amplitude information corresponding to the to-be-tested optical communication system by using the trained convolutional neural network so as to estimate channel performance parameters of the to-be-tested optical communication system. According to the method, a plurality of channel performance parameters of the optical communication system can be simultaneously estimated by utilizing the neural network.

The invention discloses a real-time estimation method for the turning proportion of a level crossing of urban roads. The method comprises that sectional traffic flow data of an entrance lane of the crossing and traffic flow data of a downstream road corresponding to an exit lane of the crossing are obtained, the flow deviation mode between the exit lane and the downstream road thereof is analyzed, and a Kalman filtering model for turning proportion estimation is established. According to flow conservation of the entrance and exit lanes of the crossing and random roaming of flow deviation, an observation equation of the Kalman filtering model for turning proportion estimation is established; according to the continuity and smoothness of short-period fluctuation of the turning proportion, a state equation of the Kalman filtering model is established; and the turning proportion of the crossing is estimated in real time by utilizing the Kalman filtering algorithm. The substantial random fluctuation mode of flow deviation between the exit lane of the crossing and the downstream road of the exit lane is fully dug, and accurate real-time turning proportion information of the traffic flow of the crossing can be provided for design and optimization of crossing signals of the urban roads.

The invention belongs to the field of visual navigation, and particularly relates to a visual pose real-time estimation method based on a digital image map and feature point tracking, which is characterized in that an optical flow method is adopted to track inter-frame matching feature points, so that subsequent frame matching point pairs are obtained in one step, the tedious process of frame-by-frame detection and matching is omitted, and the real-time estimation of the frame-by-frame visual pose is realized. A visual pose real-time estimation method based on a digital image map and feature point tracking is provided, so that real-time frame-by-frame high-precision visual pose estimation in an outdoor large scene is realized, the average frame rate per second of pose estimation experiments in different scenes is more than 55 frames, and a guarantee is provided for real-time visual navigation estimation.

The invention provides a high-dynamic GNSS carrier wave signal tracking method. A time frequency image can simultaneously combine a time domain and a frequency domain to analyze a signal. On a principle, a contradiction between a frequency resolution and a time resolution is balanced and signal nondeterminacy caused by high dynamic is limited in several pixels. Therefore, different from a traditional tracking method, a dynamic tracking ability in the invention is not limited by a loop band width so that a receiver can track a super high dynamic carrier wave signal, and a usage scope of a GNSS receiver based on Big Dipper/GPS is expanded.

The invention discloses a river surface flow field accurate measurement radar based on computational intelligence, and particularly relates to the technical field of flow measurement. The radar comprises a software structure and a hardware structure. The radar system adopts a coding technology on a signal waveform to realize interference resistance, adopts a fuzzy clustering intelligent algorithm to ensure stability and reliability of observation data, adopts broken line array weighted optimization to realize high-precision river surface vector field synthesis under a large visual angle condition, adopts a variance weighted intelligent algorithm to reduce data jitter and improve stability of observation vector data, and adopts remote data analysis based on big data and an artificial intelligence technology. Intelligent algorithms such as an anti-interference means, an intelligent processing technology, a high-resolution broken line array angle resolution technology, a fuzzy clustering technology and a filtering jitter reduction technology are integrated, a corresponding hardware structure is designed for related intelligent technologies, and the problems existing in traditional equipment are effectively solved.

The invention provides an LS-SVM-based sensorless control system of a bearingless induction motor. According to the non-linear estimation model of radial displacement variable, formulas (4) and (5) are given. The generalization ability of SVM is used to approximate the nonlinear relationship between radial displacement and related physical variables, so that the real-time estimation of the radialdisplacement of the rotor can be realized, the estimation accuracy of the radial displacement does not depend on the accurate mathematical model of the motor, the radial displacement estimation errorcaused by the inaccurate motor parameters can be effectively overcome, and the complicated signal extraction algorithm is not needed, and the LS-SVM rotor radial displacement estimator is constructed,thereby getting rid of that mechanical radial displacement sensor, getting rid of the mechanical radial displacement sensor, overcoming the influence of modeling error of nonlinear radial displacement estimation model, and avoiding the radial displacement estimation error caused by inaccurate motor parameters. A new control system is constructed based on LS-SVM rotor radial displacement estimator, which can effectively reduce the cost of bearingless induction motor control system.

The invention discloses an improved UKF algorithm for unmanned ship alignment under severe sea conditions. The improved UKF algorithm re-empowers an innovation vector in a sliding window by using a projection statistical algorithm, realizes real-time estimation of a measurement noise variance matrix, aims at the situation that each observed quantity is inevitably not only affected by single external interference during unmanned ship alignment under severe sea conditions, solves a self-adaptive factor matrix by utilizing a genetic algorithm, and adjusts the measurement noise variance matrix optimally. Compared with the prior art, the improved UKF algorithm has the advantages that compared with a conventional self-adaptive robust UKF algorithm, the improved UKF algorithm achieves optimization of the self-adaptive adjusting capacity, and can improve the precision and stability of unmanned ship alignment under severe sea conditions.

The invention discloses an unmanned aerial vehicle position estimation system and method based on a visual sensor. The system comprises an MCU module carried on an unmanned aerial vehicle and a flight control module. The MCU module is respectively connected with the camera, the inertial measurement unit, the ultrasonic ranging sensor, the GPS sensor, the LCD display screen and the electrified erasable programmable read-only memory; data transmission is realized between the MCU module and the flight control module through a universal asynchronous transceiving transmitter; data transmission is realized between the MCU module and the upper computer through Bluetooth; the mounting angle of the inertial measurement unit is parallel to the body of the unmanned aerial vehicle, and the mounting angles of the camera and the laser distance measuring sensor are perpendicular to the body of the unmanned aerial vehicle. According to the invention, under the condition that the manufacturing cost is not increased, the measurement precision and the calculation rate of pose estimation are both considered.

The invention provides a target passive positioning method based on a deep sea distributed vertical line array, and the method comprises the steps: 1), collecting sound pressure signals of target sound source radiation at a t moment for three or more synchronous vertical line arrays at the same time; 2) carrying out azimuth spectrum estimation on each sound pressure signal to obtain an estimated value of an angle of arrival of each detection node; 3) calculating a corresponding arrival angle template value by using the sound field model; 4) matching the estimated value of the angle of arrival with a corresponding template value to obtain a distance estimated value between the target sound source and each detection node; step 5) based on the search range and the search grid interval, obtaining distance information between each grid point and each detection node; and 6) matching the distance information with the distance estimation value to obtain the position information of the target sound source. According to the method provided by the invention, the real-time estimation of the position information of the target sound source is realized by adding system configuration and utilizing the distance information of the three or more detection nodes.

The invention discloses an ionosphere TEC real-time measurement method based on a GNSS receiver, and the method comprises the following steps: 1, extracting the real-time measurement data of the GNSS receiver, 2, calculating the ionosphere TEC based on pseudo-range observation, 3, calculating the ionosphere TEC based on a carrier phase, 4, calculating the ionosphere TEC based on carrier phase leveling, 5, constructing an ionosphere TEC observation equation, and 6, carrying out Kalman filtering real-time estimation; and 7, extracting and outputting real-time TEC information of the ionized layer. The method disclosed by the invention is high in robustness and high in calculation speed, can meet the requirements of GNSS receivers in different areas for relatively high-precision real-time ionosphere measurement, so that high-precision ionosphere TEC parameters are obtained, and the method has an important practical value.