28 results about "Ekf algorithm" patented technology

The invention relates to a heading machine pose measurement and control method and system based on visual direction finding and communication distance measurement. The system comprises a base station end measuring system in a roadway, a mobile end measuring system on a heading machine body and a motion control module. The method comprises the following steps: S1, establishing a base station end coordinate system; s2, establishing a mobile terminal coordinate system; s3, the base station side identifies and measures the vector information of the characteristic light source of the mobile terminal through a visual sensor; s4, the base station end and the mobile end measure the distance between the base station end and the mobile end through communication ranging equipment; s5, calculating the three-dimensional position of the mobile terminal in the coordinate system of the base station terminal based on the vector information and the distance information; s6, according to the three-dimensional position and the inertial measurement data of the mobile terminal, carrying out combined navigation calculation based on an EKF algorithm; and S7, performing pose control based on a combined navigation calculation result. According to the invention, a means of combining visual direction finding, communication distance measurement and inertial measurement is adopted, so that the position measurement precision is improved.

The invention relates to a heading machine pose measurement and control method and system based on bidirectional vision. A system used by the method comprises a base station end measuring system in a roadway, a mobile end measuring system on a heading machine body and a motion control module. The method comprises the following steps: S1, establishing a base station end coordinate system; s2, establishing a mobile terminal coordinate system; s3, the base station end identifies and measures the three-dimensional position information of the feature light source of the mobile end under the coordinate system of the base station end through a binocular vision sensor; s4, the mobile terminal identifies and measures the vector information of the laser beam of the base station end through a visual sensor; s5, carrying out combined navigation calculation based on an EKF algorithm; and S6, performing pose control based on a combined navigation calculation result. According to the invention, a means of combining bidirectional vision measurement and inertia measurement is adopted, so that the cost requirement on a high-precision inertia measurement unit is reduced.

The invention belongs to the technical field of intelligent health monitoring and wearable equipment, and particularly relates to an intelligent wearable terminal health data real-time synchronous monitoring system which comprises a multi-mode interface dynamic sensing subsystem, a self-adaptive light path compensation subsystem, a physical layer compensation coprocessor, a main application processor and a wireless transmission module. The method is characterized in that the dynamic state of an interface is sensed in real time through an MEMS pressure array, an IMU and a ToF displacement unit, the contact state is fused and estimated through an EKF algorithm, an MEMS micro-reflector is driven to implement closed-loop optical compensation, and an incident light path is stabilized; and meanwhile, the compensation metadata and the PPG signal are packaged into a fusion data frame, and the fusion data frame is synchronously transmitted to a remote platform in real time. Through high-frequency closed-loop control of the MEMS micro-reflector, the system can eliminate noise before the noise is injected into a signal link, so that the purity and fidelity of an original PPG signal are fundamentally ensured, and the heart rate monitoring precision under strong interference scenes such as strenuous exercise and the like is greatly improved.

The invention relates to a method and system for estimating the state of charge of a lithium iron phosphate battery, and the method comprises the steps: obtaining the cell parameters of the lithium iron phosphate battery: calculating the OCV slope based on the cell parameters and an OCV-SOC relation curve graph through employing an improved EKF algorithm; wherein the improved EKF algorithm is that an S-shaped weighting function is introduced into a classical EKF algorithm to weight the OCV slope; and calculating an SOC value based on the weighted OCV slope. According to the invention, the SOC accurate estimation of the lithium iron phosphate battery in the voltage platform area can be realized. Based on an improved extended Kalman filtering algorithm, the SOC estimation precision is improved, the reliability of SOC calculation of a platform area is enhanced, and accurate calculation of the SOC of the lithium iron phosphate battery in the voltage platform period can be realized without a high-complexity algorithm.

The invention discloses a permanent magnet synchronous motor sensorless current reconstruction method suitable for an electric aircraft, which comprises the following steps: switching modes of a motor control system of the electric aircraft through a fault detection module and a switch, the modes comprising normal operation and fault tolerance; whether a current sensor fails or not is detected in real time through a fault detection module, if the current sensor fails, a direct-current bus current signal is obtained through single-resistor sampling, another phase of current and rotor position and rotating speed information are reconstructed in real time through an EKF method, third phase of current is calculated in combination with the Kirchhoff's current law, and the third phase of current is calculated. And position-sensorless vector control is realized. According to the invention, by combining the single-resistor sampling technology and the EKF algorithm, the three-phase current, the rotor position and the rotating speed of the motor can be reconstructed at the same time only through a single sampling resistor, the feedback current of the motor can be normally obtained under the condition that the current sensor fails, and the safe flight of an airplane is ensured.

The application discloses a kind of EKF-based photovoltaic inverter IGBT thermal network online parameter identification method, comprising the following steps: S1, using third-order Cauer network, the heat transfer path of IGBT module establishes IGBT thermal network discrete state space model;S2, based on IGBT thermal network discrete state space model, design EKF algorithm for thermal parameter joint estimation for the synchronous estimation of state and parameter;S3, using current sensor and voltage sensor real-time acquisition IGBT current waveform, voltage waveform, and preprocessing;S4, in the embedded microcontroller of photovoltaic inverter, with fixed sampling period, EKF algorithm is operated, time update and measurement update are sequentially executed in each cycle, and the optimal estimation value of current time updated thermal parameter and junction temperature estimation value are output.The application can track the actual thermal characteristics of IGBT in real time, solve the parameter mismatch problem, and realize dynamic online identification of thermal parameters.

The application discloses a satellite solar cell array rotation angle estimation method, which comprises the following steps: step 1, establishing an earth inertial coordinate system, a satellite body coordinate system and a solar cell array coordinate system, and defining a satellite solar cell array rotation angle; step 2, establishing a state equation of an augmented state composed of a sun vector, the solar cell array rotation angle and a gyro constant drift noise; step 3, calculating an augmented state quantity estimation value by using an extended Kalman filter; and step 4, calculating a satellite solar cell array rotation angle estimation value. The method fuses gyro angular velocity and sun sensor vector data, constructs a nonlinear dynamic model containing a sun vector, a rotation angle and a gyro bias, and designs an EKF algorithm to estimate the solar cell array rotation angle in real time, thereby solving the strong dependence of a traditional scheme on a star sensor and a mechanical sensor, and significantly improving the autonomous recovery capability and energy guarantee reliability of a satellite in an extreme fault scene.

The invention discloses a built-in permanent magnet synchronous motor MTPA control method based on an improved EKF algorithm, and belongs to the field of permanent magnet synchronous motor high-performance control. According to the method, a dynamic forgetting factor multi-information extended Kalman filter algorithm is provided for solving the problems of MTPA trajectory offset and operation efficiency reduction caused by IPMSM key parameter time variation, and the algorithm adaptively adjusts historical data weight through a dynamic forgetting factor mechanism to realize rapid tracking when parameters suddenly change; and a multi-information theory is utilized to fuse historical information, so that noise is effectively suppressed, and the steady-state estimation precision is improved. The algorithm serves as an online identification module to be embedded into an MTPA control system, accurate quadrature-axis inductance and permanent magnet flux linkage can be obtained in real time, the current distribution angle is dynamically corrected, and therefore it is ensured that the motor stably operates along the optimal efficiency track under the full working condition. The method does not need extra signal injection, and has high precision, strong robustness and good engineering practicability.

The application discloses a kind of real-time positioning method, device and readable medium for permanent magnet under fast motion, it is related to permanent magnet positioning field using random walk model as the motion state model of permanent magnet, and the pose of permanent magnet and the covariance matrix related to state vector are predicted in combination with EKF algorithm, and the predicted pose and covariance matrix are updated by Kalman gain, the final pose of permanent magnet is obtained after iteration operation, can effectively improve the precision and accuracy of positioning, the relationship between the moving speed of permanent magnet and positioning precision is established, so as to know that the tracking performance of the application in fast moving target has been greatly improved, so as to greatly expand the application scene of permanent magnet positioning, solve the problem that permanent magnet is difficult to be positioned under fast motion, and positioning precision is low.

This invention provides a substation equipment installation guidance and control method and system based on semantic perception and EKF algorithm, belonging to the field of substation equipment installation guidance and control technology. To address the technical problems of current substation equipment installation guidance methods relying on manual experience, resulting in poor installation accuracy, dependence on stringent auxiliary markers, and inability to adapt to unstructured installation environments, this invention preprocesses the raw point cloud data of the acquired equipment, including voxel downsampling and statistical outlier removal, filtering out dust and noise from the construction site. The unordered point cloud is classified into base, equipment, ground, or background, and then subjected to perception processing and semantic segmentation. In the segmented point cloud data, the flange plane is extracted using a random sampling consistency algorithm, and the bolt hole center coordinates are identified using a clustering algorithm. Coarse and fine registration strategies are employed for virtual-real registration operations. This invention is applied to substation equipment installation guidance.

This invention discloses a path planning method for an unmanned surface vehicle (UAV) with autonomous obstacle avoidance. The method first constructs a state vector describing the UAV's navigation state, then uses the EKF algorithm to combine the state vector with the predicted vector to obtain a predicted vector. Simultaneously, it detects all obstacles near the UAV and the motion state of each obstacle, sets multiple feature parameters, and determines the value of each feature parameter based on the motion state of each obstacle. A command model is established, and control commands are derived by combining the predicted vector and feature parameter values. Constraints are established, and the final command to control the UAV's movement is obtained by combining these constraints with the control commands. An adaptive learning model is established to judge the constraint states; if the state is unfavorable, it is updated. This method ensures the UAV will not collide with obstacles through constraints, guaranteeing safety. The judgment is rapid and highly reliable, and the adaptive learning model helps ensure the UAV's operational efficiency.

This invention relates to the field of smart grid technology, and more specifically to a method and system for optimizing the tracking and communication of inspection vehicles. The method includes: constructing a RIS-ISAC-NOMA system framework for smart grid application scenarios; in non-line-of-sight (NLOS) scenarios, utilizing a reconfigurable smart metasurface (RIS) to transform NLOS paths into equivalent virtual line-of-sight (LOS) paths, and adjusting the propagation path of ISAC signals in real time; designing a vehicle tracking and prediction method based on the EKF algorithm, thereby achieving efficient real-time vehicle perception and communication; and combining NOMA technology to propose a power allocation optimization scheme for multiple inspection vehicles, further improving the system's communication rate and resource utilization efficiency. Simulation results also verify the advantages of the proposed system in terms of communication performance and resource utilization. This invention can significantly improve the overall system performance while reducing pilot signal overhead.

The invention relates to the technical field of aerial robot navigation perception, and provides a flying object detection algorithm based on a laser radar. According to the target detection algorithm provided by the invention, point cloud data is obtained mainly through a laser radar, then a high-confidence free space voxel map is constructed, and the robustness problem of dynamic object detection in a complex environment is solved by combining hierarchical clustering and a motion tracking algorithm. Meanwhile, a voxel marking strategy is combined, pose transformation is provided through a front-end SLAM algorithm, point cloud data are processed in real time, a u-depth graph and a v-depth graph generated by radar point cloud are used for detecting a dynamic object, and an EKF algorithm is applied to track the object. After actual operation, the method has a good recognition effect on the dynamic obstacles in the air, and can recognize and track a plurality of dynamic obstacles in the air at the same time. Meanwhile, in the detection process, the algorithm delay is low, and the requirements for the real-time performance and accuracy of dynamic detection can be well met.

The application relates to a lithium iron phosphate battery state of charge estimation method and system, which comprises the following steps: obtaining a battery cell parameter of a lithium iron phosphate battery; based on the battery cell parameter and an OCV-SOC relationship curve, an improved EKF algorithm is used to calculate an OCV slope; wherein the improved EKF algorithm is that a sigmoid weight function is introduced into a classical EKF algorithm to weight the OCV slope; and based on the weighted OCV slope, an SOC value is calculated. The application can realize accurate SOC estimation of the lithium iron phosphate battery in a voltage platform region. Based on an improved extended Kalman filter algorithm, the SOC estimation accuracy is improved, the reliability of platform region SOC calculation is enhanced, and without a high-complexity algorithm, accurate calculation of the lithium iron phosphate battery SOC in the voltage platform period can be realized.

The invention discloses a casting cutting and polishing robot trajectory optimization method based on vision and genetic algorithms, which comprises the following steps: comparing casting point cloud data with a standard mathematical model, extracting the number and coordinates of casting riser feature points, and generating an initial cutting trajectory; determining a tool tail end movement track of the cutting robot through a visual mileage calculation method; iMU sensor data of the cutting robot are collected, the track data and the IMU sensor data are fused through an EKF algorithm, and the fused tool tail end position and posture are output; by taking the total arrival time, precision and total energy consumption of casting riser cutting as optimization objectives, constructing a multi-objective optimization function; constructing a constraint condition, solving the multi-objective optimization function through a genetic algorithm to obtain an optimal cutting track of the cutting robot meeting the constraint condition, and cutting the casting riser; according to the method, collaborative optimization of cutting time shortening, energy consumption reduction and precision improvement can be achieved on the premise of meeting robot kinematics constraint and casting process requirements.

The invention relates to a heading machine pose measurement and control method and system. A system used by the method comprises a base station end measuring system in a roadway, a mobile end measuring system on a heading machine body and a motion control module. The method comprises the following steps: S1, establishing a base station end coordinate system; s2, establishing a mobile terminal coordinate system; s3, the base station side identifies and measures the vector information of the characteristic light source of the mobile terminal through a visual sensor; s4, the base station end and the mobile end measure the distance between the base station end and the mobile end through communication ranging equipment; s5, resolving the three-dimensional position of the mobile terminal in the coordinate system of the base station terminal; s6, the mobile terminal identifies and measures the vector information of the laser beam of the base station end through a visual sensor; s7, carrying out combined navigation calculation based on an EKF algorithm; and S8, performing pose control based on the combined navigation calculation result. According to the invention, a means of combining bidirectional vision measurement, communication distance measurement and inertial measurement is adopted, so that the measurement precision is improved, and a high-cost inertial measurement unit is not needed.

The invention discloses a fan tower shaking attitude detection method. The method comprises the following steps: acquiring an original three-axis acceleration value and a three-axis angular velocity value of a tower, which are acquired by an inertial measurement unit (IMU) in real time; performing sensor error compensation on the original three-axis acceleration value and the three-axis angular velocity value to obtain a calibrated three-axis acceleration value and a calibrated three-axis angular velocity value under the carrier coordinate system; based on the calibrated three-axis angular velocity value and three-axis acceleration value, data fusion is carried out through an extended Kalman filter (EKF) algorithm, the optimal estimated value of the rotation quaternion representing the attitude of the tower drum is obtained through solving, and an observation noise covariance matrix of the EKF algorithm is dynamically adjusted according to the credibility of the measured value of an accelerometer; according to the optimal estimation value of the rotation quaternion, the shaking attitude angle of the tower drum is obtained through calculation. According to the method, integral drift of the IMU is effectively inhibited, high-precision and high-reliability attitude calculation of quasi-static and dynamic shaking of the tower drum can be realized, and the method is low in cost and easy to deploy.

The invention provides a multifunctional intelligent agricultural robot based on AI motion control and visual identification, and belongs to the technical field of agricultural intelligent equipment.The multifunctional intelligent agricultural robot comprises a hardware system and a software system, and the hardware system adopts a core control layer, a sensing layer, an execution layer and an auxiliary layer; the software system adopts modular design of an ROS system program module, a visual system, a navigation system, water and fertilizer management and the like, and the ROS system program module and the visual system cooperatively complete integrated operation of watering, fertilization, crop growth detection and fruit picking through a data bus. The laser and vision fusion navigation technology and the improved EKF algorithm are adopted, the improved YOLOv8 algorithm is carried, the fruit maturity recognition accuracy is high, the double six-degree-of-freedom mechanical arms conduct cooperative picking, and the damage rate is low; the accurate water and fertilizer management system realizes high water resource utilization rate. The robot has the advantages of integrated functions, high operation precision and strong environmental adaptability, greatly reduces the labor cost and resource consumption, improves the economic benefits of agricultural production, and is suitable for various agricultural operation scenes such as orchards and farmlands.

This invention provides a method and system for measuring the lateral displacement of a bridge pier after a ship collision based on multi-source data fusion. The method includes: acquiring sensor observation data and calculating the bridge sensor displacement based on the sensor observation data; acquiring a set of bridge image frames, extracting the keypoint set corresponding to each bridge image frame set using the SuperPoint model, and then completing the keypoint set; calculating the bridge pixel displacement sequence based on the keypoint set, and then converting the bridge pixel displacement sequence to physical space to obtain the bridge visual displacement sequence; fusing the bridge visual displacement sequence and the bridge sensor displacement sequence using the EKF algorithm to obtain the observed bridge displacement; and fusing the bridge observed displacements corresponding to all cameras to obtain the actual bridge displacement. This invention solves the problem in existing technologies where measurement using visual methods is easily affected by environmental factors such as rain, fog, and dust, as well as object occlusion, leading to a decrease in measurement accuracy.

The present application relates to the technical field of navigation, in particular to an AUV adaptive navigation method based on combination of deep learning and physical model. The method comprises the following steps: in the process of modeling AUV motion system, a weighted gain bias estimation EKF algorithm is proposed, the DVL velocity bias is taken as a state variable to be estimated online, the sensor system error is compensated, and a Kalman gain update strategy weighted according to state confidence is introduced; a deep learning network of adaptive measurement noise is constructed, the network is used to dynamically predict the DVL velocity x to time-varying noise standard deviation, and y to time-varying noise standard deviation; the time-varying noise standard deviation obtained in the above step is written into the measurement noise covariance matrix at the moment, and the obtained AUV position trajectory is calculated. The AUV navigation adaptability and navigation precision can be significantly improved.

The application discloses a kind of electric power energy storage control method and system, it is related to energy storage equipment management and control technical field, method includes collecting energy storage system state data and energy storage system work log data;Based on ampere-hour integration method, the energy storage system state data is analyzed in real time, and energy storage battery real-time state data is generated;Every interval setting first length is based on EKF algorithm, the energy storage system state data is analyzed, and energy storage battery state calibration data is generated;The electric power energy storage control method and system, by analyzing the error amount that needs to be calibrated and corresponding battery parameters, state etc. Feature data corresponding prior probability of calibration are constructed error prediction model, then based on the energy storage battery feature input error prediction model of next calibration time that meets historical mode is analyzed, so as to reach the error amount of next calibration time is predicted, realize the effect that real-time estimated battery state parameter is pre-calibrated by predicted error amount.

The present application relates to the technical field of lithium battery state estimation, and more particularly to a lithium iron phosphate battery SOC estimation and correction method based on a fuzzy EKF-AH algorithm. Starting with a certain start of an energy storage device, the BMS reads the SOC and SOH at the last shutdown, and according to the standing time, selects the open circuit voltage OCV correction or EKF algorithm correction SOC; the SOC is estimated using the EKF-AH algorithm, the fuzzy rule base associated with the state of charge SOC and the health status SOH is established combined with fuzzy control, and the EKF-AH algorithm weight and measurement noise error are dynamically adjusted; the estimated difference of the EKF algorithm and the ampere-hour integral method is calculated, and if the sum of the estimated difference is greater than the corresponding threshold, an SOH correction warning is issued. The estimation accuracy of the whole life cycle of the lithium iron phosphate battery is improved, and the SOH is also corrected.

The invention relates to the field of mechanical equipment, and discloses a heavy-load robot adaptive path planning method and system based on a space-time attention mechanism, and the method comprises a data collection unit which is used for collecting the motion, load and environment original data of a robot, according to the perception of an encoder, an inertial measurement unit, a load sensor, a laser radar, a visual camera and a motor controller, original data such as a rotating speed, an attitude, a load, an environment and a driving force are obtained; and the introspection calibration ring is used for receiving the state data and outputting calibrated kinetic model parameters, and obtaining deviation data of the prediction state signal and the observation state signal transmitted by the sensor preprocessing unit according to the prediction state signal transmitted by the planning feedback ring and the observation state signal transmitted by the sensor preprocessing unit. The method comprises the following steps: operating at a high frequency of 50-200Hz through an introspection calibration ring, updating kinetic model parameters in real time through an EKF algorithm on the basis of a residual error of a real observation state and a prediction state acquired by a sensor, and ensuring that the model always fits real physical characteristics of a robot, even if a load is changed.

A vehicle quality estimation method integrating the Wasserstein geometric consensus algorithm and deep networks is proposed, involving the fields of artificial intelligence, data processing and algorithms, and vehicle engineering. The method includes the following steps: S1. CAN bus data acquisition; S2. Preprocessing the acquired data; S3. Inputting the preprocessed signal into a pre-trained SARE-Net and outputting a preliminary estimate; S4. Synchronously running the EKF algorithm to output the posterior probability distribution of the vehicle quality. P k EKF S5. Based on the preprocessed signal, a collaborative calculation method for confidence factors based on multi-criteria dynamic game optimization is adopted to generate the optimal confidence factor in real time. λ t * S6. Optimal confidence factor λ t * The invention incorporates the Wasserstein probabilistic consensus fusion framework, generating an optimal consensus probability distribution by solving a weighted centroid problem, and using its mean as the final quality estimate. This invention offers high quality estimation accuracy, outstanding anti-interference capabilities and stability, while also exhibiting strong adaptability to various operating conditions, low model dependency, good economic efficiency, and high real-time performance.

The invention relates to the technical field of navigation, in particular to an AUV (Autonomous Underwater Vehicle) adaptive navigation method based on combination of deep learning and a physical model. Comprising the following steps that in the process of modeling an AUV motion system, a weighted gain deviation estimation EKF algorithm is provided, DVL speed bias serves as a state quantity to conduct online estimation and compensation on sensor system errors, and a Kalman gain updating strategy weighted according to state confidence is introduced; constructing a deep learning network for adaptively measuring noise, and dynamically predicting an x-direction time-varying noise standard deviation and a y-direction time-varying noise standard deviation of a DVL speed quantity by using the network; and writing the time-varying noise standard deviation obtained in the above step into a measurement noise covariance matrix at the moment, and calculating the obtained AUV position trajectory. And the navigation adaptability and the navigation precision of the AUV can be obviously improved.

The invention discloses a battery state-of-charge and expansive force combined online estimation method. The method comprises the following steps: establishing a three-dimensional coupling model of state-of-charge-mechanical pressure-impedance model parameters; according to the impedance model parameters and the time-frequency domain transformation relation, a battery end voltage time domain simulation model under the real vehicle operation time scale is established, and the SOC-end voltage relation is determined; according to the battery expansion stress data and the corresponding SOC-terminal voltage relation, nonlinear mapping of differential expansion peak-SOC is established, and an expansion force prediction model is obtained; a state equation including SOC, polarization voltage and expansion stress is constructed, an observation equation is established, multi-time-scale prediction and correction are performed by using an EKF algorithm, and SOC and expansion force joint online estimation is realized. According to the method, online SOC and expansive force joint estimation is achieved, the steps are simple, online implementation is easy, and the method is suitable for online application of an electric vehicle power battery management system.

The application belongs to but is not limited to the technical field of unmanned aerial vehicle communication, and discloses a cooperative detection method and system in an unmanned aerial vehicle communication and sensing integrated network, an unmanned aerial vehicle communication and sensing integrated system is constructed, a base station and a detection unmanned aerial vehicle jointly perform a detection task; in a cooperative detection scene, an unmanned aerial vehicle communication and sensing signal transmission and reception signal model is established; according to a maximum likelihood criterion, detection sets of the base station and the unmanned aerial vehicle are subjected to sensing data fusion by using an EKF algorithm; a maximum problem of an achievable communication rate is subjected to mathematical modeling; communication performance is enhanced by jointly optimizing an unmanned aerial vehicle trajectory and a beamforming algorithm to solve an optimization problem of the maximum of the achievable communication rate. The application can solve problems of a black flight unmanned aerial vehicle, such as high mobility, miniaturization and being easily blocked by buildings, and effectively improves sensing accuracy, enhances cooperative sensing distance and enhances detection performance by using communication to fuse sensing information.

The application discloses a kind of comprehensive positioning method and system for transmission tower high-precision tilt monitoring, it is related to transmission tower tilt monitoring technical field, including the fusion model of IMU and UWB is constructed, initialization system sampling rate;IMU and UWB data are fused using EKF algorithm, switch to IMU dominant mode when UWB signal quality drops, update positioning state;Real-time monitoring transmission tower tilt state, short-time motion model is constructed based on acceleration and differential information, and generates tilt monitoring report.The method disclosed in the application solves the problems of discontinuous positioning, insufficient accuracy and high algorithm complexity in traditional transmission tower tilt monitoring system by innovative IMU and UWB data fusion model, dynamic switching algorithm and lightweight real-time monitoring mechanism;The overall effect is to realize high-precision, continuous and efficient transmission tower tilt monitoring, and provide a good adaptive technical solution for high-precision positioning and state monitoring.