71 results about "Ekf algorithm" patented technology

The present invention relates to the technical field of lithium battery state estimation, and in particular to a fuzzy EKF-AH algorithm-based state of charge (SOC) estimation and correction method for a lithium iron phosphate battery. The method comprises: by taking a certain startup of an energy storage device as a start, a BMS reading an SOC and a state of health (SOH) at the previous shutdown, and on the basis of a standby time, selecting an open circuit voltage (OCV) or an EKF algorithm to correct the SOC; using an EKF-AH algorithm to estimate the SOC, establishing, on the basis of fuzzy control, a fuzzy rule library associated with the SOC and the SOH, and dynamically adjusting a weight and a measurement noise deviation of the EKF-AH algorithm; and calculating estimation differences between the EKF algorithm and an ampere-hour integration method, and if the sum of the estimation differences is greater than a corresponding threshold, issuing an SOH correction warning. The present invention improves the estimation accuracy of the entire life cycle of the lithium iron phosphate battery, and assists the correction of the SOH.

The invention discloses a comprehensive positioning method and system for high-precision inclination monitoring of a power transmission tower, and relates to the technical field of inclination monitoring of the power transmission tower, and the method comprises the steps: constructing an IMU and UWB fusion model, and initializing a system sampling rate; fusing IMU and UWB data by using an EKF algorithm, switching to an IMU dominant mode when the UWB signal quality is reduced, and updating a positioning state; and monitoring the inclination state of the transmission tower in real time, constructing a short-time motion model based on acceleration and differential information, and generating an inclination monitoring report. According to the method, through an innovative IMU and UWB data fusion model, a dynamic switching algorithm and a lightweight real-time monitoring mechanism, the problems of discontinuous positioning, insufficient precision and high algorithm complexity in a traditional transmission tower inclination monitoring system are solved; the overall effect is that high-precision, continuous and high-efficiency inclination monitoring of the transmission tower is realized, and a technical scheme with good adaptability is provided for high-precision positioning and state monitoring.

A quad-rotor unmanned aerial vehicle parameter identification method based on adaptive extended Kalman filtering collects flight data through an unmanned aerial vehicle self-loaded sensor, and performs parameter identification work by using actual flight data in combination with an adaptive extended Kalman filtering algorithm inferred by variational Bayesian. The method comprises the steps of firstly converting a nonlinear system state equation and an observation equation into linear equations, then deriving a state, a prediction error and a measurement noise covariance matrix based on a variational Bayesian (VB) machine learning method by selecting inverse Warshard (IW) prior, and finally correcting an updating process of an EKF algorithm by utilizing the obtained covariance. According to the method, a relatively low parameter identification error can be achieved under the condition that a measurement error is unknown.

The invention relates to the technical field of intelligent power grids, in particular to an inspection vehicle tracking and communication optimization method and system, and the method comprises the steps: constructing an RIS-ISAC-NOMA system framework oriented to an intelligent power grid application scene, converting a non-line-of-sight NLOS path into an equivalent virtual line-of-sight LOS path through a reconfigurable intelligent metasurface RIS in a non-line-of-sight NLOS scene, and carrying out the tracking and communication optimization of an inspection vehicle through the virtual line-of-sight LOS path. A propagation path of an ISAC signal is adjusted in real time, and a vehicle tracking and predicting method based on an EKF algorithm is designed, so that efficient vehicle real-time sensing and communication are realized; a multi-inspection vehicle power distribution optimization scheme is provided in combination with the NOMA technology, the communication rate and the resource utilization efficiency of the system are further improved, meanwhile, the simulation result also verifies the advantages of the proposed system in the aspects of communication performance and resource utilization, and the overall efficiency of the system can be remarkably improved while the pilot signal overhead is reduced.

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 provides an exoskeleton system assistance control method and device based on a man-machine co-fusion strategy, and the method comprises the steps: collecting basic motion data of a human body and an exoskeleton system through various types of equipment, and initializing the motion state of the exoskeleton system; constructing a man-machine co-fusion kinetic model based on the basic motion data; on the basis of a man-machine co-fusion kinetic model, processing the collected real-time data through an EKF algorithm, and estimating various dynamic parameters of a human body and an exoskeleton system; determining a power-assisted control strategy of the exoskeleton system in combination with the dynamic parameters and a man-machine co-fusion kinetic model, and adjusting the power-assisted control strategy according to the personalized parameters of the user; and controlling the exoskeleton system to execute the adjusted power-assisted control strategy. According to the method, the man-machine collaborative motion intention prediction algorithm is adopted for power assisting control, the power assisting accuracy and flexibility of the exoskeleton system can be improved, and the method can dynamically adapt to individual differences of users.

The invention relates to the technical field of space flight and aviation, in particular to a low-delay communication control system and method for an analog machine in a fixed flight training course. The control system comprises a multi-source sensor fusion module, a data simplification processing module, a dynamic time delay optimization communication framework, a distributed intelligent decision-making terminal, a virtual-real mapping synchronous controller, a milli delay regulation and control module and an intelligent sensing alarm interaction module. According to the invention, 4G, 5G and satellite double links are integrated, an optimal link is dynamically selected by adopting a Q-Learning algorithm, millisecond-level time delay control is realized, transmission reliability is improved through a packet loss rate compensation algorithm, multi-sensor data is fused based on an FPGA hardware accelerator and an EKF algorithm, and a data compression ratio is improved in combination with Huffman coding and Kalman filtering. The bandwidth requirement is greatly reduced, through software and hardware collaborative optimization, the end-to-end time delay is reduced to be within hundreds of milliseconds, the virtual-real synchronization precision is improved to the centimeter level, and the decision response speed is improved by two times.

The invention designs a vehicle attitude determination and positioning method, and particularly relates to a special vehicle vehicle-mounted combined attitude determination and positioning method based on a ground view angle odometer, and the method comprises the following steps: obtaining the captured data of the ground view angle odometer, including a road surface image and IMU (Inertial Measurement Unit) data; the method comprises the following steps: extracting feature points from a pavement image based on an SIFT (Scale Invariant Feature Transform) vehicle ground view angle road visual odometer front-end algorithm to form feature descriptors; the method comprises the following steps: optimizing and processing a multi-frame feature descriptor based on a BA optimized vehicle ground view angle visual odometer back-end algorithm to obtain inter-frame pose estimation; inter-frame pose estimation and IMU data are fused based on a loose coupling mode, and the state and pose of the vehicle are predicted through an EKF algorithm. Compared with the prior art, the method has the advantages of realizing real-time and accurate pose positioning of the special vehicle and the like.

The invention discloses a lithium iron phosphate battery state-of-charge estimation method, which comprises the following steps: establishing a first-order RC equivalent circuit model of a lithium iron phosphate battery, performing pull-type transformation on the first-order RC equivalent circuit model to obtain a complex frequency domain equation, bilinearly changing the complex frequency domain equation to a z plane to obtain a system function expression, and estimating the state-of-charge of the lithium iron phosphate battery according to the system function expression. Performing pull-type inverse transformation on the system function expression to a discrete time domain; identifying the parameters of the lithium iron phosphate battery by using an AFFRLS algorithm to obtain model parameter values; constructing a space state equation set of the first-order RC equivalent circuit model, estimating the state of charge of the lithium iron phosphate battery by using an EKF algorithm, and defining an SOC estimation error function; optimizing the process noise covariance matrix Q and the measurement noise covariance matrix R of the EKF by using a simulated annealing algorithm to obtain a final estimated SOC; the SOC estimation precision and stability of the lithium iron phosphate battery are improved through the simulated annealing algorithm.

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 electronic countermeasure and target positioning, and relates to a TDOA radiation source tracking and trajectory prediction method based on EKF. The method comprises the following steps: S1, setting N sensor nodes to measure a TDOA observation value; s2, establishing a radiation source motion state space model; s3, calculating a Jacobian matrix to linearize the nonlinear observation equation; s4, executing an EKF prediction and updating step, and updating state estimation by using a TDOA observation value; s5, predicting a future trajectory based on the current state; and S6, performing loop execution to realize continuous tracking. According to the method, the nonlinear problem of TDOA positioning is solved through the EKF algorithm, high-precision tracking and trajectory prediction of the high-speed moving radiation source are achieved, good robustness and real-time performance are achieved, the algorithm complexity is moderate, and the method is suitable for accurate positioning of the moving radiation source in an electronic countermeasure system.

The invention discloses a power grid equipment edge calculation and information transmission method and system based on an EKF-PPO algorithm. The method comprises the following steps: constructing a state evolution model of mobile eavesdropping equipment; constructing a radar measurement model; predicting a mobile eavesdropping device state and an MSE matrix based on the state evolution model; a channel between the unmanned aerial vehicle and the mobile eavesdropping device is calculated, and the trajectory and speed of the unmanned aerial vehicle, the bit number locally calculated by the edge device, the bit number uploaded to the unmanned aerial vehicle, the transmitted beam forming vector of the edge device and the frequency locally calculated by the edge device are designed by adopting a PPO algorithm; a Kalman gain is calculated using radar measurement noise, and a mobile eavesdropping device state and an MSE matrix are corrected based on the Kalman gain and real-time radar measurement data. The position of the mobile eavesdropping device is estimated in real time through the EKF algorithm, it is ensured that unloading data can be prevented from being exposed to the eavesdropping device, meanwhile, the flight path, the communication beam and the data unloading decision of the unmanned aerial vehicle are optimized through the PPO algorithm, and the safety and efficiency of signal transmission are guaranteed.

The invention discloses a lithium ion battery SOC (state of charge) and SOH (state of health) joint estimation method based on an FOMISUKF-EKF algorithm. The method comprises the following steps: acquiring dynamic response and static characteristic data required by battery modeling; selecting a fractional order equivalent circuit model and establishing a state-space equation; identifying each parameter value of the model by adopting an ant colony algorithm; after the identified initial parameters are injected into the algorithm, SOC estimation under a micro scale is realized based on fractional order unscented Kalman filtering and a multi-innovation sliding mode observer; and when a preset period is met, switching to a macroscale, taking the SOC sequence as input, and performing joint identification on the battery parameters and SOH by adopting an EKF algorithm. According to the method, the multi-information theory and the sliding-mode observation algorithm are introduced on the basis of the traditional UKF algorithm, the sliding-mode observer is used as the state variable of the input end estimation system, the influence of system parameter change and external disturbance can be reduced to a certain extent, and rapid convergence and effective suppression of measurement noise are realized.

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 discloses a lithium ion battery parameter identification and state of charge (SOC) estimation method based on a particle swarm optimization and extended Kalman filter (PSO-EKF) algorithm, and relates to the technical field of lithium ion battery management systems. The method is used for solving the problem that a traditional SOC estimation method is insufficient in precision, adaptability and efficiency. According to the method, a bird flock foraging behavior is simulated through a PSO algorithm, a parameter-population scale is optimized by taking a convergence rate and diversity as targets, and rapid and accurate search of battery model parameters is realized; meanwhile, the SOC is accurately estimated by using the nonlinear processing capability of the EKF algorithm. And the two methods supplement each other, so that the precision and efficiency of parameter identification and SOC estimation are remarkably improved. The method provides powerful support for optimization and upgrading of a lithium ion battery management system, and has wide application prospects and remarkable economic and social benefits.

The invention provides a lithium battery parameter and state estimation method, system and device based on multi-scale Kalman filtering, and a medium. The method comprises the following steps: S1, establishing a battery model; s2, testing the open-circuit voltage of the lithium ion battery to obtain an OCV-SOC fitting curve; s3, discretizing the model state equation of the battery model to obtain a battery model state equation; and S4, acquiring sampling data of the lithium battery, inputting the sampling data into a battery model state equation, estimating the SOC according to a calculation result of the battery model state equation by using an MIUKF algorithm in each time period of the microscopic time scale, obtaining a corresponding OCV according to the OCV-SOC fitting curve and the SOC, performing battery parameter online identification by using an EKF algorithm at each time of the macroscopic time scale, and obtaining a battery parameter online identification result. And updating the battery model state equation by using the identified battery parameter estimation value. According to the method, the SOC estimation error is obviously reduced, the problem of poor SOC estimation precision caused by parameter change in the working process of the lithium battery is solved, and the precision and robustness are 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 provides a mixed pot transfer trolley and method based on SLAM (Simultaneous Localization and Mapping) technology and GNSS (Global Navigation Satellite System) technology fusion positioning, which comprises a laser radar, an IMU (Inertial Measurement Unit), a GNSS satellite positioning unit, a microprocessor and an embedded processor, an embedded underlying control system, an SLAM mapping and positioning system, an SLAM and GNSS fusion positioning system and a global path planning and trajectory tracking system are arranged in the microprocessor, the microprocessor is in communication connection with the embedded processor, and the embedded processor is in communication connection with the actuator. The relative position of the transfer trolley is determined in real time according to distance information acquired by a laser radar and prior information of a laser speedometer and IMU data fused by an EKF algorithm through an SLAM mapping and positioning subsystem, and a point cloud environment map is constructed; the GNSS positioning system provides an initial position of SLAM positioning and real-time correction, and the trajectory tracking subsystem calculates the advancing linear speed and the steering angle of the transfer trolley in each control period in real time; therefore, the transfer trolley can track the preset walking path.

The invention relates to a self-adaptive all-attitude heading estimation method taking a portable terminal as a carrier, which adopts two groups of Euler angles to represent the attitude of the portable terminal, solves the problem of singular value existing in a single representation method, and can correctly solve the heading angle of the equipment under the condition that any pedestrian carries the terminal equipment. An EKF algorithm based on quaternion is adopted for attitude updating, then quaternion is directly converted into two groups of Euler angles, data of an accelerometer and a magnetometer are fused in the method, calibration is provided for attitude updating, and compared with a method for performing attitude updating on the two groups of Euler angles, the precision is higher, and the reliability is higher; the course angles in the two groups of Euler angles are fused by using the adaptive factor, and frequent attitude switching is not needed, so that the condition that the attitude of the portable terminal equipment is changeable during use is better met; through a weight distribution method, the situation of numerical jump of the final output course angle is optimized, and the precision and stability of the result are ensured.

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 invention discloses a liquid level indication anti-leakage tray based on a weight sensor. The liquid level indication anti-leakage tray is characterized in that the weight sensor detects weight signals of chemicals in a liquid medicine barrel; the signal processing module converts signals detected by the weight sensor into liquid level height, the liquid level height is converted into liquid level percentage, the liquid pumping rate of a liquid medicine pump is calibrated online in real time through RLS, the reference pump speed is input into an EKF algorithm, the liquid level height is fused through the EKF algorithm, a nonlinear kinetic model is constructed to predict the liquid level track, and the liquid level return time is calculated. Calculating safety trigger time according to the liquid level return-to-zero time, designing a dynamic calculation interval function based on the remaining time, and adaptively adjusting the execution frequency of a prediction algorithm according to the liquid level height and the liquid level change rate; and the signal output module sends a shutdown control signal to the liquid medicine pump according to the time point of the safety triggering time. The liquid level monitoring function of converting the weight into the liquid level is achieved, and when the liquid level of liquid medicine is about to be zero, the liquid medicine pump is automatically stopped working.

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 invention provides an unmanned aerial vehicle maneuvering target prediction and tracking method based on an improved dynamic window method, and the method comprises the following steps: S1, building an unmanned aerial vehicle kinematic model and maneuvering target multi-model state estimation, and obtaining a constant speed CV model, a constant steering rate speed CTRV model and an unmanned aerial vehicle system state equation of a maneuvering target; s2, based on the model, adopting an IMM-EKF algorithm of a self-adaptive model probability to obtain a prediction track cluster of the maneuvering target; s3, constructing a potential field virtual target point based on the predicted trajectory cluster of the maneuvering target and an unmanned aerial vehicle system state equation; and S4, combining the potential field virtual target point with the improved DWA algorithm to obtain an unmanned aerial vehicle control instruction, and executing the instruction to perform target prediction and tracking. Compared with the prior art, the method has the advantages that the technical problems that a traditional DWA algorithm is insufficient in target maneuverability adaptation, prone to falling into local optimum and low in tracking efficiency in maneuvering target tracking are solved, and the like.

The application discloses an electromagnetic array positioning method and device based on LSTM-EKF fusion and a readable medium, and relates to the electromagnetic positioning field.The method comprises the following steps: calculating the relative distance between each electromagnetic transmitting module and an electromagnetic receiving module at the current time through a magnetic dipole model; acquiring the position coordinates of each electromagnetic transmitting module, constructing input data together with the relative distance between each electromagnetic transmitting module and the electromagnetic receiving module at the current time, and inputting the input data into a trained position coordinate prediction model of the electromagnetic receiving module to output the prediction value of the position coordinates of the electromagnetic receiving module at the current time; initializing the estimated value of a state vector and a covariance matrix thereof to obtain an initial estimated value of the state vector and an initial covariance matrix, and inputting the initial estimated value of the state vector and the initial covariance matrix into an EKF algorithm to output the final estimated value of the state vector.The application solves the problems of the existing electromagnetic positioning technology, such as noise sensitivity, susceptibility to metal interference, significant decline in positioning accuracy and limited working range.

The invention discloses a multi-robot cooperative positioning method and system based on UWB relative distance measurement, relates to the technical field of multi-robot cooperative positioning, and aims to solve the problem of cluster cooperative autonomous navigation of a multi-robot system in an environment that external navigation equipment is unavailable or weak communication exists. According to the technical key points, a state model of a multi-robot cooperative positioning problem and a UWB relative measurement model are established, and a linearization error state system is deduced; the reason of inconsistency of standard extended Kalman filtering in cooperative positioning is analyzed, and the essence of the standard extended Kalman filtering is revealed to be from considerable asynchronization between an original nonlinear system and a linearization error state system of an estimator. The invention designs a method based on linear time-varying transformation, constructs a KD-EKF algorithm with a time-invariant and unobservable subspace, and performs Monte Carlo contrast simulation to verify that the KD-EKF has higher positioning precision and better consistency compared with a standard EKF method and an FEJ-EKF method. A multi-robot system experimental platform based on the Tucker ground robot is developed, and experimental verification is carried out on the designed KD-CL algorithm. Experimental results verify the superiority and effectiveness of the proposed algorithm.

The invention discloses a lithium ion battery charge state estimation method based on an improved AO-EKF algorithm, and relates to a lithium ion battery, and the estimation method comprises the steps: building a lithium ion circuit equivalent model, obtaining a discretization state equation and a terminal voltage observation equation corresponding to the circuit equivalent model, and carrying out different battery characteristic tests, identifying parameters in the circuit equivalent model; introducing Tent chaotic mapping to initialize a cluster of the eagle optimization algorithm, setting a dynamic switching condition of a cluster search strategy of the eagle optimization algorithm, and further optimizing a noise covariance matrix and an observation covariance matrix of the system in the EKF algorithm process by combining with the circuit equivalent model after parameter identification; and based on the optimized noise covariance matrix and observation covariance matrix, optimizing to obtain a state vector updating equation and an error covariance updating equation so as to output a final SOC estimation value. According to the method, the accuracy of the lithium ion SOC can be improved, and estimation errors caused by environment changes and battery characteristic changes are avoided.

The invention relates to the technical field of robot control, in particular to a humanoid robot foot type walking anti-disturbance navigation method, which is used for solving the problem of disturbance rejection in the aspect of tolerating model uncertainty by adopting the method provided by the invention. The problem of state estimation precision degradation caused by model uncertainty caused by Jacobian matrix perturbation in the first-order linear approximation process is effectively suppressed, and the first-order state estimation precision of the humanoid robot nonlinear system is remarkably improved. According to the method, real state distribution is taken as a center, a Warisstein distance fuzzy set is constructed as an allowable neighborhood, and an EKF algorithm is modeled into a game state estimation problem based on Warisstein distance fuzzy set constraint, so that the navigation and positioning precision of the humanoid robot is remarkably improved, and the method has great significance in wider application of the humanoid robot.

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.