55 results about "State model" patented technology

The invention discloses an improved Kalman filtering ocean current estimation method based on inertial information and DVL speed. The method comprises the following steps: S1, system initialization and data acquisition; s2, performing inertial navigation system (INS) resolving, namely performing mechanical arrangement resolving based on IMU data; s3, constructing a fusion filtering state model and an observation model; s4, adaptive Kalman filtering iteration estimation: after discretizing the state equation and the observation equation, executing filtering iteration according to a period; and S5, state feedback correction and ocean current output. According to the improved Kalman filtering ocean current estimation method based on inertial information and DVL speed, a fusion framework of an augmented state model and adaptive Kalman filtering is constructed, and high-precision ocean current estimation and navigation correction are realized through five key processes.

The invention provides an integrated navigation method combining starlight refraction positioning with inertial navigation in a near space, and belongs to the technical field of starlight refraction navigation positioning. The method comprises the following steps: firstly, determining a state model of starlight refraction positioning in a near space by taking an error equation of an inertial navigation system of a near space aircraft as a state equation; designing a near space starlight refraction positioning algorithm, identifying a refraction star by using a star sensor on the aircraft, acquiring a refraction angle as a measurement, and establishing a measurement model of starlight refraction positioning in a near space; and obtaining the optimal estimation value of the system state variable by using an extended Kalman filter (EKF) method. The method has the advantages of being suitable for near space, high in reliability, high in autonomy and the like, and can be finally used for pure astronomical positioning and astronomical / inertial integrated navigation.

This application provides a method and apparatus for simulating the performance degradation of a high-power power supply. The method includes: obtaining a spatial state equation for the output voltage of the high-power power supply system based on an equivalent circuit model of the high-power power supply; acquiring performance degradation models of multiple components with degradation characteristics in the high-power power supply system, and importing the performance degradation models of the multiple components with degradation characteristics into the spatial state equation to obtain a spatial state model; using the spatial state model to obtain performance degradation array data of the high-power power supply corresponding to each number of runs; and concatenating the performance degradation array data of the high-power power supply corresponding to each number of runs to obtain performance degradation time sequence data of the high-power power supply.

The invention relates to a self-adaptive control method for a multi-mechanical-arm system with unpredictable states. The method comprises the following steps: constructing a communication topological structure of the multi-mechanical-arm system by utilizing a graph theory; a dynamic model of the multi-mechanical-arm system is established, and the dynamic model of the system is converted into a state model; setting a leader trajectory and defining a collaborative error model; establishing a state observer, and estimating an unmeasurable system state variable by using the state observer; constructing a radial basis function neural network model, and estimating an uncertain nonlinear function existing in the system; and a self-triggering mechanism is utilized to design triggering conditions so as to dynamically prolong a triggering interval, and a controller based on cross coupling errors is designed so as to control the multiple mechanical arms to track an expected trajectory within the specified time. Aiming at a multi-mechanical-arm system with unmeasurable states, the invention designs a self-adaptive neural network specified time controller based on a self-triggering mechanism, so that all mechanical arms can be converged to a specific track established by a leader within specified time.

The invention provides a setting calculation method and system based on different tenses, equipment and a storage medium, and belongs to the field of electric power systems.The method comprises the steps that first order reduction is conducted on a multi-tense model, and a first calculation model and a second calculation model are obtained, the first calculation model comprises a common model of a past tense model, a current tense model and a future tense model, and the second calculation model comprises a difference model of the past tense model, a difference model of the current tense model and a difference model of the future tense model; performing double order reduction on the first calculation model to obtain a plurality of common calculation units; and performing triple order reduction on the plurality of common calculation units and the second calculation model based on the target setting demand to obtain a target model corresponding to the target demand, and determining a setting strategy corresponding to the target demand according to the target model. According to the invention, rapid conversion calculation of the power grid setting constant value in different time states can be realized.

This application provides a target localization method, apparatus, electronic device, and storage medium. The method obtains the initial position of the target object by allocating distance through optimal sub-patterns; it pre-constructs a motion state model of the target object, predicts the state vector of the target object at the next moment based on the state transition equation of the motion state model, and determines the prior position of the target; it filters the current real observation set based on the prior position of the target to obtain a first observation set; it searches and locates the target based on the first observation set, using the previous position of the target object as the center, and determines the real-time target position; it narrows the search range to improve the real-time performance of localization; it fuses the real-time target position with the prior position of the target to obtain a corrected target position, and uses the corrected target position as the target localization result. By fusing the prediction and observation, the respective errors are offset, solving the problem of excessive localization error and improving localization accuracy and reliability.

The invention relates to the technical field of intelligent control, in particular to a cut-tobacco dryer steady-state model predictive control method based on Kalman filtering time delay correction, which comprises the following steps of: utilizing second-level historical production data and a system identification technology to process a cut-tobacco drying steady-state production process; according to the method, a dynamic transfer function prediction model and a state-space equation model from the moisture removal air door opening degree and the cylinder wall temperature to the outlet moisture are established, an appropriate Kalman filter (KF) is selected to carry out online correction on a cut-tobacco dryer prediction model with time delay, and the control of the process parameters in the cut-tobacco drying steady-state production stage is realized by using a model predictive controller (MPC) algorithm. Therefore, ideal outlet material moisture is achieved, and the problems that in the cut-tobacco drying process of an existing cut-tobacco dryer, dynamic characteristics of a system are ignored in a control method applied to the cut-tobacco drying process of the cut-tobacco dryer, and the control effect of the cut-tobacco drying process of the cut-tobacco dryer is poor due to the fact that the system with large inertia for controlling the cut-tobacco drying process of the cut-tobacco dryer is poor in prediction effect are solved.

The invention discloses a high-order extended Kalman filtering method for a nonlinear system. The method comprises the following steps: S1, establishing a nonlinear system composed of a linear state model equation and a nonlinear measurement model equation; s2, establishing a calculation algorithm for solving the statistical characteristics of the Kronecker product type composite random variables; s3, analyzing the high-order characteristics of the state estimation error of the nonlinear system by using the algorithm established in the step S2; s4, combining the known measurement information with the high-order features of the errors, designing and optimizing extended Kalman filtering, and gradually estimating the next state in the nonlinear system; and S5, circulating the process of the step S4, estimating the state of the nonlinear system step by step until the state estimation of the set step number is completed, and obtaining the state estimation value of each step. According to the method, the estimation model in the filtering process is adaptively adjusted based on the high-order statistical characteristics of the system state estimation error and the known measurement information, so that the stability and the precision of the filter are improved.

A complex sea condition sea target positioning and tracking method constrained by dependent variables belongs to the technical field of intelligent maritime affair monitoring and ship navigation, and comprises the following steps: S1, constructing a sea target positioning and tracking state model including a state equation based on uniform linear motion and two observation equations adapted to AIS / GNSS and radar; s2, establishing a limited dependent variable observation model, and quantitatively observing nonlinear correlation between loss / truncation and the real state of the target; s3, performing prior prediction of the current target state and the covariance based on the historical state; s4, fusing the prediction state and actual observation (including censored data) to complete state and error covariance updating; and S5, introducing a Bernoulli random variable to calculate an observation state adaptive weight. Through combination of the limited dependent variable model and the filtering method, the problems of trajectory breakage and target missing detection of a traditional tracking method caused by incomplete observation data under complex sea conditions are solved, and the method is directly integrated to a shipborne radar monitoring system and a maritime affair supervision platform.

Techniques are provided for adjusting a spectral density for computing a filtering solution. In an embodiment, a rolling history of correction data for a current epoch and previous epochs is created to compute a statistical value. The statistical value can be used to compute long-term and short-term averages for the current epoch. The long-term and short-term averages can be used to compute a ratio value that can be a scale factor for adjusting the spectral density. In an embodiment, sensor data for a current epoch can be obtained and compared to one or more threshold values and the spectral density can be adjusted when the threshold values are exceeded. In an embodiment, misclosure values from two different filters may be used to determine if a steady-state model of a first filter or a disturbed model of a second filter should be used to compute a filtering solution.

The embodiment of the invention discloses a GNSS receiver clock modeling method based on power-law noise, and relates to the technical field of satellite navigation positioning, and the method comprises the steps: obtaining phase difference data between a local clock and a reference clock, and obtaining a diffusion coefficient of clock noise through extraction based on the phase difference data; establishing a clock state space equation containing a clock offset parameter and a frequency offset parameter, and calculating a process noise covariance matrix of the clock state space equation based on the diffusion coefficient; and embedding the two-dimensional clock random state model and the process noise covariance matrix into a process noise embedding filtering estimation algorithm, and calculating to obtain a clock offset parameter, a frequency offset parameter and a three-dimensional coordinate parameter of the receiver. According to the method, estimation noise of receiver frequency deviation can be remarkably suppressed, and the short-term stability of the elevation direction in GNSS dynamic positioning is effectively improved. The method and the device are suitable for the GNSS terminal adopting a low-cost clock so as to realize high-precision time synchronization and positioning.

The application provides a multi-parameter joint calibration method based on graph optimization of inertial and ultra-short baseline, applies the graph optimization technology to the multi-parameter joint calibration of inertial / ultra-short baseline, and the whole process comprises the following steps: establishing a multi-error parameter state model, constructing a multi-error parameter calibration factor, calculating based on a nonlinear optimization problem of an incremental interval balance, and finally obtaining accurate multi-error parameters. Compared with the prior art, the application can simultaneously estimate the responder position error, the rod arm error and the installation angle error, sufficiently utilizes historical observation data, solves the nonlinear problem of the optimization equation through repeated iteration and relinearization, and has the advantages of high calibration precision and fast calibration speed.

The present invention relates to the field of equipment testing technology, and specifically to an intelligent thermostat durability testing system, comprising: a state modeling unit, the state modeling unit being used to define a state set and an extended variable set of a target thermostat, defining a state transition condition of the target thermostat based on the extended variable set and the state set, and constructing a state transition diagram based on the extended variable set, the state set, and the state transition condition. By defining the state set, the extended variable set, and the state transition condition of the target thermostat, the present invention can accurately simulate and describe the behavior and state changes of the thermostat under different operating conditions. By constructing a state transition diagram, the present invention can ensure that test cases cover all operating states of the thermostat and comprehensively test the system, thereby better evaluating the reliability of the device.

This invention discloses an ADRC method for attenuating periodic disturbances in time-delay systems, belonging to the field of automatic control technology. The method includes: 1. Obtaining the controlled process model and disturbance frequency, and establishing an extended state model containing the periodic disturbance; 2. Constructing a reduced-order extended state observer and tuning the gain using the bandwidth parameter method; 3. Introducing a pure time-delay module into the observer's output channel to construct an ADRC structure; 4. Revealing the mechanism by which periodic disturbances in time-delay systems cannot be attenuated through two-degree-of-freedom equivalent transformation analysis; 5. Based on the frequency domain characteristic equation, analytically determining the delay of the time-delay module to achieve effective attenuation of periodic disturbances at specific frequencies. This invention solves the problem of attenuating periodic disturbances in time-delay systems without significantly increasing structural complexity. Compared to traditional schemes that require high-gain or complex prediction structures, this invention only requires adjusting the pure time delay of the observer's output channel, and the parameter tuning process is clear and intuitive, significantly lowering the threshold for engineering applications.

The invention discloses an in-situ monitoring data anomaly detection method and device based on trend decomposition and state modeling. The method comprises the following steps: preprocessing an ontology sequence and an environment sequence acquired in an in-situ monitoring area; decomposing the two types of sequences into a long-term trend term, a seasonal term and a residual term, normalizing, fragmenting and splicing into a fragment set; a fragment set is extracted in batches, and value, position and time coding and fusion are carried out on each component fragment; inputting each coding segment into a spatial state modeling module and an attention mechanism module, fusing results to obtain a predicted long-term trend, seasons and residual errors, and carrying out reverse normalization and splicing fusion to obtain a final prediction result; and processing a to-be-predicted ontology sequence and a corresponding environment sequence according to the previous steps, reasoning by using the trained model to obtain ontology prediction fragments, splicing the ontology prediction fragments into a continuous prediction sequence, calculating a prediction residual error, and performing bilateral exponentially weighted moving average anomaly detection. According to the invention, the accuracy and stability of anomaly detection can be effectively improved, and the timeliness and reliability of early warning are improved.

This invention primarily relates to the field of thermal power unit system technology, specifically a method for identifying a closed-loop optimized control system for a deep peak-shaving thermal power unit. The method includes: S100 establishing a spatial state model of the thermal power unit system; S200 designing linear quadratic controller parameters for the spatial state model of the thermal power unit system, calculating the optimal control law through a state feedback matrix, and forming a closed-loop system of the thermal power unit; and obtaining the control input, control output, and system setpoint of the closed-loop system; S300 decomposing the closed-loop system into two open-loop subsystems; S400 obtaining the sensitivity function of the closed-loop system by performing subspace identification on the control input and system setpoint, and constructing a noise-free intermediate signal using the sensitivity function; and S500 obtaining a consistent and unbiased estimate of the parameters of the thermal power unit system by performing subspace identification on the intermediate signal and the control output of the closed-loop system.

The invention provides a cross-coupling global continuous robust control method, and relates to the technical field of motor control. The method comprises the steps that forklift physical parameters and controller parameters are initialized; the expected speeds of the two forklifts are determined according to the preset track; acquiring an actual speed and calculating a self-calibration error and a cross coupling error; constructing a state space model; determining an error vector and an error first-order derivative; establishing an objective function, setting a control coefficient, constructing a control function, and obtaining a global continuous robust control rate based on the control function; and the self-calibration error and the cross coupling error are substituted into the global continuous robust control rate, the steering angle and the accelerated speed are output to execution mechanisms of the first forklift and the second forklift, and global continuous and stable synchronous movement of the two forklifts is achieved. The sliding mode buffeting is eliminated by introducing the global continuous control signal, and the state model containing the lumped disturbance term and the Hurvetz polynomial stability verification mechanism are constructed, so that the global continuous stability of the system is realized.

The invention discloses a signal denoising and carrier synchronization method and device of a continuous wave mud pulse system, and belongs to the field of measurement while drilling in petroleum drilling. The method comprises the following steps: S1, preprocessing a collected mud pulse signal; s2, adjusting a noise covariance matrix of an adaptive Kalman filter in real time based on an expectation maximization algorithm, and reconstructing and filtering harmonic waves of each order of pump noise through a pump noise linear time-invariant space state model by using the adjusted adaptive Kalman filter; s3, filtering random noise by adopting a wavelet threshold denoising method; s4, performing frame synchronization on the denoised signal by using the m sequence; s5, demodulating the mud pulse signal by adopting a BPSK (Binary Phase Shift Keying) orthogonal demodulation technology; and S6, adjusting a noise covariance matrix of the unscented Kalman filter in real time based on an expectation maximization algorithm, and estimating and compensating the phase offset by using the adjusted unscented Kalman filter through the nonlinear state space model of the phase offset and the frequency offset.

The invention discloses a DNN-based adaptive optimization control method for a fractional order single-machine infinite bus system, and relates to the field of single-machine infinite bus system control. The method comprises the following steps: establishing a dynamical model of a single-machine infinite bus system, and converting the dynamical model of the system into a state model; designing a function approximate DNN architecture, approaching an unknown function in the system, and designing a weight updating law based on a first-order Taylor series to reduce the mathematical difficulty; in the backstepping process, a virtual controller and an actual controller are constructed by utilizing an optimization backstepping technology, and the overall control optimization of the system is realized; an event trigger function is designed, and consumption of system communication resources is reduced; and carrying out Lyapunov analysis to ensure that each signal of the system is bounded. The adaptive optimization backstepping controller based on the DNN architecture is designed for a fractional order single-machine infinite bus system, unknown nonlinear terms in the system can be compensated, and a single-machine power angle tracks a given reference signal.

One aspect of the invention relates to a method for controlling a system, an output quantity x of which system is a function of an input quantity u, the method comprising: - obtaining (410) a space-state model of the system; - obtaining (420) a target value (I) of the output quantity x; - receiving (430) an observed value y k of the output quantity; - updating (450) the state equation of the space-state model of the system based on the received observed value y k ; - determining (460) a value u k of the input quantity u required to obtain the target value (I) of the output quantity x using a predictive control algorithm based on the updated model of the system; and - controlling (470) the system based on the value u k of the determined input quantity u.

The invention discloses an asteroid multilayer internal structure inversion method based on relative position measurement, and belongs to the technical field of aerospace. A multi-layer model is established for the internal structure of the differentiated asteroid, and the gravitational potential of the differentiated asteroid to an external point is calculated; an asteroid multilayer internal structure inversion system is constructed, the position and speed of a surrounding detector, the position of an attached beacon and asteroid multilayer model parameters are used as state variables, and the inversion system further comprises a state model and an observation model based on relative position measurement; unscented Kalman filter estimation is used on the premise that noise obeys Gaussian distribution, a final state variable estimation value and covariance are obtained after accumulation is conducted for a certain time, asteroid multi-layer model parameter related estimation values are extracted, and inversion of an asteroid multi-layer internal structure is achieved. According to the method, the error between a given state variable nominal value set and an obtained estimation value set is calculated, filtering parameters are optimized through error analysis, and the inversion precision of the asteroid multi-layer internal structure based on relative position measurement is further guaranteed.

The application discloses a composite seeker jamming track recognition method based on an interactive multi-model, which comprises the following steps: firstly, initializing system parameters, assuming the motion parameters of a target and jamming; secondly, determining a state model used for describing the motion form of the target, and then modeling a multi-model system; thirdly, processing the state model to obtain updated model probability and system state estimation; fourthly, calculating a jamming decision threshold according to the updated model probability, so as to determine the appearing jamming track; fifthly, further identifying the specific jamming; and finally, outputting a jamming track serial number. The application introduces the motion modeling of jamming into the composite seeker, effectively improves the jamming recognition probability of the track gradual change, does not need to introduce an extra high-dimensional feature calculation, removes the jamming track in the track level dimension, and has the advantages of small engineering implementation cost, easy realization and the like.

The application relates to the technical field of deep space exploration and discloses a deep space exploration intelligent combined navigation method, system, device and medium. The method comprises the following steps: establishing an orbit dynamics model of a probe when the probe is detecting a planet at the end of a cruising stage; according to the orbit dynamics model, establishing a state model and a measurement model of the probe when the probe is autonomously navigating the planet at the end of the cruising stage; obtaining state error covariance of the state model and measurement error covariance of the measurement model as state input of a deep Q network, and obtaining multiple free action directions of the state error covariance and the measurement error covariance as action output of the deep Q network, so as to obtain optimal state error covariance and optimal measurement error covariance through the deep Q network; and according to the real-time optimal state error covariance and the real-time optimal measurement error covariance of the probe at the end of the cruising stage, performing state estimation on the probe, so as to complete autonomous navigation of the probe on the planet at the end of the cruising stage.

The invention discloses a shield tunneling composite stratum intelligent identification method and system based on a time sequence convolution space state model and a self-adaptive threshold value, and the method comprises the steps: collecting a real-time monitoring data set for shield tunneling composite stratum identification, carrying out the preprocessing of the real-time monitoring data set, and generating an initial stratum time sequence data feature; extracting and enhancing the initial stratum time sequence data features by using a state space model, and obtaining time sequence dynamic features; according to the invention, under the condition of keeping linear calculation complexity, joint optimization is carried out on a long-time dependency relationship and class imbalance, and high-precision and low-delay stratum type identification is carried out on a shield tunneling site; and the judgment threshold is dynamically updated by introducing an online feedback mechanism, so that the recall rate of a small number of rare categories and the accuracy of common categories can be considered, the recognition accuracy of tunneling stratum recognition is remarkably improved, the reliability of a tunneling stratum recognition result is guaranteed, and the method is suitable for being widely popularized and used.

The application discloses a sampling device and a chemical system, and belongs to the technical field of chemical systems. The application comprises collecting state data in a chemical pipeline, collecting flow velocity data used by the chemical pipeline, pre-processing the state data in the chemical pipeline, including data cleaning, removing outliers and data alignment, establishing a state model in the chemical pipeline by using the state data in the chemical pipeline and training the state data in the chemical pipeline to improve the generalization ability and prediction accuracy of the model, applying the state model in the chemical pipeline to real-time monitoring after the state model in the chemical pipeline is trained and verified, adjusting the sampling amount of the chemical pipeline and the opening and closing of the switching valve mechanism according to the prediction result, and determining the flow velocity in the pipeline through the setting of the state model in the chemical pipeline, so that the flow direction can be changed according to the situation to realize bidirectional flow, thereby sampling under the influence of transportation, and improving the practicability.

A satellite bidirectional and GPS different time frequency transmission link fusion method, data preprocessing, obtaining link data set W and link data set G; respectively establishing state model and measurement model of link data set W and link data set G; using wavelet decomposition method to scale decomposition of state model and measurement model of link data set W and link data set G, link data set W and link data set G are decomposed to the same resolution, obtaining state model and measurement model of link data set W and link data set G on low resolution; Kalman filtering fusion is carried out on link data set W and link data set G at the same resolution; the Kalman filtering fusion result is obtained by using wavelet decomposition coefficient reconstruction method to obtain the original data fusion result; judge the reduction and stability of the fused link noise.

The present disclosure discloses a method and system for processing airborne gravity data based on K-F filtering, comprising the following steps: acquiring airborne gravity data from a measurement carrier and constructing a first mathematical model of airborne gravity anomalies; introducing an unknown adjustment coefficient into the first mathematical model to obtain a second mathematical model of gravity anomalies, wherein the unknown adjustment coefficient is an adjustment coefficient for which a specific value is to be obtained; organizing the second mathematical model into a Kalman filter state equation and a measurement equation to obtain a spatial state model; obtaining a specific value of the unknown adjustment coefficient based on the spatial state model; substituting the specific value into the second mathematical model and solving the second mathematical model to obtain a third mathematical model containing noise; and performing FIR low-pass filtering on the third mathematical model based on a preset cutoff frequency of the FIR low-pass filter. This improves the ability to handle dynamic noise.

The application relates to an efficient particle filter node positioning method for ocean sensor networks, including the following steps: 1) obtaining a first state vector of mean value of posterior probability at k−1 moment; 2) establishing the state model of the nonlinear system; 3) obtaining the observation model of the nonlinear system; 4) propagating particles, and calculating the state vector ū+k at the next moment according to the particle vector at k−1 moment; 5) obtaining the observed value and calculating the weight; 6) judging whether it is degraded, if not, the weight is used to calculate the position expectation, otherwise, 7) is executed; 7) modifying small-weight particles; 8) resampling the improved residual and normalizing the weights; and 9) using the weight of 8) to calculate the position expectation and realize node positioning.

This invention discloses an adaptive multi-source information fusion localization method and system based on lane line constraints, belonging to the field of autonomous driving technology. The method fuses data from an inertial measurement unit, satellite positioning module, wheel speed sensor, and lane line detection module using an extended Kalman filter to construct a state model including position, speed, attitude error, and sensor bias. Depending on the availability of a high-precision map, it flexibly uses position error or lateral deviation as the lane line constraint observation model. Furthermore, this invention introduces an adaptive observation noise adjustment mechanism based on lane line detection quality and a degradation processing mechanism based on chi-square detection. This invention effectively solves the positioning drift problem under satellite signal obstruction environments, improves vehicle lateral positioning accuracy and system robustness, and achieves low-cost, high-precision continuous positioning.