116 results about "Time derivative" patented technology

The invention discloses a solar radiation space-time prediction method and system based on physical information constraint and a neural network, and the method comprises the steps: collecting multi-dimensional time sequence meteorological data, extracting high-dimensional time sequence dynamic features, converting geographic space data into a fuzzy set, and carrying out the defuzzification of the fuzzy set through an inference rule, thereby obtaining geographic space features; and a gating mechanism is adopted to realize deep fusion of the space-time features to generate high-dimensional space-time fusion features. In a model training stage, an energy conservation equation is introduced into an optimization process, a physical residual error is constructed by calculating a time derivative and a space derivative of a predicted value, a physical constraint total loss function is formed in combination with a data loss item, and model parameters are updated by using a gradient descent method. According to the method, the accuracy and reliability of a prediction result are remarkably improved while the calculation efficiency is ensured, and the method is particularly suitable for solar radiation prediction under complex meteorological conditions; according to the method, abnormal prediction caused by data noise can be effectively corrected, and a solution with physical rationality and data adaptability is provided for the fields of solar resource evaluation, photovoltaic power generation power prediction and the like.

The present disclosure provides a computer-implemented system for automated augmentation of differential equation models. The system stores differential equations representing mechanistic behavior of physical or computational processes and constructs a hybrid computational solver by embedding a trainable universal approximator with adjustable parameters into the differential equations, where approximator outputs augment time derivatives during numerical integration. An iterative training process adjusts parameters through numerical integration, monitors integration failures, assigns infinite penalty values to loss functions when failures occur, and computes gradients using automatic differentiation otherwise. The system computes sensitivity metrics via Jacobian matrix evaluation, classifies input / output subsets as significant based on threshold-exceeding sensitivity metrics, generates a reduced approximator operating on classified subsets, and replaces the universal approximator with the reduced version to create an optimized solver.

The invention discloses a pulse electroplating detection method, a pulse electroplating device and equipment, and the method comprises the steps: synchronously collecting transient current density data of a three-pole assembly in a pulse switching time window, and calculating a time derivative of a current density ratio to obtain electrochemical stress gradient data; performing space-time correlation analysis on the stress gradient data of the continuous pulse period, and tracking a spatial propagation trajectory of stress anomaly; a key monitoring area is determined according to the propagation trajectory, and ion migration impedance sensitivity distribution is detected through micro-amplitude current modulation; performing multi-dimensional coupling analysis on the stress gradient, the propagation trajectory and the impedance sensitivity data, and identifying time sequence correlation characteristics of stress evolution and ion anomaly; and performing quantitative evaluation and grade division on the electrochemical state of the avoidance area based on the coupling correlation characteristics. According to the scheme, early recognition and predictive evaluation of the ion migration abnormity of the boundary layer of the avoidance area can be realized in the pulse electroplating process, and the electroplating quality control precision and the process stability are remarkably improved.

The invention relates to a lithium battery health state prediction method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: collecting multi-dimensional time sequence data generated by a lithium battery charging and discharging experiment, and processing the multi-dimensional time sequence data to generate a model input sequence; inputting the model input sequence into a deep neural network to obtain a time derivative of the state of health of the lithium battery at the current moment, and pre-training the deep neural network; inputting the model input sequence into a Bayesian LSTM network, outputting a predicted value of the lithium battery health state at the next moment, pre-training the Bayesian LSTM network, and performing uncertainty estimation on the predicted value of the lithium battery health state at the next moment; and constructing a total loss function, jointly training the deep neural network and the Bayesian LSTM network by using the total loss function to obtain a target prediction model, and predicting the state of health of the lithium battery. By adopting the method, the health state of the lithium battery can be predicted in a high-precision, high-reliability and high-adaptability manner.

The invention provides a novel multi-source information and damage coupled coal mine rock burst prediction method. The method comprises the following steps that rock mass fracture signals, a three-dimensional stress field and acoustic emission parameters are collected in real time through a micro-seismic monitoring array, an optical fiber stress sensor and a rock sound probe; an LSTM-attention fusion module is adopted to carry out spatio-temporal feature extraction on the collected multi-source data, an attention weight coefficient is generated, and a weighted fusion feature vector is output; calculating the rock mass damage degree based on a statistical damage mechanics Weibull distribution theory; based on the law of conservation of energy and the damage mechanics theory, a dynamic energy threshold function is constructed, the mining time effect and the rock mass damage state are incorporated into a quantitative evaluation system, and a dynamic energy accumulation rate threshold function is established by introducing a time accumulation effect and a damage coupling term; when the real-time energy accumulation rate is larger than or equal to the energy accumulation rate threshold value and the time derivative of the energy accumulation rate is larger than the sudden change threshold value, third-level early warning is triggered. According to the invention, advanced accurate early warning of the rock burst can be realized.

The invention belongs to the technical field of computational electromagnetics, and discloses a numerical calculation method for simulating electromagnetic wave propagation characteristics in time-varying plasma, which is a three-dimensional Newton-HIE-FDTD method for efficiently simulating propagation of electromagnetic waves in time-varying plasma based on a hybrid explicit-implicit finite difference time domain method and Newton's second law of motion. The method inherits the advantages of the Newton-ADE-FDTD method, the time-varying electron density is accurately represented through the time derivative, and the abundant dynamic characteristics of the time-varying plasma can be accurately captured. On the basis, the method also fuses the advantages of an HIE-FDTD method, so that the time step length of the algorithm is only restricted by the sizes of grids in two directions, and compared with other simulation methods based on time-varying plasma, the method provided by the invention is more excellent in calculation efficiency and is more suitable for large-scale popularization and application. The method is especially suitable for analysis and simulation of electromagnetic wave propagation characteristics in time-varying plasma.

The invention provides a novel finite difference time domain (FDTD) method based on a Newton second law of motion and a bilinear transformation (BT) technology. The method is used for simulating propagation of electromagnetic waves in time-varying magnetized plasma. According to the method, the time-varying electron density is accurately represented through the time derivative, so that abundant dynamic characteristics of the time-varying magnetized plasma can be captured more accurately. A numerical result shows that compared with a Newton-ADE-FDTD method, a traditional ADE-FDTD method and a BT-FDTD method, the Newton-BT-FDTD method provided by the invention has higher precision and calculation efficiency on the time-varying magnetized plasma, especially under the condition of the time-varying magnetized plasma with dramatic electron density change. In addition, the propagation characteristics of the electromagnetic waves in the time-varying magnetized plasma are analyzed.

The invention belongs to the technical field of power control, and particularly relates to a handheld laser welding head power control method and system based on the molten pool temperature, and the method comprises the following steps: obtaining a molten pool temperature sequence collected by a coaxial temperature sensor of a handheld laser welding head in real time; the polynomial order of a Savitzky-Golay filter is dynamically adjusted based on the second-order time derivative of the molten pool temperature sequence, the molten pool temperature sequence is filtered, and a real-time molten pool temperature measurement value is obtained; on the basis of the real-time molten pool temperature measurement value and the first-order time derivative of the real-time molten pool temperature measurement value, a preset arcing temperature threshold value, a steady-state temperature interval and a process adjustment gradient threshold value are compared, and it is judged that the current welding process is in one of the three stages of arcing, steady-state welding or arc suppression pit filling; when the steady-state welding phase is determined, the temperature deviation between the target temperature and the real-time molten pool temperature measurement value is calculated. Differential power control strategies for different process stages are achieved, and the overall quality and consistency of handheld laser welding are improved.

The invention relates to the technical field of biological radar signal processing, in particular to an FMCW radar-based heart beat interval estimation method, which comprises the following steps of: S1, converting a chest vibration echo phase time sequence obtained by irradiating a chest area of a monitored object by an FMCW radar into an acceleration time sequence by using a second-order time derivative; s2, dynamically mapping the center frequency and the wavelet order in a preset frequency analysis interval, constructing a self-adaptive wavelet dictionary, then executing multi-order wavelet time domain convolution operation on the acceleration time sequence by using the dictionary, and aggregating time-frequency energy distribution to generate a time-frequency energy diagram; and S3, performing a deconvolution operation reconstruction strategy based on an energy-guided multi-stage time-frequency feature screening technology and wavelet function conjugation, generating an approximate time domain signal of heart beat vibration, and completing heart beat information inversion. According to the method, the accuracy and robustness of IBI extraction can be effectively improved under the condition of low signal-to-noise ratio, so that stable monitoring of light and moderate HRV (heart rate variability) is supported.

The invention provides a three-dimensional water elasticity response calculation method based on boundary element-explicit finite element coupling, and belongs to the technical field of water elasticity response calculation based on computer data processing. The wave evolution of a fluid domain is solved by adopting a boundary element method, and the dynamic response of the elastic plate is solved by adopting an explicit finite element method. Inspired by an implicit boundary condition method of a wave-rigid body coupling effect, an explicit dynamic control equation of a structural domain elastic plate is substituted into a boundary condition met by an object plane acceleration potential of a fluid domain elastic plate, and an additional relation between an elastic plate surface fluid velocity potential time derivative and a normal derivative of the elastic plate surface fluid velocity potential time derivative is established; the implicit boundary condition is satisfied by the surface of the elastic plate. The deduced implicit boundary condition and the satisfied Laplacian equation are combined, and the value of the surface of the elastic plate can be accurately obtained. According to the three-dimensional time domain numerical method for wave-elastic plate coupling provided by the invention, stable and accurate calculation of wave load and structural response can be realized.

The invention discloses a thickener dosing control method and system, and relates to mineral processing. The method comprises the following steps: acquiring concentration values measured by an ith concentration sensor at different depths at a moment t; obtaining the turbidity T of the overflow water, the effective viscosity of the overflow water or underflow, the volume fraction of fine particles and the volume fraction of coarse particles in the overflow water, the Zeta potential of the feeding end, the Zeta potential of the overflow end, the time derivative of the Zeta potential of the overflow end, the equivalent diameter d of the particles, the density of the particles, the density of the fluid and the viscosity of the fluid; calculating the effective settling velocity of the particle swarm; carrying out online correction on the effective settling velocity by utilizing a least square method or a Kalman filtering algorithm; performing time lag correction on the concentration values at different depths by using the corrected effective settling velocity; correcting the turbidity T of the overflow water according to the corrected concentration profile; respectively calculating a flocculant dosing rate and a coagulant dosing rate; according to the invention, accurate cooperative control of the dosing rate of the flocculating agent and the coagulant is improved through the artificial intelligence model.

The invention relates to a temperature control method of a temperature controller, which comprises the following steps: S1, collecting multi-source heterogeneous data, fusing the collected data through an adaptive weighted Kalman filtering algorithm, and outputting a temperature field model, S2, inputting the temperature field model into a double-layer LSTM neural network, calculating the importance weight of each region of the temperature field model, and outputting a real-time temperature field and a time derivative dT / dt thereof, s4, calculating a heat capacity parameter C and a heat resistance parameter R in real time, and adjusting a PID (Proportion Integration Differentiation) parameter through a fuzzy rule base; S5, switching to a pulse strong control mode when dT / dt is detected to be greater than or equal to 5 DEG C, and switching to a PID mode when delta T is less than or equal to 5 DEG C and is stable for 2 seconds; the system has the advantage of accurate control.

The invention relates to the technical field of automatic control, and discloses a pelvic and abdominal muscle group cooperative training self-adaptive control system based on biological feedback, which comprises a state drift observation module, a dynamic threshold value resolving module and a self-adaptive control module, and is characterized in that the system monitors the amplitude attenuation rate of a main constraint variable in real time to update an attenuation coefficient; constructing a dynamic safety envelope containing slew rate limitation and amplitude saturation; when the first-order time derivative of the disturbance variable exceeds the limit, the controller locks the output slope of the disturbance variable, and dynamic oscillation of the time-varying coupling system caused by high-frequency pulse disturbance is suppressed by establishing a dynamic mapping mechanism of the disturbance loading rate and the main variable response bandwidth. The adaptive compensation of the parameter drift of the controlled object and the steady-state maintenance of the control precision are realized.

The invention relates to the technical field of limited space fire hazard flashover prediction. The invention provides a limited space fire hazard flashover prediction method and system based on a physical information neural network. The method comprises the following steps: acquiring sensor observation data and initial fuel distribution data; inputting the space-time coordinate vector and the initial fuel distribution data into a Mamba neural network to obtain predicted values of temperature, incident radiation and fuel mass fraction; calculating a space-time derivative of the predicted value, and constructing a physical residual term containing energy conservation, radiation transfer and fuel consumption equation residual, a boundary residual term and an observation residual term; weighted combination is carried out on the three types of residual errors to construct a loss function, and network parameters and to-be-learned physical parameters are updated through an optimization algorithm; and predicting a temperature field based on the updated network, and judging the time of detonation when the temperature reaches a detonation critical value. The problems of poor reliability and prediction deviation caused by lack of physical rule constraints of an existing data-driven fire prediction model are solved.

The present application relates to the technical field of data fusion, and specifically relates to a multi-modal sensor external parameter automatic calibration method and system, which comprises the following steps: acquiring a data frame sequence of a first sensor on a ship body and a second sensor on a mast and horizontal plane velocity data of a second sensor position, performing time derivative operation on the velocity data to obtain acceleration components and calculating a synthetic vector amplitude to identify a disturbance time period, obtaining a horizontal displacement sequence through time integration of the velocity data, combining vertical height to calculate a mast swing angle change sequence, converting the mast swing angle change sequence into a sensor space posture offset angle sequence, and finally performing coordinate transformation operation on the initial external parameter calibration parameters to obtain corrected external parameter calibration parameters. The present application can identify and compensate position offsets caused by external disturbances on sensor fixed structures in real time, and ensure the accuracy and reliability of multi-sensor data fusion.

The invention discloses a mineral resource intelligent prediction method based on a liquid neural network, and relates to the field of artificial intelligence and geological exploration cross technology, and the method comprises the steps: carrying out the time series reconstruction of a space-time coordinate observation data set, generating a multi-dimensional continuous space-time field function, and calculating a space gradient field and a time derivative field of the multi-dimensional continuous space-time field function, generating a continuous spatio-temporal data stream; and fusing the geological background static feature vector and the metallogenic dynamic feature vector, inputting the fused feature vector into a full connection layer where a liquid neural network branch and a convolutional neural network branch are jointly connected, mapping to generate a mineral resource predicted value, and meanwhile, generating a mineral resource quantity distribution diagram through spatial interpolation rendering. According to the method, the continuous spatio-temporal data flow is input into the liquid neural network branch for internal state evolution, the mineralization dynamic feature vector is generated, high-resolution capture of the dynamic time sequence mode in the mineralization process is achieved, and the accuracy and time sequence resolution of prediction of the mineralization dynamic process are improved.

The application relates to an industrial process state monitoring method based on robust space-time joint projection, which comprises the following steps: data preprocessing and standardization; data space manifold structure and time dynamic information capturing: calculating the similarity degree between samples according to neighborhood overlapping order similarity, obtaining a data similarity matrix, and thus obtaining the overall space manifold information of process data; subsequently, calculating a data time derivative matrix, extracting slow change characteristics in the data, and obtaining key dynamic information; feature extraction and monitoring model building based on robust space-time joint projection: comprehensively integrating space-time information, performing dimension reduction projection on the data, obtaining key low-dimensional characteristics for retaining the representation of system operation states, constructing monitoring statistics and monitoring thresholds based on the key low-dimensional characteristics, and building an offline process monitoring model; real-time online monitoring of the state of an industrial system.

The invention relates to the technical field of electric power system transient state analysis, in particular to a double-high-characteristic electric power transient state stability analysis method, system and device and a medium. The nonlinear part of the model is limited in an adjustable neighborhood of a given balance point so as to construct a Lyapunov function; the estimation of a Lyapunov function time derivative is improved by introducing a non-uniform limiting condition, and the conservative property is reduced; meanwhile, the construction of the Lyapunov function and the lower limit estimation of the fault clearing time are subjected to collaborative optimization, so that more accurate fault clearing time estimation is obtained; according to the method, the optimal Lyapunov function and parameters are solved by using the semi-definite programming problem, the method effectively adapts to the complex dynamic characteristics of high-proportion renewable energy and power electronic equipment, and the transient stability analysis precision and reliability of a power system are improved.

The invention relates to the field of unmanned aerial vehicle network security, in particular to a universal intrusion detection method for heterogeneous unmanned aerial vehicles. The method comprises the following steps: acquiring unmanned aerial vehicle sensor data, and preprocessing; extracting a timestamp disassembly feature, a time derivative feature and a periodic feature of a sample in the preprocessed data set; constructing a unified feature matrix; calculating feature importance according to the unified feature matrix to obtain a feature sequence; constructing a multi-layer Transform encoder, inputting the feature sequence into the multi-layer Transform encoder, performing aggregation processing to obtain an aggregation vector, and calculating an attack probability according to the aggregation vector; a binary cross entropy loss function is used to train a multilayer Transform encoder; and inputting a to-be-detected sample into the trained multi-layer Transformer encoder to obtain the probability of whether the to-be-detected sample is an attack or not. The method can be migrated and used among multiple UAV types, and is a universal security detection framework with cross-platform robustness and practicability.

The present application belongs to the technical field of industrial automatic linkage control, and relates to a combustion chamber nozzle regulation linkage control system, comprising: a state acquisition module, used for acquiring a controlled regulation unit displacement parameter and a physical interference carrier state parameter; a polarity phase discrimination module, used for real-time calculation of a first-order time derivative of the displacement parameter and determination of a polarity switching identifier; a matrix storage module, used for storing forward and reverse interference sub-matrices with heterogeneous weights; a compensation operation module, used for calling a sub-matrix and determining an asymmetric compensation amount when the polarity switching identifier jumps; a boundary constraint module, used for amplitude limiting processing of a compensation amount change rate; and a linkage control module, used for issuing a synchronous regulation instruction. The present application identifies asymmetric blocking characteristics of a physical response end, eliminates commutation transient compensation mismatch, and suppresses system cross oscillation in a multivariable strong coupling environment.

A computer-implemented method for estimating a health state of a solenoid valve including a solenoid for supporting a process control system. The method includes the steps of monitoring a current passing through the solenoid, determining a time period between an instant of initiating a state change of the solenoid valve and an instant when the time derivative of the current is discontinue, comparing the time period with a reference time period indicative for the health state of the solenoid valve thereby determining the health state.

The invention relates to the technical field of electronic data processing, and discloses a detection data processing method and system for a helium leak detector for a diaphragm compressor, and the method comprises the steps: obtaining an original helium concentration time sequence signal and a periodic frequency parameter of the stroke of the diaphragm compressor; constructing a notch filter bank, and filtering the original helium concentration time sequence signal to obtain a preliminary signal; performing wavelet transform decomposition on the initial signal, performing threshold processing on a wavelet coefficient, and then performing reconstruction to obtain a de-noised signal; iterative fitting is carried out on the de-noised signal, a dynamic background baseline is estimated, and a background correction signal is obtained; calculating a first-order time derivative sequence, a local information entropy sequence and morphological correlation; and when the amplitude of the first-order time derivative sequence is continuously higher than a first threshold value, the local information entropy sequence is lower than a second threshold value, and the morphological correlation degree is higher than a third threshold value, judging that leakage occurs. According to the invention, multi-dimensional and high-confidence identification can be carried out on the leakage event, and the detection sensitivity, the anti-interference capability and the reliability of tiny leakage are improved.

This application relates to the field of burner combustion control technology and discloses a burner fuel grading control optimization method. This method calculates the background noise energy during a silent monitoring period and selects the frequency of the minimum energy value as the target micro-perturbation frequency. A sinusoidal micro-perturbation signal is generated based on this frequency and superimposed on the command of the shift fuel regulating valve. Valve displacement and combustion chamber sound pressure signals are simultaneously acquired and orthogonal decoding is performed. Electromechanical transfer gain, thermoacoustic gain, and dynamic transmission phase difference are calculated and obtained. Based on the electromechanical transfer gain, it is determined whether amplitude compensation should be performed. A two-dimensional linkage verification matrix is ​​constructed by combining the time derivatives of the thermoacoustic gain and the dynamic transmission phase difference, thereby distinguishing between pure thermoacoustic mode over-limit and physical spatial anomalies, and executing the corresponding fuel grading closed-loop regulation or safety interlocking procedure. This invention eliminates valve mechanical execution errors, accurately distinguishes combustion instability conditions under strong noise backgrounds, and improves the safety of combustion system operation.

The invention discloses a power grid power flow analysis method and device based on a quotient gradient method and a neural network, and the method comprises the steps: converting a nonlinear algebraic equation set of flow calculation into an ordinary differential equation based on the quotient gradient method, and constructing a physical information neural network model according to the ordinary differential equation. A system state reference trajectory is generated through an ordinary differential equation solver to serve as a training data set, a composite loss function containing a predicted value and reference trajectory error and a predicted value time derivative and theoretical derivative error is defined, and a training model is iteratively trained. And inputting to-be-calculated working condition parameters and steady-state set time into the trained model, and outputting a steady-state power flow solution through forward propagation. According to the method, the convergence, the physical interpretability and the generalization ability under the ill-conditioned or extreme working condition are improved, and the real-time online analysis requirement is met.

The present application relates to the field of unmanned aerial vehicle network security, and particularly relates to a general intrusion detection method for heterogeneous unmanned aerial vehicles. The present application comprises: acquiring unmanned aerial vehicle sensor data and preprocessing; extracting timestamp disintegration features, time derivative features and periodic features of samples in the preprocessed data set; constructing a unified feature matrix; calculating feature importance according to the unified feature matrix to obtain a feature sequence; constructing a multi-layer Transformer encoder, inputting the feature sequence into the multi-layer Transformer encoder, performing aggregation processing to obtain an aggregation vector, and calculating an attack probability according to the aggregation vector; training the multi-layer Transformer encoder using a binary cross-entropy loss function; and inputting a to-be-detected sample into the trained multi-layer Transformer encoder to obtain a probability of whether it is an attack. The present application can be used between multiple UAV types, and has a cross-platform robust and practical general security detection framework.

The invention discloses a robot control method based on active disturbance observation and phase coupling control, and the method comprises the steps: collecting the current state quantity of a robot in real time, and constructing a joint space dynamic model; generating state estimation and disturbance estimation in the joint space dynamic model; a phase parameterization method is adopted to map the phase of the multi-degree-of-freedom action to an expected trajectory, and a closed-loop trajectory tracking and disturbance compensation control law is designed in combination with disturbance estimation and a sliding manifold mechanism; according to the method, a position error transformation design continuously-derivable robot auxiliary level function is introduced, the stability is analyzed by adopting a Lyapunov method, and a closed-loop trajectory tracking and disturbance compensation control law and a coupling gain adaptive law are substituted into a time derivative of a candidate Lyapunov function to complete the control of the robot. The control output can be kept smooth and continuous, instruction sudden change or attitude instability is avoided, high action success rate and landing stability can still be maintained when the environment changes, and the landing impact load peak value is effectively reduced.

This invention discloses a trace oxygen sampling control method, including: calculating the complex impedance modulus, phase angle, and time derivative based on real and imaginary impedance data; obtaining the double-layer perturbation index by weighted fusion of the impedance change rate and phase stability factor; adaptively determining the pressure fluctuation amplitude based on the double-layer perturbation index to protect sensor stability and improve replacement efficiency; and generating a micro-pressure differential modulation control parameter based on the pressure fluctuation amplitude. This invention solves the technical problem of traditional fixed-parameter control methods that cannot balance sensor protection and rapid response, and achieves intelligent and precise control of the trace oxygen sampling process.

The invention relates to a space spinning nutation non-cooperative target safe approaching control method. The method comprises the following steps: establishing dynamics and kinematics models of the relative attitude error and the relative orbit error of the double spacecrafts, and compacting the relative attitude error and relative orbit error model into a six-degree-of-freedom form; the method comprises the following steps: defining constraint functions and constraint auxiliary variables of safety and motion boundaries of a service spacecraft, and constructing a spacecraft auxiliary system which is used for rendezvous and docking and takes safety constraints into consideration; designing a shortest trace direction sliding mode variable for the spacecraft auxiliary system, wherein the shortest trace direction sliding mode variable enables the attitude and the relative orbit of the service spacecraft to maneuver to an expected attitude orbit required by a task from an initial state in a shortest path; a non-singular auxiliary switching function is designed, and the singular phenomenon is avoided when a time-related derivative is solved by a shortest trace direction sliding mode variable; and constructing a security approximation controller considering security constraints for the spatial spin nutation non-cooperative target.

The present invention relates to robotic arm control, and in particular to a method for controlling a preset performance of a robotic arm based on segmented threshold event triggering. The event triggering mechanism of the present invention can reduce data transmission and improve resource utilization, and the control law structure is simple and easy to implement. It comprises the following steps: step 1. establishing a mathematical model of a single-joint robotic arm; step 2. designing a preset performance function to limit the motion trajectory of the trajectory tracking error generated by the mathematical model of the single-joint robotic arm in combination with nonlinear transformation; step 3. constructing an instruction filter with filtering error compensation to process the time derivative of the virtual control law based on the intermediate variable obtained by the nonlinear transformation; step 4. designing an event-driven control law and an adaptive law based on the output of the instruction filter in combination with a neural network observer, a segmented threshold triggering mechanism and adaptive technology.

The application discloses a kind of hybrid energy storage auxiliary frequency modulation instruction prediction method and system of thermal power generating unit, belong to energy storage frequency modulation instruction prediction technical field, method: obtain original frequency modulation instruction sequence, carry out dynamic phase compensation;Phase reference value is calculated, and nonlinear correction is carried out;First correction signal is obtained;Information entropy gradient enhancement is carried out to sampling point;First correction signal is enhanced, and second correction signal is obtained;Chaotic time series reconstruction is carried out, and third correction signal is obtained;Chaotic time series reconstruction includes: constructing time delay embedding space to map to high-dimensional phase space, calculating local Lyapunov exponent, combining the time derivative of signal and chaotic adjustment factor to carry out dynamic reconstruction;Third correction signal is input to GRU neural network, and the output frequency modulation instruction prediction result is obtained.The application can predict the size of frequency modulation instruction in advance, let super-capacitor or battery act in advance, improve the yield value, and the degree of convenience and accuracy are high, with engineering applicability.