44 results about "Nonlinear kinetics" patented technology

The invention discloses a nonlinear dynamics calculation method for a gear pair. The method comprises the steps that the feedback effect of vibration displacement to the dynamic contact condition of agear surface of the gear pair in the actual running process is considered, and the nonlinear excitation of the dynamic meshing rigidity and dynamic comprehensive meshing errors is calculated. Dynamiccontact analysis of the gear pair and a system dynamics solution process are combined, and an excitation-response-feedback loop-locked calculation process of the system is achieved. According to themethod, the nonlinear dynamics phenomenon of the gear pair due to parameters such as gear surface errors and shape correction can be acquired, the dynamics behavior of the system in the dynamic contract process can be more truly simulated, and the calculation accuracy of dynamics is improved.

The invention discloses a coma degree evaluating method based on multiple indexes of non-linearity and complexity, which is used for monitoring the brain state of a patient in a coma, and can realize continuous coma depth monitoring, and coma phase grading and early warning. Multiple complexity indexes such as complexity, Lyapunov exponent, approximate entropy, related dimensions, and the like are extracted from coma electroencephalographic signals by adopting a nonlinear dynamics analytical method, and a comprehensive coma state exponent is obtained by combining a traditional GOS (Glasgow Outcome Scale) evaluation system and a traditional GCS (Glasgow Coma Scale) evaluation system; the coefficients of the correlation between the parameters are obtained by clinical experiments; a coma state grading database is founded and a parameter fusion coefficient is determined on the basis of clinical effects; and finally, complete conscious state grading evaluation exponents are founded to guide the treatment of a patient and make prognosis.

The invention discloses a cyanobacterial bloom predicting method based on a non-linear kinetic time-series model, which belongs to the water environment predicting field. The method comprises the following steps: using the blue algae growth rate as a time-varying parameter; creating a non-linear kinetic time-series model for the cyanobacteria growth with double nutrient salt cycling; using the combination of the numerical algorithm and the intelligent evolutionary algorithm, optimizing the constant parameters in the non-linear kinetic time-series model for the cyanobacteria growth; through setting the multivariate time-series model to realize the prediction of the time-varying parameter and the cyanobacteria biomass and through using the bifurcation theory and the central manifold theory to make a nonlinear kinetic analysis of the cyanobacteria growth time-varying system, obtaining the outbreak conditions for a cyanobacteria bloom so as to make an early warning of the bloom outbreak. The method proposed by the invention not only determines the conditions for a cyanobacteria bloom outbreak, but also improves the bloom prediction accuracy, providing an effective reference for an environmental protection department and governance decisions for water environment treatment.

A dynamic coupling time varying failure rate analysis method of a on-orbit space flexible gear mechanism comprises the seven steps that firstly, according to the fact that a shaft in the on-orbit space flexible gear mechanism is an elastic body, a kinetic equation of the flexible shaft is built, and the finite element method is adopted for analysis; secondly, a gear kinetic equation is built; thirdly, a nonlinear kinetics equation of coupling of the flexible shaft and a gear in the on-orbit space flexible gear mechanism is built; fourthly, transmission stimulation analysis is performed on the space flexible gear mechanism; fifthly, the Newmark algorithm is adopted to calculate a dynamic load coefficient; sixthly, a gear bending stress reliability limit state function is built in the on-orbit space flexible gear mechanism; seventhly, dynamic reliability and failure rate analysis are performed on the space flexible gear mechanism. The method solves the problem that reliability calculating accuracy is low because of nonlinear factors of a traditional transfer matrix method and mode superposition method, and has engineering practical value for improving reliability of the on-orbit space flexible gear mechanism and other spacecrafts. The method is also suitable for other flexible gear mechanisms.

The invention discloses a truck smoothness stability simulated analysis method. Simulated analysis is carried out on the phenomenon that the smoothness of a truck of a certain model number is sometimes good and sometimes bad, and the smoothness is not stable. The nonlinear kinetics theory is used in the field of truck smoothness, and the numerical simulation analysis method of the truck smoothness instability phenomenon is established. Simulation results are analyzed, simulation analysis results are displayed in a curve mode, and the smoothness instability phenomenon is expressed visually. The smoothness sudden changing reason is explained, and a corresponding solution and a certain foundation are provided for truck comfort and whole truck performance improving. A parameterized graphical user interface is developed based on a MATLAB developing platform, a user only needs to input basic parameters, the smoothness stability phenomenon can be subjected to simulated analysis, the method is simple, and operation is easy.

The invention provides an epileptic seizure EEG signal classification system based on nonlinear dynamics features and belongs to the technical field of neural information. The system comprises a preprocessing module, a feature extraction module, a feature sequencing module, a feature selection module and a classification module, wherein the preprocessing module removes noise in EEG data by discrete wavelet transform and acquires effective EEG data containing no noise; the feature extraction module calculates nonlinear dynamics features of each EEG signal by multiple entropy algorithms; the feature sequencing module sequences features by variance analysis; the feature selection module selects an optimized feature subset having the most significant influence on model accuracy by a front andback sequential algorithm; and the classification module converts judgment of epileptic seizure and seizure diapauses into binary classification problems by a least square support vector machine algorithm. The classification system can classify EEG signals of epileptic seizure with low calculation complexity, good timeliness and high accuracy.

The invention discloses a six-degree-of-freedom ultralow frequency vibration isolation device based on a zero stiffness system and a control system thereof, and relates to the fields of multi-degree-of-freedom low frequency, ultralow frequency and even zero-frequency vibration isolation and micro vibration simulation experiments. The six-degree-of-freedom ultralow frequency vibration isolation device based on the zero stiffness system is formed by connecting a supporting platform, a basic platform and a vibration isolation module. The vibration isolation module comprises an upper connecting rod, a lower connecting rod, a columnar shell, a vertical spring, a horizontal spring and a connecting assembly between the springs. The upper end of the upper connecting rod is connected with the supporting platform. The lower end of the upper connecting rod penetrates through an upper end cover of the columnar shell and is connected with the connecting assembly between the springs. The upper end of the lower connecting rod is fixedly connected with a lower end cover of the columnar shell. The lower end of the lower connecting rod is connected with the basic platform. The vibration isolation module meets the characteristic of zero stiffness. In combination with a gravity environment self-adapting control system, an isolated object is in a suspended state. Low frequency, ultralow frequency and even zero-frequency full-band vibration immune and isolation performance can be realized, and the problem of the multicoupling technique of multi-degree-of-freedom ultralow frequency, vibration resonance and non-linear dynamics is solved.

The invention discloses an electronic throttle valve control method based on a self-adaption sliding mode technology. The electronic throttle valve control method comprises the steps that the pedal angle theta<d> and the current throttle valve output angle theta<t> are collected in real time, and the system error e is worked out; the optimal control voltage u of a throttle valve is worked out through a self-adaption robust sliding mode control algorithm, and the duty ratio T set by a motor driver is obtained by conversion according to the formula T=u / 12; and the driver drives the throttle valve according to the duty ratio T obtained from an electronic control unit, and the output throttle valve output angle theta<t1> is obtained. According to the electronic throttle valve control method based on the self-adaption sliding mode technology, the defect that parameters of the electronic throttle valve are uncertain in the prior art is overcome, the problems that the nonlinear characteristic of an electronic throttle valve control system is increased due to existing of gear clearance torque, and the control precision is low are solved, and quick and accurate control over the throttle valve of nonlinear kinetics is achieved.

The invention discloses a nonlinear kinetic equation solving method and system for mechanical nonlinear vibration analysis. The method comprises the following steps: constructing and obtaining a nonlinear finite element kinetic equation in a system time domain according to a to-be-analyzed mechanical nonlinear vibration system; obtaining a nonlinear finite element kinetic equation in a frequency domain through a multi-harmonic balance method; solving a Jacobian matrix of a nonlinear finite element kinetic equation in a frequency domain by utilizing an automatic differential method; solving thenonlinear finite element kinetic equation in the frequency domain by adopting an arc length method to obtain a solution of the nonlinear finite element kinetic equation in the frequency domain; according to the solution of the nonlinear finite element kinetic equation in the frequency domain, the solution of the nonlinear finite element kinetic equation in the time domain is obtained through generalized inverse Fourier transform, then a frequency response curve is obtained, and mechanical nonlinear vibration analysis is carried out.

The invention discloses a non-uniform quantization-based uncertain multi-agent system consistency control method. The method comprises the following steps: establishing a non-linear kinetic model with quantization input, unmodeled dynamics and dynamic disturbance for each agent in a multi-agent system; decoupling a coupling relationship between unmodeled items and state items in unmodeled dynamicand dynamic disturbance items by utilizing an input-state index stability principle to separate independent unmodeled dynamic items and state items; decomposing a quantized input item into an algebraic expression of the input item with a weight coefficient; performing controller design on a simplified intelligent agent model by combining an adaptive neural network method and a backstepping technology; providing consistency convergence and stability conditions by using a Lyapunov stability theory; and giving a control parameter selection rule according to convergence and stability conditions so as to achieve a consistency quantitative control effect. According to the method, the communication burden of a networked control system is reduced, and a good robustness control effect is achieved.

The invention relates to a complex learning based non-minimum phase aircraft neural network control method, a longitudinal channel model of an aircraft is firstly decomposed into a speed subsystem anda height subsystem, PID is used to control the speed subsystem, and a back-stepping method is used to control the height subsystem. As for the height subsystem, the unstable inner dynamic state is changed to the asymptotic stable inner dynamic state on the basis of outputting redefinition, the inner dynamic state of the system outputting redefinition is subjected to coordinate transformation, anda pitch angel instruction used for designing a controller is derived; the unknown nonlinear kinetics of the system is estimated through the neural network, a model error signal is designed, a complexlearning algorithm is given in combination with a tracking error, and the approximation performance of the nonlinear kinetics of the neural network is improved. The inner dynamic state is stabilizedby outputting redefinition, the uncertain kinetics of the aircraft is estimated on the basis of the complex learning neural network, and a novel concept is provided for controlling the non-minimum phase aircraft.

The invention discloses an electroencephalogram abnormal signal detection device and method. The device comprises an electroencephalogram signal preprocessing unit which is used for obtaining an original electroencephalogram signal and carrying out the denoising of the original electroencephalogram signal, and obtaining a target electroencephalogram signal, a wavelet decomposition and reconstruction unit which is used for acquiring the target electroencephalogram signal, and performing X-layer decomposition by adopting Daubechies wavelets according to the coverage frequency of the abnormal waveform and the sampling frequency of the electroencephalogram detection equipment to obtain X-layer frequency bands and characteristic components of each frequency band, a nonlinear kinetic parameter estimation unit which is used for calculating sample entropy characteristics of the electroencephalogram signals of each frequency band after wavelet decomposition, a normalization unit which is used for carrying out normalization processing on the feature components and the sample entropy features to obtain feature vectors, and a detection and classification unit which is used for detecting and classifying the feature vectors. According to the method, features after wavelet transform and features of nonlinear dynamics are combined, comprehensive consideration is carried out, and classificationdetection is carried out on final waveforms.

A control system for controlling an operation of a system with continuous- time nonlinear dynamics subject to constraints including equality and inequality constraints on state and control variables of the system, including an estimator to estimate a current state of the system using measurements of the operation of the system and a controller to iteratively solve, at each control time step, an approximation of a constrained nonlinear optimization problem to produce a control solution, wherein the approximation includes a linearization of the nonlinear dynamics of the system discretized by time intervals in the control horizon and represented using an approximation of the constraint Jacobian matrix for each time interval of the control horizon. The iterative solution procedure is based ona block- wise update formula for the approximation of the constraint Jacobian matrix and the intermediate condensing matrices using an evaluation of one or combination of the discretized dynamics of the system and at least one directional derivative of the discretized dynamics of the system. Each block in the constraint Jacobian matrix and in the intermediate condensing matrices represents one time interval in the prediction horizon and can be updated independently, based on a block- wise rank-one update formula without any iterative solution procedure and without any matrix-matrix multiplications or matrix factorizations.

The invention provides a method for judging the peeling position of a rolling bearing. Firstly, a nonlinear kinetic model of a spalling fault bearing is established; obtaining a simulated frequency domain vibration curve of the spalling fault bearing; then acquiring a vibration signal on a bearing with a fault or a suspected fault; extracting an actual measurement frequency domain vibration curve,then calculating fault characteristic frequencies of an outer ring and an inner ring of a bearing with a fault or a suspected fault, comparing the fault characteristic frequencies with the actual measurement frequency domain vibration curve by taking the simulation frequency domain vibration curve as a reference, determining whether the bearing to be measured is a fault bearing or not, and determining a specific fault position. According to the invention, the problem of inaccurate actual fault simulation of a fault nonlinear kinetic model is avoided; meanwhile, pulse modulation in the processthat the rolling body passes through the fault position of the inner ring and the outer ring during operation is considered, the operation state of the bearing during early faults can be simulated more accurately, the detection efficiency and precision are effectively improved, the equipment maintenance cost is reduced, and the production efficiency is improved.

The invention provides a new mechanical elasticity-based energy storage chaos phenomenon controlling and eliminating method. A nonlinear dynamical model of a permanent magnet motor type mechanical elastic energy storage (MEES) unit is deduced, the unit does chaotic motion under certain parameters and the running condition in the energy storage process, known and unknown parameters of a system are controlled, and the chaos phenomenon of the permanent magnet motor type MEES can be eliminated by a simplest controller.

The invention discloses a quasi-static and non-linear kinetic analysis method for the uncertainty of a viscoelastic dielectric elastomer based on an interval method. In the method, a viscoelastic kinetic model of the dielectric elastomer is provided, uncertainty of material parameters, external load and voltage is considered, and creep analysis, relaxation analysis and kinetic response analysis are carried out on the dielectric elastomer with interval uncertainty parameters by introducing an interval perturbation method and a first-order Taylor expansion method. The effectiveness of the interval method provided by the invention is verified by a Monte Carlo simulation method, and the uncertainty prediction method can be used in the design of an active control system taking a dielectric elastomer as a driver or a sensor in the future.

The invention discloses an optimal design method for a heavy-load and unbalanced-load gear transmission system based on motion stability. The method comprises the steps: extracting various internal and external excitation factors which affect the stability and accuracy of gear transmission; the influence of a time-varying center distance caused by factors such as deformation of a transmission shaft and a supporting part due to heavy load on a meshing line and a gear swing effect caused by unbalance loading are mainly introduced, and a bending-torsion-swing coupling nonlinear kinetic equation of a gear transmission system is established and solved; and a parameter interval of stable movement of the system is extracted by means of a nonlinear dynamics theory, so that the system avoids complex nonlinear dynamics behaviors, and the purposes of guiding gear machining design and parameter optimization are achieved.

The invention discloses a cyanobacterial bloom predicting method based on a non-linear kinetic time-series model, which belongs to the water environment predicting field. The method comprises the following steps: using the blue algae growth rate as a time-varying parameter; creating a non-linear kinetic time-series model for the cyanobacteria growth with double nutrient salt cycling; using the combination of the numerical algorithm and the intelligent evolutionary algorithm, optimizing the constant parameters in the non-linear kinetic time-series model for the cyanobacteria growth; through setting the multivariate time-series model to realize the prediction of the time-varying parameter and the cyanobacteria biomass and through using the bifurcation theory and the central manifold theory to make a nonlinear kinetic analysis of the cyanobacteria growth time-varying system, obtaining the outbreak conditions for a cyanobacteria bloom so as to make an early warning of the bloom outbreak. The method proposed by the invention not only determines the conditions for a cyanobacteria bloom outbreak, but also improves the bloom prediction accuracy, providing an effective reference for an environmental protection department and governance decisions for water environment treatment.

A dynamic coupling time varying failure rate analysis method of a on-orbit space flexible gear mechanism comprises the seven steps that firstly, according to the fact that a shaft in the on-orbit space flexible gear mechanism is an elastic body, a kinetic equation of the flexible shaft is built, and the finite element method is adopted for analysis; secondly, a gear kinetic equation is built; thirdly, a nonlinear kinetics equation of coupling of the flexible shaft and a gear in the on-orbit space flexible gear mechanism is built; fourthly, transmission stimulation analysis is performed on the space flexible gear mechanism; fifthly, the Newmark algorithm is adopted to calculate a dynamic load coefficient; sixthly, a gear bending stress reliability limit state function is built in the on-orbit space flexible gear mechanism; seventhly, dynamic reliability and failure rate analysis are performed on the space flexible gear mechanism. The method solves the problem that reliability calculating accuracy is low because of nonlinear factors of a traditional transfer matrix method and mode superposition method, and has engineering practical value for improving reliability of the on-orbit space flexible gear mechanism and other spacecrafts. The method is also suitable for other flexible gear mechanisms.

The invention relates to a method for controlling a workpiece vibration system in the milling process of a thin-walled workpiece through bifurcation and chaotic analysis. The method comprises the steps that firstly, a nonlinear kinetic equation of a rectangular thin-walled workpiece under the thermal coupling effect in the milling process is established; then, the condition that a nonlinear vibration system generates Smale horseshoe chaos is revealed through a Melnikov function; meanwhile, according to the bifurcation and chaotic analysis method, the influence rule of the change of the load, the temperature, the technological parameters and the like on the nonlinear dynamic behavior of the system in the thin-wall workpiece machining process is analyzed, and the thin-wall workpiece vibration control method optimized through the machining parameters is finally established. According to the method, by using the bifurcation and chaotic analysis method, the influence rule of the change of the load, the temperature, the technological parameters and the like on a workpiece vibration system in a milling process is analyzed based on an established kinetic equation of the workpiece vibration system in the machining process. Processing parameters can be scientifically selected by using a chaos and bifurcation analysis vibration control method, so that the processing quality can be effectively improved.

The invention discloses a magnetic flux linkage optimization method for a permanent magnet synchronous motor (PMSM) based on nonlinear kinetic analysis. The method comprises firstly establishing a mathematical model of the PMSM; converting the mathematical model of the PMSM by using an affine transformation method; performing simulation to determine the magnetic flux linkage range of the PMSM, wherein since the mathematical model of the PMSM is stable at an equilibrium point after simulation, the magnetic flux linkage range of the PMSM can be obtained; then obtaining the Lyapunov exponential spectrum of the simulated mathematical model of the PMSM by using matlab simulation software so as to further reduce the magnetic flux linkage range of the PMSM; and finally, establishing the simulatedmathematical model of the PMSM, selecting at least five magnetic flux linkage values for simulation, and obtaining the optimal magnetic flux linkage value of the PMSM when the magnetic flux density is close to saturation. The method can obtain the magnetic flux linkage value at the optimum working point when designing a motor and reduces the design cost.

The invention discloses a nonlinear dynamic modeling method for a harmonic reducer transmission system, and aims to overcome the defect that internal interaction of harmonic gear transmission cannot be reflected as a harmonic gear transmission system is subjected to dynamic modeling processing in a 'black box' mode in a dynamic model of a harmonic gear transmission system in the prior art. According to a harmonic gear transmission principle, a comprehensive dynamic model is established by considering the interaction of all parts in a harmonic gear. The method comprises the following steps: determining physical parameters and operation parameters of the harmonic gear transmission system; simplifying a transmission model of the system according to the harmonic gear transmission principle; calculating meshing rigidity; obtaining a nonlinear dynamic differential equation of the system by adopting the Newton second law, and performing the nonlinear dynamic solution on the system. According to the dynamic model established by applying the method, the internal effect of each part is comprehensively considered, and the precision of the model is ensured.

The invention relates to the field of experimental devices, in particular to a double-mass gap nonlinear dynamics experimental device. The double-plastid gap nonlinear dynamics experiment device comprises an inner plastid, an outer plastid, a shearing motion assembly, a gap nonlinear assembly, a sensor assembly and a supporting assembly. The inner plastid penetrates through the outer plastid, and the inner plastid and the outer plastid are connected through the shearing motion assembly; the inner plastid is connected with one side of the gap nonlinear assembly, and the outer plastid is connected with the other side of the gap nonlinear assembly; the sensor assembly is used for measuring the acceleration time history of the inner plastid and the outer plastid in the horizontal and vertical directions; and the supporting assembly is connected with the inner plastid. The double-plastid gap nonlinear dynamic experiment device provided by the invention can conveniently and effectively perform a double-plastid gap nonlinear dynamic experiment, and can be used for performing experimental verification on an existing nonlinear dynamic theory.

The invention discloses a method for classifying ECG vector diagrams based on nonlinear dynamic characteristics, belonging to the technical field of ECG detection; the method comprises the following steps: collecting Frank three-lead ECG vector diagram signals; using median filtering to remove noise ; extract 10 nonlinear dynamic features of each lead respectively; carry out normalization processing on each feature, carry out feature fusion, and use the training vector diagram mode and the test electrocardiogram mode in the nonlinear dynamics The differences in indicators can realize the classification of normal ECG vector diagram and abnormal ECG vector diagram. The above method applies the nonlinear dynamic analysis method to the classification of ECG vector diagram for the first time. The extracted nonlinear dynamic features can represent the dynamic properties of ECG vector diagram, and better excavate the intrinsic characteristics of ECG vector diagram. Abnormal and normal vector electrocardiograms are distinguished and suitable for use in routine electrocardiographic examinations.

The invention provides a gas-liquid two-phase flow pattern nonlinear kinetic analysis method based on generalized composite multi-scale entropy. The method comprises the following steps: firstly, carrying out an air-water-gas-liquid two-phase flow experiment, and collecting pressure difference fluctuation time sequences of three flow patterns; then performing generalized coarse graining processing according to the flow type differential pressure fluctuation time sequence, calculating entropy values of all coarse graining time sequences of different scale factors, averaging the entropy values under the same scale factor, obtaining generalized composite multi-scale entropy under different scales, and drawing an entropy value change trend chart of the generalized composite multi-scale entropy; and finally, according to the generalized composite multi-scale entropy change trend charts of different flow patterns, calculating the generalized composite multi-scale entropy growth rate, and obtaining generalized composite multi-scale entropy growth rate distribution of the three flow pattern pressure difference fluctuation time sequences, thereby realizing representation and identification of the flow patterns. The generalized composite multi-scale entropy in the invention can map the complexity of the gas-liquid two-phase flow, and has the characteristics of strong robustness, simple calculation and easy realization.