194 results about "Linear system" patented technology

The invention relates to the technical field of wireless communication, in particular to a wireless communication system signal sending and receiving method based on an AI model. The system comprises a delay coordinate embedding unit, a neural flow tracking unit, a continuous evolution unit, a Riemann decoding unit and a closed-loop control unit. The system maps the pilot frequency to a high-dimensional phase space by using a Takens principle, and reconstructs a channel dynamic topology; the core of the method is that a manifold derivative network is utilized to calculate a flow field vector, continuous time deduction is carried out through a Shenchang differential equation, evolution deviation is eliminated in combination with a geometric projection correction term, and thus the future channel state is accurately predicted. Calculating a CSI matrix based on the prediction result to optimize beam transmission; according to the method, the limitation of single-variable observation is broken through, and reconstruction and perception from discrete data to a full view of a high-dimensional nonlinear system are realized.

The invention discloses a dynamic load identification method and device based on a physical guidance convolution long-short-term memory network, and relates to the field of data processing, and the method comprises the steps: constructing a dynamic load identification model based on the convolution long-short-term memory network, and training the dynamic load identification model, and obtaining a trained dynamic load identification model; loss functions used in the training process of the dynamic load recognition model comprise a data driving loss function and a physical guidance loss function, the physical guidance loss function is constructed based on a kinetic equation converted from a motion equation of a linear system under the action of power, and an overall transfer matrix is used; acquiring the acceleration and / or strain response time domain signal of each test point acquired in each time window when the to-be-identified dynamic load is applied to one or more action points on the structure, and inputting the acceleration and / or strain response time domain signal into the trained dynamic load identification model to obtain the predicted dynamic load corresponding to the one or more action points. The problem that current load identification is high in dependence on data is solved.

The invention discloses a transfer learning-based aero-engine full envelope transition state optimization method, which comprises the following steps of: training a deep reinforcement learning agent in a source task environment to obtain an optimal transition state control strategy under the working condition, and applying the strategy of the source task agent to a target task environment in a parameter transfer manner, and further training and optimizing the strategy in the target task environment until the optimal control strategy of the target working condition is obtained. According to the method, autonomous optimization and expansion of the control strategy can be realized according to different operation conditions of a complex nonlinear system, and the method is of great significance to intelligent control of the propulsion aero-engine and improvement of the operation performance and reliability of the engine.

The invention relates to the technical field of flow metering, in particular to a temperature return control method of an intelligent control valve. The method comprises the following steps: receiving a temperature return control strategy sent by a user and a set value of specified temperature return, and judging an initial control parameter set according to the temperature return control strategy; real-time operation data are collected from the temperature sensing module and the flowmeter according to the sampling period, the return water temperature and flow at the current moment are obtained, and the return temperature deviation is calculated; calculating a control target value of the intelligent control valve by utilizing a model fuzzy logic predictive control algorithm based on the temperature return deviation; and the valve opening degree is adjusted based on the control target value, and the control parameters are dynamically corrected through the temperature return control strategy for self-adaptive temperature return control. According to the method, through a composite algorithm of fusing fuzzy logic control and model predictive control (MPC), on the basis that preliminary control signals are generated through fuzzy reasoning, a nonlinear system model is combined to carry out multi-step prediction on the future return water temperature change trend.

The invention discloses a safety control method of a high-order chain type multi-agent system under spoofing attack, and belongs to the technical field of safety control of multi-agent systems. The method comprises the following steps: establishing a dynamic model of a high-order chain type multi-agent system; constructing an extreme attack scene in which all communication links are subjected to bounded spoofing attacks; based on a backstepping control method, a distributed fixed time security control protocol is designed for a system, and the protocol counteracts attack influence by designing a virtual control strategy and a Lyapunov function; a Lyapunov stability theory is utilized to prove that under the action of the protocol, a system tracking error can be converged to zero within fixed time, so that safe and consistent tracking is realized. The defect that the prior art mainly aims at a linear system or only can realize asymptotic convergence is overcome, the method is particularly suitable for scenes such as non-complete constraint wheeled mobile robots, and the robustness and the rapid recovery capability of the system under extreme spoofing attacks are effectively improved.

The invention provides an intelligent modeling method and system for a self-adaptive regulation and control water-turbine generator set. Multi-source data in the operation process of a water-turbine generator set speed regulation system are collected, a bidirectional sparse attention residual network BiKAN is used for conducting preliminary prediction on the operation state of the system, and deviation signals are formed by the predicted data and actually-measured data. A local sparse variational mode decomposition LSVMD method is introduced to carry out multi-scale mode decomposition on prediction errors, an online sparse gated cycle unit network OSBiGRU is constructed, and dynamic capture and online correction of non-stationary errors are realized by taking decomposition errors and multi-source state features as input. A hierarchical progressive adaptive regulation and control mechanism is constructed, different regulation and control levels are dynamically divided according to a unified error evaluation criterion, and corresponding model updating strategies are driven. And dynamic optimal adjustment of model structure parameters is realized by using a triangular topology aggregation algorithm in which a Newton Raphson search rule strategy is introduced. Compared with the prior art, the modeling precision and the prediction stability of a complex nonlinear system are remarkably improved.

The invention relates to a belt weigher weighing precision compensation method based on multi-sensor data fusion. According to the method, an original weighing signal and an electromagnetic interference signal of the belt weigher in a running state and a baseline noise signal in a no-load state are synchronously acquired, a feature vector is extracted based on the electromagnetic interference signal, and an intermodulation noise component in the weighing signal is predicted by using a nonlinear system identification model in combination with the baseline noise signal. Then, the noise component is subtracted from the original weighing signal through an adaptive cancellation algorithm to obtain a local compensation weighing signal, and finally, a final weighing signal is generated based on a plurality of local compensation weighing signals through a signal-to-noise ratio weighted fusion algorithm, so that intermodulation noise caused by electromagnetic interference generated by the variable frequency driver is effectively suppressed. The weighing precision and the anti-interference capability of the belt weigher are improved.

The invention discloses a multi-spacecraft attitude and orbit coupling all-drive convergence time controller generation method, which is applied to the technical field of aerospace and comprises the following steps: establishing a multi-spacecraft attitude and orbit coupling error system all-drive model based on all-drive characteristics of a tracking error model; wherein the multi-spacecraft attitude and orbit coupling error system all-drive model is a pseudo-linear system model aiming at the stability problem of a multi-spacecraft attitude and orbit coupling error system; aiming at the multi-spacecraft attitude and orbit coupling error system all-drive model, constructing an all-drive convergence time controller based on an all-drive system method, a sliding mode control method and a preset time control method, and adjusting the convergence time of the system based on the all-drive convergence time controller; the all-drive convergence time controller is a controller designed based on a preset time parameter and an adjustable time parameter. According to the method for adjusting the preset time through attitude and orbit coupling, the multiple spacecrafts can maneuver to the expected attitude and orbit within the preset time, formation is completed, and the formation efficiency is improved.

The invention discloses a PID (Proportion Integration Differentiation) tracking control method based on error perception nonlinear gain, which is used for high-precision tracking control of an MIMO (Multiple Input Multiple Output) nonlinear system. Comprising the following steps: 1) constructing an adaptive gain by using current error information, historical error information and prediction error information; 2) realizing intrinsic integral saturation resistance through nonlinear integral design; and 3) complete autonomous operation can be realized without accurate system dynamics priori knowledge or manual adjustment. According to the method, the nonlinear compensation capability and the robust adaptive performance are enhanced while the structural simplicity of the PID controller is kept. Through rigorous Lyapunov analysis, the stability of the method to a non-linear system in a general form is verified, and the method shows excellent performance in both adjustment and time-varying reference tracking scenes. The method can be widely applied to the fields of linear motor servo systems, sea surface ship control, unmanned vehicle dynamics control and the like needing high-precision tracking control.

The invention discloses a heavy-load mechanical arm obstacle function adaptive sliding mode control method and device based on a disturbance observer, and the method comprises the steps: building a dynamic model which considers the model error, an unmodeled part, friction and lumped disturbance caused by an external load; converting a lumped disturbance estimation problem into a linear system state estimation problem of defined auxiliary state variables and unknown input driving, and designing a sliding mode disturbance observer used for estimating and compensating lumped disturbance; for a disturbance estimation error, a bounded adaptive control gain of a controller is designed by adopting a barrier function, and the error is kept within a preset range while control jitter is avoided. Through systematic control architecture design, the key problem that high-precision tracking and smoothness control of the excavator in a complex working environment are difficult to consider at the same time is effectively solved.

The embodiment of the invention provides a robot driving control method and device, electronic equipment and a storage medium, and belongs to the technical field of nonlinear system control. The method comprises the following steps: constructing a state prediction model according to a first observation state sequence, a second observation state sequence and a historical control input sequence, and calling the state prediction model to perform state error prediction based on the first observation state sequence, the second observation state sequence and the historical control input sequence, performing parameter adjustment on the state prediction model according to the predicted observation state error sequence and a preset dimension reduced-order matrix to obtain an error compensation state space model; based on the first given state data and the first control input data of the current time, an error compensation state space model is called to conduct state prediction, second given state data corresponding to the to-be-predicted time is obtained, and the target robot is driven and controlled according to the second given state data. According to the embodiment of the invention, the driving control accuracy of the robot can be improved.

The present application relates to a kind of based on position dynamics adaptive anisotropic cloth simulation method, system, electronic equipment and storage medium, belong to computer graphics physics simulation field.The method includes setting cloth grid;Topology relationship of grid is calculated, and the set of point edge surface is formed;According to Newton's law, the linear system of external force is constructed, and change coordinate is solved using conjugate gradient method;Using graph coloring method, data dependence is decoupled to grid, using the method of position dynamics, strain constraint iteration parallel computing is carried out to change coordinate, and the coordinate correction amount after cloth motion deformation is obtained;According to the revised coordinate, grid tensor field is calculated, grid dynamic refinement and coarsening are carried out, and non-uniform anisotropic cloth grid is formed.The present application uses position dynamics method to calculate dynamic non-uniform grid, simulates the anisotropy of cloth deformation, retains the dynamic behavior of cloth wrinkle under fine scale, is quickly solved on the basis of not losing simulation details, satisfies real-time.

The application discloses a macro modeling method for ensuring direct current precision, and the method comprises the following steps: reading n-port network parameters; initializing sampling points for constructing a macro model; initializing the order of the macro model; initializing the pole of the macro model; establishing a weighted linear system according to the current order, the sampling points and the pole; solving a linear equation set, judging convergence according to an error, and obtaining the macro model; and outputting the optimized macro model by applying a residue matrix disturbance to optimize the macro model. The macro modeling method for ensuring direct current precision provided by the application controls the direct current precision in two stages. In the first stage, a weighted least square method is adopted on the basis of a linear system established by a traditional vector fitting algorithm to preliminarily control the direct current precision. In the second stage, a small disturbance is applied to the residue matrix of the macro model, so that the disturbed macro model has accurate direct current characteristics.

The invention relates to a model predictive control algorithm applied to a control valve to adjust chamber pressure, and relates to the technical field of semiconductor manufacturing equipment.The model predictive control algorithm comprises a modeling module configured to abstract a chamber pressure dynamic process into a three-order linear system model; the control algorithm module is configured to establish a state space expression based on the three-order model, and adopts a model prediction control method to perform rolling optimization on a future valve opening sequence by taking a minimum pressure set point tracking error and a valve action penalty as objective functions in a prediction time domain; the pressure sensor is configured to combine real-time pressure sensor feedback. According to the invention, the opening degree of the vacuum control valve is dynamically optimized to realize high-precision adjustment of the pressure of the chamber.

The invention discloses a rigid-flexible coupling system modeling and self-adaptive control method based on data driving, and belongs to the field of space tethered robots. The method comprises the following steps: constructing a physical simulation model of the space tethered robot, and collecting state variables and control input data of system operation to construct a training data set; on the basis of the Kupman operator theory, constructing a mapping model from a nonlinear system to a dimension raising linear system with a deep neural network as an embedded function, and training the mapping model on the basis of the training data set; designing a linear quadratic optimal controller; under a reinforcement learning framework, a strategy optimization target taking controller parameters and model parameters as optimization objects is constructed, and collaborative adaptive adjustment of the model parameters and the controller parameters is realized through iterative updating. According to the invention, the optimal control of the space tethered robot is realized.

The invention relates to a mark point hole filling method, device and equipment. The method comprises the steps of obtaining boundary point data of a target hole in the surface of a three-dimensional model, wherein the target hole is a hole formed by scanning mark points; filling the target hole according to the boundary point data of the target hole, and determining a triangular mesh and / or a triangular filling vertex used for filling; according to the triangular mesh and / or the triangular filling vertex, updating the initial topological structure of the three-dimensional model to obtain the topological structure of the updated mesh data corresponding to the three-dimensional model; and if the target hole is filled by using the triangular filling vertex, optimizing and adjusting the position of the triangular filling vertex in the topological structure of the updated grid data by using a Laplace operator linear system so as to obtain the grid data of the three-dimensional model after hole filling. By adopting the method, the hole filling effect of the marked points can be achieved.

The application discloses a kind of direct identification method of low-order linear system order of different real pole, comprising the following steps: initialization to be measured system to zero state, constant excitation is applied to the input end of to-be-measured system and simultaneously acquires the response output signal of to-be-measured system, calculates sample adjacent different elements, discriminant factor is calculated using sample adjacent different elements, calculate first discriminant element, secondary discriminant element, tertiary discriminant element and its average value and standard deviation, identify, eliminate singular discriminant element and update, the order N of to-be-measured system is analyzed out;The application gives a solution to the key technical pre-problem in the control and operation level for different real pole low-order linear system, and builds a core technical foundation for subsequent realization of safe and controllable, efficient and low-consumption and high-performance stable operation.

The invention discloses an off-line estimation method for an automobile active noise reduction nonlinear transfer function based on RNN (Recurrent Neural Network). Specific used models are LSTM (Long Short Term Memory) and GRU (Generalized Range Unit). For a specific vehicle model, offline acquisition and preprocessing of data, model training and deployment of the model to a specific audio DSP are carried out, a traditional vehicle active noise reduction algorithm (LMS, APA, LS and the like) assumes an acoustic path as a linear system (MA / ARMA model) which cannot adapt to the nonlinear characteristics of an actual acoustic path, and two RNN models of LSTM / GRU are adopted to carry out nonlinear transfer function estimation, so that the nonlinear characteristics of the acoustic path cannot be adapted to the nonlinear characteristics of the actual acoustic path. The nonlinear features of the acoustic path can be accurately represented, the in-vehicle noise suppression effect is effectively improved, and the acoustic experience of a driver and passengers is improved.

The invention discloses an aircraft shape high-precision aerodynamic design method and device based on a quantum discrete adiabatic algorithm, and relates to the technical field of quantum computing. The method comprises the following steps: constructing a linear system model according to target aerodynamic data based on a computational fluid mechanics method; based on the cyclic shift basis matrix, according to the sparse coefficient matrix and the source item vector, performing block coding by using a linear combination unitary operator technology to obtain a block coding unitary operator; based on a preset iteration operator, constructing a walking operator according to the block coding unitary operator; according to the walking operator, line preparation is carried out through a cyclic calling method, and a quantum solving line is obtained; executing a quantum solving line, and measuring flow field quantum state information; and based on the target aerodynamic data, according to the flow field quantum state information, carrying out high-precision aerodynamic optimization design on the shape of the aircraft by utilizing an aerodynamic integration method. The high-precision aerodynamic design method for the aircraft profile is efficient and accurate based on the quantum discrete adiabatic algorithm.

The invention discloses a noise enhancement nonlinear system joint detection and estimation method under a Bayesian framework, and belongs to the field of signal processing. Firstly, independent additive noise is added to a nonlinear system input signal, and noise-corrected nonlinear system output is obtained after the signal passes through a nonlinear system. And secondly, under the Bayesian criterion, judging which hypothesis in the binary hypotheses is established by utilizing the output of a nonlinear system of noise correction, and estimating unknown parameters in the signal of which the judgment result is H1. On the premise that the detection performance is not reduced, a noise enhancement nonlinear system joint detection and estimation model which minimizes the estimation risk is constructed. The additive noise is the optimal solution of the model and is random distribution formed by not more than two constant vectors. According to the method, noise enhancement and nonlinear system joint detection and estimation under the Bayesian framework are combined, and the Bayesian estimation risk is further reduced under the condition that the Bayesian detection cost is not increased.

This invention discloses a dynamic load identification method and apparatus based on a physically guided convolutional long short-term memory network, relating to the field of data processing. The method includes: constructing and training a dynamic load identification model based on a convolutional long short-term memory network to obtain a trained dynamic load identification model; the loss functions used in the training process of the dynamic load identification model include a data-driven loss function and a physically guided loss function. The physically guided loss function is constructed based on the dynamic equations transformed from the motion equations of a linear system under dynamic action, and uses an overall transfer matrix; acquiring the time-domain signals of acceleration and / or strain responses of each test point collected within each time window when the dynamic load to be identified is applied to one or more points on the structure, and inputting these signals into the trained dynamic load identification model to obtain the predicted dynamic load corresponding to one or more points. This invention solves the problem of the current load identification method's strong dependence on data.

The invention discloses a predefined time sliding mode control method based on an improved cascade non-linear extended state observer, so as to realize higher-performance and more reliable control of an uncertain non-linear system, and the method comprises the following steps: 1, providing an inequality based on PTC based on PTC basic definition, and aiding the design of a PTC controller with an adjustable characteristic; 2, aiming at the inequality proposed in the step 1, designing an adjustable PTSMC in combination with a sliding mode control algorithm; and step 3, for non-matching disturbance widely existing in a non-linear system, providing an improved non-linear extended state observer, and compensating an adjustable PTSMC algorithm. The invention provides a novel PTC (Positive Temperature Coefficient)-based inequality and auxiliary controller design. And meanwhile, the designed controller has a better convergence speed. An adjustable predefined time sliding mode controller is designed based on an inequality, and the control performance of the system is effectively improved. The design of ICNLESO can observe and compensate system disturbance in real time, and the anti-interference performance of the system is enhanced.

This invention discloses an efficient direct method for identifying the order of a low-order linear system containing complex poles, comprising the following steps: initializing the system under test to a zero state, applying a constant excitation to the system under test, and simultaneously acquiring the response output signal V of the system under test. (out) (t), calculate the second-order discriminant Ψ (2) (i) and its average value μ (2) With standard deviation σ (2) Calculate the third-order discriminant element Ψ (3) (i) and its average value μ (3) With standard deviation σ (3) Identify, remove outlier discriminants and update μ (2) μ (3) σ (2) σ (3) The order N of the system under test is analyzed. ord This invention addresses a key technical challenge in the control and operation of low-order linear systems with complex poles, laying a solid foundation for achieving safe, controllable, efficient, low-consumption, and high-performance stable operation.

The present invention discloses a method for identifying nonlinear parameters of structural clearances. First, a mathematical model of the structural clearance nonlinear system is established; then, the frequency response function of the system under sinusoidal sweep excitation is obtained through the input-output response of the system; in the frequency response function, a pair of symmetric displacement points exist at any displacement response amplitude to define the same displacement frequency response function. By defining the real and imaginary parts of the displacement frequency response function through the symmetric displacement FRF points at each given displacement response amplitude, the natural frequency of the system related to the displacement response amplitude can be obtained; then, the expression of the nonlinear equivalent stiffness of the system is extracted through the simulation analysis of the model, and the equivalent stiffness curve of the system is further obtained through the model simulation data; finally, based on the particle swarm algorithm, the clearance characteristic parameters are obtained by curve fitting according to the equivalent stiffness expression obtained by integration and the equivalent stiffness curve obtained by model simulation; the present invention can identify the clearance nonlinear parameters more quickly and accurately globally.

The application discloses a method for estimating unknown state and noise of a nonlinear system, which comprises the following steps: constructing a general nonlinear system model with measurement noise and sampling output; constructing an extended nonlinear system through filtering transformation; and proposing an extended non-fragile observer to estimate unknown state and noise of the constructed extended nonlinear system. The proposed non-fragile observer is verified, and it is proved that the non-fragile observer can still track unknown state of the nonlinear system under the conditions of noise and gain disturbance. The proposed observer can still well track unknown state of the extended system.

The application provides a kind of nonlinear system fault diagnosis method based on sliding mode observer and DCNN, it is related to fault diagnosis technical field.The method first constructs the mathematical model of nonlinear system, the mathematical model considers concurrent situation of system actuator and sensor fault and nonlinear term and external interference, and sliding mode observer is designed for nonlinear system;Then define the residual error of sliding mode observer, establish residual error equation;Determine the residual error threshold of sliding mode observer;Nonlinear system is monitored using sliding mode observer, residual error information is obtained and residual error information is preprocessed based on residual error threshold to establish residual error signal data set;Finally, the residual error signal generated by sliding mode observer is diagnosed in depth using deep learning model DCNN, and the fault diagnosis of nonlinear system is realized.

The application discloses a Pick iterative random characteristic flow field deduction and reconstruction method and system for bridge wind engineering and belongs to the technical field of bridge wind engineering and structural health monitoring. The application divides a global space-time calculation domain, constructs local affine mapping and a space-time random characteristic base function, adopts Pick iterative linearization incompressible Navier-Stokes equations, avoids Jacobian matrix calculation, assembles a global over-determined linear equation group containing momentum, mass conservation, boundary and interface constraints, forms a double-enhanced linear system through algebraic row balance and physical enhancement layered weighting, realizes high-fidelity reconstruction of a flow field through least square solution, supports unknown fluid parameter alternating inversion under sparse data, and can automatically generate bridge wind-induced vibration early warning. The application eliminates edge device memory bottleneck, improves solution robustness and precision, strictly guarantees physical conservation, and is suitable for real-time deduction of bridge complex flow fields, sparse observation reconstruction and safety monitoring.

The application provides a lithium battery SOC estimation method based on extended Kalman filtering, comprising the following steps: constructing an equivalent circuit model of a lithium battery; establishing a discrete nonlinear system state and an observation equation according to the equivalent circuit model; obtaining a linear system state and an observation equation required by an extended Kalman filtering algorithm according to the discrete nonlinear system state and the observation equation; updating a prediction and correction part of the extended Kalman filtering algorithm; judging whether the updating process of the prediction and correction part of the extended Kalman filtering algorithm is completed; if yes, reconstructing the linear system state and the observation equation required by the extended Kalman filtering algorithm; and estimating the SOC of the lithium battery according to the reconstructed linear system state and the observation equation required by the extended Kalman filtering algorithm. The application can simultaneously consider the convergence speed of the SOC estimation value of the lithium battery in the initial estimation stage and the smoothness of the SOC estimation value waveform of the lithium battery in the stable estimation stage after converging to the allowable error range.

The invention discloses a double-closed-loop active disturbance rejection control system and method for a flexible joint mechanical arm. In order to solve the problems of endpoint jitter, poor anti-interference capability and the like of a flexible joint mechanical arm under traditional PID control, a double-closed-loop active-disturbance-rejection controller composed of a tracking differentiator, a nonlinear state error feedback law and an expansion state observer is designed by establishing a mathematical model of a permanent magnet synchronous motor and a flexible joint; wherein the expansion state observer can estimate and compensate the total disturbance inside and outside the system in real time, and linearizes a complex nonlinear system, so that the endpoint jitter of the mechanical arm is effectively inhibited, and the trajectory tracking precision, the dynamic response speed and the robustness to parameter change and external disturbance of the system are remarkably improved; the control performance of the flexible joint mechanical arm is verified through Simulink simulation, and an effective solution is provided for high-precision control of the flexible joint mechanical arm.

The application provides a power system transient stability evaluation method and a power system operation trajectory prediction method based on a Koopman operator, the power system transient stability evaluation method comprising the following steps: S1, collecting operation trajectory data of state variables of a power system; S2, using an EDMD method to increase dimensions of the collected state variables to a high-dimensional linear space to approximate an action of the Koopman operator by using a limited-dimensional linear matrix K ; S3, using spectral decomposition of the Koopman operator to locate discrete spectra of stable equilibrium points of the power system, and using characteristics of eigenvalues of the Koopman operator and inverse mapping of dictionary functions to find stable equilibrium points of the power system in an original state space; and S4, based on the obtained stable equilibrium points and analysis of a stable mechanism of a linear system, constructing a power system transient stability criterion, and using the transient stability criterion to judge whether the system is transiently stable after the power system suffers from a large disturbance and collects an arbitrary working point.