53 results about "Nonlinear model" patented technology

The application discloses a kind of charging equipment health state evaluation method and its device, electronic equipment, it is related to new energy technology field, wherein, the evaluation method includes: the actual parameter value of the component parameter of target charging equipment is obtained, based on actual parameter value, using preset nonlinear model determines the benchmark value and increment value of different component parameters, for different components under each circuit, based on benchmark value and increment value, determine the health value of each different component, based on the health value of each different component, determine the health state of target charging equipment.The application solves the technical problem that the accuracy of related art for evaluating the health state of charging equipment is low.

The application discloses a multi-power unit high-speed train control method, and relates to the technical field of train control.The method comprises the following steps: performing stress analysis on a multi-power unit high-speed train to determine a high-speed train dynamics model; determining a discrete-time nonlinear model based on the high-speed train dynamics model; constructing control force and control force change rate constraints; converting the discrete-time nonlinear model to obtain a dynamic linearization data model; designing a parameter estimation algorithm and an adaptive extended state observer; designing a discrete-time terminal sliding mode function and a hyperbolic curve type reaching law; determining a data-driven terminal sliding mode control scheme based on the dynamic linearization data model, the parameter estimation algorithm, the adaptive extended state observer, the discrete-time terminal sliding mode function and the hyperbolic curve type reaching law; and performing tracking control operation on the multi-power unit high-speed train by using the data-driven terminal sliding mode control scheme.The application improves the control precision of high-speed train operation while reducing the sliding mode chattering.

The application discloses a kind of based on multi-dimensional structure coupling and phase change determination digital content asset evaluation method and system.The method comprises: obtaining multidimensional state data;System state characteristics are constructed;Phase change determination is carried out based on state variable derivative change;When detecting phase change, value transition analysis is carried out;Output value evaluation result.Wherein, the system state characteristics are mapped by multivariate function containing structure coupling degree function, the phase change determination is realized by comparing first derivative or second derivative of state variable with preset threshold, and the value transition analysis is realized by nonlinear model containing piecewise function or exponential function.The application identifies the transition process of digital content from exchange value to structural value, solves the technical problems that the value evaluation dimension is single and the dynamic adaptability is poor in the prior art, and can be widely applied in AI content evaluation, digital asset pricing, public opinion analysis and other fields.

This application belongs to the field of unmanned surface vessel (USV) control technology, specifically relating to a method and apparatus for predictive control of USV trajectory tracking using dynamic reference linearization. The method includes: establishing and discretizing a nonlinear dynamic model of the USV to construct a predictive model; constructing an online nonlinear optimization problem including terminal stability components based on this model; generating a dynamic reference trajectory in each control cycle based on the optimal solution of the previous moment, and linearizing the nonlinear model point-by-point along this trajectory to obtain a high-fidelity linear time-varying predictive model; further transforming the original nonlinear programming problem into an efficiently solvable quadratic programming problem; and outputting control quantities in real time by solving this problem and integrating reference commands. This application ensures the theoretical stability of the closed-loop system, achieves a synergy of high precision, high real-time performance, and high reliability, and effectively improves the trajectory tracking performance of USVs in complex marine missions.

A source-grid-load-storage flexibility multi-time scale optimization scheduling method considering conditional value at risk, comprising: constructing a power system supply-demand flexibility quantification model, including: a power system flexibility demand quantification model, a power system flexibility supply quantification model, and a power system flexibility supply-demand balance model; constructing a multi-time scale optimization scheduling model, including: a day-ahead optimization scheduling model, an intra-day optimization scheduling model, and a real-time optimization scheduling model; linearizing the CVaR nonlinear model; solving the linearized model, and performing simulation calculation under different strategies and different scenarios. The scheduling method has important theoretical value and engineering practical significance for accurately quantifying and controlling system operation risk, fully tapping the potential of system flexibility, and improving the economy, safety and operation efficiency of high-proportion new energy power systems.

The application discloses a kind of sand belt abrasion prediction method and system of fusion acoustic signal and acoustic emission signal.Method includes: installing acoustic signal sensor and acoustic emission sensor and collecting corresponding signal, time domain analysis and frequency domain analysis are carried out to two kinds of signals, and the time domain characteristics and frequency domain characteristics of signal are respectively extracted;Using LSTM neural network fusion the time-frequency domain characteristics of the two kinds of signals, establish the nonlinear model of sand belt abrasion in the process of complex blade robot grinding;Using state estimation technique in probability statistics method, predict the probability of sand belt stable abrasion and severe abrasion occurring at next time;According to the abrasion probability, feedback adjustment and control the process parameters in grinding process, prevent tool excessive abrasion from causing blade to appear burn and other quality decline phenomenon.The abrasion state of sand belt in the process of processing is monitored in real time and accurately, timely feedback regulation, prevent complex blade from being damaged due to severe abrasion of sand belt in the process of robot processing.

This invention discloses a method, apparatus, system, and storage medium for simulating modulation TOF camera correction. First, a light intensity threshold is calculated as the depth error classification standard. The light intensity threshold with the largest standard deviation of the depth error is selected as the depth error oscillation critical point. A light intensity-distance lookup table is established based on the light intensity-distance dataset to correct high-intensity depth data. A light intensity-depth random forest nonlinear model is established, and the hyperparameters of the model are optimized using a particle swarm optimization algorithm for low-intensity depth data. Next, a material correction classification model is constructed. For light scattering errors caused by materials in the measurement scene, the LDA algorithm is used to extract effective features and establish a material-depth distortion dataset. Finally, during real-time acquisition, the trained material correction classification model is used to classify the materials of the acquired objects. Based on the material classification results, the depth data is corrected using the material-depth distortion dataset to obtain a high-quality depth image.

The application provides a fixed-wing unmanned aerial vehicle cruise control method based on NMPC and INDI cascade control, and comprises the following steps: constructing a cascade hierarchical control architecture composed of an outer loop NMPC controller and an inner loop INDI controller; the outer loop NMPC controller generates an optimal control sequence containing thrust instructions, pitch angle instructions and roll angle instructions based on a simplified unmanned aerial vehicle nonlinear model, in combination with a preset target function and flight constraint conditions; the inner loop INDI controller takes the pitch angle instructions and the roll angle instructions as inputs, and calculates rudder deflection instructions in combination with unmanned aerial vehicle state information fed back by real-time sensors; and the unmanned aerial vehicle cruise track is tracked and controlled according to the thrust instructions and the rudder deflection instructions. The application combines the predictive optimization of NMPC with the fast robustness of INDI, forms a collaborative mechanism of'slow optimization + fast response', solves the problem of heavy calculation burden of single NMPC, makes up for the defect of lack of foresight of INDI, and significantly improves the cruise control performance.

The application relates to a fuel cell air supply system control method based on a fusion model prediction, which comprises the following steps: step S1: constructing a high-order nonlinear model of a fuel cell stack and an air supply system, and simplifying the high-order nonlinear model into a six-order model; step S2: linearizing the six-order model by using a Taylor expansion mode to obtain a linearized model; step S3: bringing state parameter data of A and B power points into the linearized model, and converting the linearized model into a form of a state space equation; step S4: designing a controller, and giving constraints and reference outputs to the controller; and step S5: realizing output coupling of two controllers, and designing a compensation amount for the coupled control amount to compensate for the control amount when a current step drops. The application can realize effective control of cathode air supply of a fuel cell.

The application provides a multi-substance density calculation method based on X-ray energy spectrum information, S1, multi-energy spectrum projection data collection: single X-ray exposure is performed on a measured object by using a photon counting detector to obtain projection values of each pixel in each energy interval; S2, multi-substance multi-energy spectrum decomposition model establishment: a nonlinear relationship model is established for multiple substances to be decomposed; S3, substance area density solving: a target function is constructed by the difference between the actual projection values obtained in step S1 and the model calculation values in step S2, and a numerical optimization algorithm is used to solve the target function; S4, quantitative image generation: all pixels are traversed to generate the area density distribution map of each substance. The application obtains multi-energy spectrum information by using a photon counting detector, combines a nonlinear model and an optimization algorithm, realizes synchronous quantitative calculation of the densities of multiple substances, overcomes the defects of limited substance type differentiation and insufficient energy information utilization, and improves the accuracy of substance identification.

This invention discloses a cooperative antagonistic control method for a rope-driven parallel antagonistic bionic joint, comprising: acquiring the system control torque and joint pose of the parallel mechanism bionic joint; grouping the acquired data to construct a dataset, and dividing the dataset into a training set and a validation set; simplifying the system dynamics model into a second-order nonlinear model, using the system control torque, joint pose, and their first derivatives as inputs to a neural network, and using the neural network to estimate the nonlinear mapping function and inertia matrix of the second-order nonlinear model to obtain the system model; using the validation set to verify the rationality of the training results; establishing error variables based on the established system model, designing a fast nonsingular terminal sliding surface, and discretizing the system model; designing the cost function of the model predictive controller; calculating the analytical solution of the control law of the cooperative controller based on the cost function of the model predictive controller; and designing an antagonistic compliant switching control strategy.

The embodiment of the application relates to the technical field of flight control, and discloses a wide-range aircraft attitude control method, device and equipment, which comprises the following steps: constructing a dynamic model of a wide-range aircraft based on a nonlinear model state quantity and a control input; obtaining a linear variable parameter model of the wide-range aircraft based on the dynamic model of the wide-range aircraft by adopting a linear variable parameter vector, the linear variable parameter vector comprising a Mach number, a flight height, an angle of attack and a sideslip angle; constructing a self-disturbance rejection controller of a roll channel, a pitch channel and a yaw channel based on the linear variable parameter model of the wide-range aircraft for flight attitude control; and performing attitude control on a target wide-range aircraft according to the self-disturbance rejection controller of the roll channel, the pitch channel and the yaw channel. The self-disturbance rejection controller is used for responding to local uncertainty in a mode, so that high-precision stable control of the wide-range aircraft is realized.

The application provides a tire half-part composite extrusion control method, a system and a storage medium, and belongs to the field of tire industrial tread manufacturing equipment control. The method comprises the following steps: step 1, collecting composite extrusion production data and preprocessing to obtain a data set; step 2, obtaining a prediction model by using the data set, and establishing a linear regression model of the total width of the tread and the tread weight per meter; step 3, constructing a model predictive controller by using the prediction model and the linear regression model; step 4, a production debugging stage; and step 5, a stable production stage. The application uses a nonlinear model and a model predictive control to establish a targeted control model, solves the problem of the influence of multivariable parameters in the tread composite extrusion production process on the quality of the extruded semi-finished product, improves the stability, accuracy and intelligent degree of the tread composite extrusion system, and realizes an efficient, high-quality intelligent semi-finished tread production process.

The application provides a simulation method and device for harmonic detection of cable joint interface pressure and a medium. The method comprises the following steps: modeling a simulation geometric model of a cable intermediate joint, and setting an ultrasonic excitation source; modeling through a nonlinear spring damper to obtain a cable joint pressure model; simulating nonlinear ultrasonic detection of the cable joint interface pressure, and obtaining a frequency spectrum diagram at each point probe. Through modeling of the nonlinear spring damper on the cable joint model, the nonlinear interaction at the interface of the cable intermediate joint is simulated, the change of the harmonic characteristics when the interface pressure changes is deeply explored by using the nonlinear harmonic detection principle, the relationship between the two is established, the state of the cable intermediate joint under various interface pressures is accurately simulated, the pressure at the interface of the cable intermediate joint is fully considered, and simulation analysis is performed on the cable joint interface pressure detection by establishing a new interface nonlinear model.

The invention discloses a Bluetooth sound equipment intelligent sound effect adjusting system based on adaptive noise reduction, and relates to the technical field of audio processing, and the system obtains an environment noise signal and an original audio signal, calculates an initial anti-phase sound wave signal based on the environment noise signal, and obtains an initial anti-phase sound wave signal based on a parameterized large-signal nonlinear model; the bottom layer electrical feedback parameter is converted into the transient physical displacement state of the current loudspeaker diaphragm, and the parameterized large signal nonlinear model takes the force factor, the mechanical stiffness coefficient and the voice coil inductance of the loudspeaker changing along with the displacement as physical constraints to generate a theoretical composite excitation signal; inputting the theoretical composite excitation signal and the transient physical displacement state into a loudspeaker nonlinear prediction network to obtain a pre-judgment result, when the pre-judgment result is that a physical limit threshold value is broken through, constructing a reverse distortion compensation waveform, and superposing the reverse distortion compensation waveform into a mixed signal of an attenuated initial reverse sound wave signal and an original audio signal to obtain the loudspeaker nonlinear prediction network. And outputting the final audio signal.

This invention relates to an optimization scheduling method and system for an electric-hydrogen coupling system based on a multi-stage hybrid search algorithm. The method employs a high-precision nonlinear model and specifically designs a two-stage scheme that divides the scheduling solution into a multi-step variable neighborhood search algorithm and an adaptive simulated annealing algorithm. This satisfies the computational efficiency requirements of the solution, while leveraging the ability of adaptive perturbation, global optimization, and escaping local optima. It generates a high-precision optimal solution while breaking through local optima.

The application discloses a soft manipulator preset time sliding mode tracking control method and system based on PSO-BP, belongs to the soft robot control technical field, and relates to the following technical scheme: a nonlinear model of a soft manipulator is established, a controlled object model is obtained by rewriting the soft manipulator, and robust right coprime factorization processing is performed to obtain an equivalent control object; a tracking error is constructed, an error integral term is introduced into a sliding mode surface, and an integral sliding mode surface is established; a compound sliding mode control law is designed based on the integral sliding mode surface; a plurality of control gains corresponding to system total convergence time is obtained through simulation; a sample data set of the control gain and the total convergence time is constructed; the sample data set is trained to establish an inverse mapping model of the expected convergence time to the control gain; a target convergence time is inputted to obtain a corresponding control gain, which is substituted into the compound sliding mode control law to generate a driving air pressure control amount. The application has the beneficial effects that a soft manipulator preset time sliding mode tracking control method based on PSO-BP is provided.

A braking pressure control method for new energy vehicles based on the Koopman operator includes: establishing the dynamic equilibrium equations and nonlinear model of the braking system; constructing an observation function set, identifying the Koopman operator matrix using an extended dynamic mode decomposition algorithm, constructing a global linear prediction model, and correcting the Koopman operator matrix; constructing an augmented state equation in the discrete-time domain, designing a linear extended state observer based on an improved Kalman filter algorithm, and outputting a lumped disturbance estimate; injecting the lumped disturbance estimate into the prediction equation of the linear model predictive control, and obtaining the optimal control sequence by solving the objective function of a quadratic programming problem; converting the first control increment of the optimal control sequence into a motor drive signal, which is applied to the master cylinder motor of the electro-hydraulic braking system for physical pressure build-up. This invention can solve the problems of low hydraulic pressure control accuracy, poor anti-interference ability, and high computational load in existing technologies.

The application relates to the technical field of prefabrication plants, and discloses a production optimization method based on a digital twin production line model, which comprises the following steps: establishing initial models of all processes, simulating the production line by adopting Monte Carlo sampling, discovering capacity and quality risks, putting forward production suggestions and management points, tracking worker and equipment production data in real time during the operation of the production process, updating model parameters, updating the model into a twin model, simulating by Monte Carlo sampling, predicting capacity and quality risks, putting forward a production optimization scheme, continuously updating model parameters and the twin model in the subsequent production process, and finally solidifying into a twin model highly matched with the existing production line. The existing production line twin technology focuses on data acquisition and display, a linear model is adopted for the prediction of capacity and quality, and the variation of samples is not considered enough. The application provides a nonlinear model and key parameters, and the matching of the production line is realized through dynamic updating, which is of great significance to actual production.

The application belongs to the technical field of fault detection and analysis in control system, and discloses a kind of hot reactor fault detection method based on polytope. Through the discretization analysis of the nonlinear model of hot reactor, the system dynamics under the condition of hot reactor failure is described by T-S fuzzy 2-D model. To solve the problem of unknown antecedent variables, a T-S fuzzy state observer is designed to calculate the estimated value of the antecedent variables using state estimation. On this basis, a T-S fuzzy fault detector is designed to generate a residual signal. Using the estimated-based method, a fault detection mechanism based on polytope is established to determine whether a fault has occurred according to the residual signal. Finally, the feasibility and effectiveness of the application are verified through simulation. The application first designs a T-S fuzzy 2-D model fault detector for hot reactor, and conducts fault detection based on polytope under the condition of unknown antecedent variables.

The application belongs to the technical field of DC-DC converter modeling, and discloses a modeling system and use method for starting of a closed-loop DC-DC converter, comprising a multi-parameter sensing module, an enhanced state space modeling module, a multi-algorithm fusion solving module, a multi-topology adaptation module and a model iteration optimization module. A three-order nonlinear model is constructed by taking the control input (duty ratio) as a new state variable, the multi-algorithm fusion solving module is used to improve the transient prediction accuracy and anti-interference ability, the enhanced state space modeling module is used to correct the model deviation, and the model iteration optimization module is used to support fast switching of multiple topologies.

The application provides a robust multi-fault synchronous reconstruction method for an uncertain quad-rotor unmanned aerial vehicle under external disturbance, and relates to the technical field of quad-rotor unmanned aerial vehicles. First, a dynamic model of the quad-rotor unmanned aerial vehicle is established, and is converted into a T-S fuzzy state space equation to construct an augmented system containing system states, process faults and sensor faults; for the obtained augmented system, a controllable order fuzzy observer is designed, and an error system is derived; the parameter matrix of the controllable order fuzzy observer is solved, and the stability of the error system is analyzed; based on the controllable order fuzzy observer, synchronous reconstruction of multi-faults of the quad-rotor unmanned aerial vehicle is realized. The nonlinear model of the quad-rotor unmanned aerial vehicle is approximated through T-S fuzzy technology, a controllable order fuzzy observer is designed, synchronous accurate reconstruction of multiple faults, effective suppression of external interference and complete decoupling of unknown inputs are realized, and the order of the observer can be flexibly adjusted according to actual requirements.

A vacuum isothermal forging method and system. The method comprises: on the basis of material characteristics of a forging, selecting mechanical property evaluation indexes and forging process parameters of the forging, and constructing a mathematical model between the mechanical property evaluation indexes and the forging process parameters; with the forging process parameters as optimization variables, the mechanical property evaluation indexes as optimization objectives and the mathematical model as an objective function model, using a preset algorithm to perform solving for a Pareto optimal solution set, so as to obtain optimal forging process parameters; measuring a surface flatness coefficient of the current forging to be forged, with the current measured value and the optimal forging process parameters as input variables and control parameters of a vacuum isothermal forging control system as output variables, using a preset neural network to establish a nonlinear model, and performing calculation by means of a genetic algorithm to obtain optimal output variables of the nonlinear model; and on the basis of the current output variables, the vacuum isothermal forging control system performing operation control. The method can effectively improve the quality of a forging.

The application relates to the wireless power transmission (WPT) technical field and particularly discloses a WPT system control method based on nonlinear model reduction, which comprises the following steps: firstly, a complex full-order mathematical model of the WPT system is constructed; then, the complex full-order mathematical model is reduced by using an LPV-Hammerstein model to obtain an LPV-Hammerstein model of the WPT system; next, different order reduced models are generated according to the LPV-Hammerstein model of the WPT system, and system identification is performed on the reduced models to obtain identification parameters; then, corresponding low-order models are established according to the identification parameters; finally, one of the low-order models with the highest fitting degree with the complex full-order mathematical model is taken as the optimal low-order model of the WPT system. The application reduces the high-order complex full-order mathematical model of nonlinear output by using the LPV-Hammerstein model, and obtains the parameters most matched with the original model through system identification. Without obtaining all circuit element parameters, a simple and accurate model can be obtained, and the dynamic characteristics of the system can be accurately described by using a first-order or second-order model.

This disclosure provides a method and system for safe control of unmanned vehicles based on derivative-aware reinforcement learning, relating to the field of vehicle safety control technology. The method includes: establishing a three-degree-of-freedom disturbed vehicle horizontal plane dynamic model and a DoS attack model based on the dynamic characteristics of the unmanned vehicle, and describing the vehicle horizontal plane dynamic model as a disturbed second-order nonlinear model; designing a finite-time switching state observer and a safe backstepping controller based on the disturbed second-order nonlinear model; inputting the parameter errors and external disturbances observed by the switching state observer to the safe backstepping controller; integrating a derivative-aware Actor-Critic reinforcement learning enhancement channel and a steady-state priority scheduler on the basis of safe robust backstepping control; enhancing the online optimization of transient tracking performance through reinforcement learning during the intervals of the DoS attack model; and reducing the reinforcement learning authority in the steady-state region through the steady-state priority scheduler to protect the steady-state error accuracy of the safe main control. This disclosure protects the steady-state error accuracy of the safe main control.

The application provides an adaptive sliding mode control method for chaos suppression of a power system, and belongs to the technical field of nonlinear stability control of the power system. The method is based on synchronous transmission technology, and comprises the following steps: a target system is set according to a demand for selecting an output signal for an established n-dimensional nonlinear model of the power system, n >= 2; a first sliding mode surface is constructed according to a state error between the power system and the target system, and then a general sliding mode surface set suitable for a system of any dimension is constructed through recursive combination; a sliding mode controller is designed based on the general sliding mode surface, and an adaptive identification mechanism for uncertain parameters of the system is integrated, so that the state of the power system accurately tracks the signal of the target system, and thus the chaos oscillation is effectively suppressed. The method realizes universal control of power system models of different dimensions, significantly improves the engineering applicability and deployment flexibility of the method, and ensures rapid stabilization and stable operation of the system under various chaotic conditions.

The application belongs to a kind of inspection robot operation control method, for the low degree of autonomy of existing overhead transmission line inspection robot, poor operation positioning accuracy and control stability in disturbance environment such as wind disturbance and line vibration, and lack of efficient approach and soft operation ability for loose damper bolt, provide a kind of transmission line inspection robot anti-interference servo control operation method and related device, through bolt pose acquisition and operation arm rough positioning, realize the end tool fast approach;Again based on bolt point feature deviation, combined with disturbance observer and adaptive nonlinear model predictive control to complete anti-interference visual servo alignment;After alignment, along the Z axis linear approach of end tool coordinate system, and compensate the small pose deviation through flexible device, cooperate with torque feedback control screwing end, so as to improve the degree of automation, alignment stability and screwing reliability of bolt operation in disturbance environment.