174 results about "Non linear model" patented technology

The invention relates to the technical field of flour processing, and discloses an intelligent control method and system for a flour workshop, and the method comprises the following steps: collecting raw material characteristic data and equipment operation state data in real time through a sensor, calculating the state variable of the current system according to the collected data, and calculating the state variable of the current system according to the current state variable. Performing rolling optimization on a control strategy in a future time domain, dynamically adjusting grinding parameters according to a rolling optimization result, correcting a prediction model according to a data deviation between real-time acquired data and nonlinear model prediction data, and calculating an optimal control parameter combination based on a stochastic optimization theory; the system comprises a sensing module, a control module, an execution module, a feedback correction module and a random optimization module. By adopting the technical scheme that a high-precision sensor is combined with intelligent control, comprehensive automation and accurate control of the flour production process are achieved. The stability and consistency of flour quality are remarkably improved, and quality fluctuation is avoided.

The invention provides an intelligent control method and system based on electro-hydraulic linkage. The method comprises the steps that multi-mode environment parameters are collected, a digital twin environment model is constructed, and data are synchronized with an electro-hydraulic servo system in real time. And establishing a nonlinear model, identifying parameters by using a least square method and a recursive least square method, verifying the precision through a step response test, and updating. And constructing an adaptive LSTM neural network inverse model to output a control voltage signal. And the signal is subjected to weighted fusion with the output of a PID controller, and the PID controller dynamically adjusts a gain parameter according to an error integral term to generate a control instruction to drive the system. According to the invention, the LSTM inverse model provides feed-forward compensation, the PID controller realizes feedback correction, and the LSTM inverse model and the PID controller are complementary. Meanwhile, based on a related nonlinear model and a parameter self-adaptive updating mechanism, the influence of working condition change is accurately described, and high-precision control of the electro-hydraulic servo system under the complex working condition is achieved.

The invention relates to the technical field of power system testing, in particular to a transformer simulation test current optimization method based on dynamic impedance matching, which comprises the following steps: performing low-power scanning on a tested transformer to establish a nonlinear model capable of predicting harmonic waves; actively synthesizing the virtual impedance of the target power grid; and a feed-forward compensation current is generated by using a harmonic prediction model and is injected into the transformer after being superposed with a fundamental wave instruction. According to the method, accurate identification modeling is carried out on nonlinear characteristics of a tested unit, active synthesis of virtual impedance of a target power grid is combined, and harmonic compensation current is generated in real time by utilizing a feedforward control thought so as to optimize finally injected test current. According to the method, the problems of inaccurate power grid simulation and insufficient nonlinear response coupling of the transformer in a traditional test are solved, high-fidelity current injection is realized, real electrical behaviors of the transformer under various complex power grid working conditions can be accurately simulated, and the flexibility, accuracy and safety of the test are remarkably improved.

The invention relates to the technical field of non-electrical variable control, and discloses an industrial water chiller temperature intelligent adjusting system, which comprises a refrigeration circulation loop and a control unit, the control unit calculates a steady state control component based on the water outlet temperature deviation, performs differential processing on the refrigerant pressure to extract the gradient, and outputs the gradient. Generating a transient compensation component when the gradient exceeds a dynamic noise threshold; according to the method, the problem of thermal lag is solved by utilizing the pressure wave transmission speed advantage, aggressive response is prevented from triggering low-pressure shutdown through a safety margin constraint mechanism, the pressure safety margin is calculated in real time, a gain coefficient is determined according to a nonlinear model, and a final driving instruction is generated. And the dynamic balance of the control response speed and the system physical security boundary is realized.

The invention relates to an electric vehicle wireless charging system LPV-Hammerstein model identification method based on an alternating least square iteration strategy, and belongs to the technical field of power electronic system modeling and parameter identification. The method comprises the following steps: firstly, constructing an LPV-Hammerstein model structure based on a WPT system circuit topology and a working mechanism, and then collecting discrete data of a system input control signal U, an output DC voltage Vo and a scheduling variable Req; then, alternately fixing other parameters by adopting an alternate least square iterative algorithm, and converting a to-be-estimated parameter problem into a linear least square sub-problem to be solved; and when a convergence condition is satisfied, a final LPV-Hammerstein model parameter estimation result is obtained. According to the method, the common nonlinear and parameter change coupling characteristics in the wireless charging system of the electric vehicle can be effectively processed, a systematized and efficient way is provided for obtaining a high-precision system dynamic model, and the limitation that the system cannot be accurately described by a traditional linear model or a simple nonlinear model is overcome.

The invention discloses an efficient self-cleaning method for a heat exchanger of an air conditioner, relates to the technical field of efficient air conditioners, and designs the steps of multi-source data acquisition and residual dirt identification, residual cumulative effect prediction and formula optimization decision, directional deep cleaning, dynamic parameter control and the like according to the self-cleaning requirements of the heat exchanger of the air conditioner. Accurate positioning and grading of stubborn dirt are realized by utilizing data of multiple types of sensors; on the basis of machine learning or a nonlinear model, predicting an accumulative effect of residual dirt in subsequent operation, dynamically generating a cleaning formula and optimizing cleaning parameters; strengthening treatment is carried out on key parts through mechanical vibration, high-pressure ultrasound and real-time feedback control; and performing secondary detection on the cleaning effect, comparing the cleaning effect with a predicted value, and if the deviation is obvious, correcting the weight of the model to form a closed-loop self-learning lifting strategy. Damage to materials and energy waste caused by excessive cleaning can be effectively reduced, and long-term efficient operation of the heat exchanger can be kept in various complex scenes.

The application discloses a kind of atmospheric environment ozone concentration prediction system based on chemical reaction mechanism, including obtaining weather forecast parameter, historical observation data cleaning analysis, analysis screening suitable model, model training and prediction, prediction result analysis, data visualization and so on functional module composition.This system is based on Lagrangian coordinate system, establishes the response relationship between meteorological parameter, pollutant concentration and ozone concentration, using thermodynamic equilibrium principle, chemical reaction equilibrium principle and photochemical reaction principle and other basic chemical reaction engineering theory, deduces the non-linear model of meteorological parameter, pollutant concentration and other parameters on ozone concentration influence, by the independent design of each software function module, with low energy consumption, low carbon emission, low operating cost, data automatic processing, easy to deploy, can be connected ecological cloud platform, system expansibility is strong, maintenance is easy, and computing efficiency is high.Therefore, it has broad market application prospect and good social and economic benefits.

The invention provides an underwater-water surface unmanned system cooperative control method based on NMPC, belongs to the technical field of robot control, and particularly provides an underwater terrain prediction method based on sonar information feedback by using multi-beam sonar data, and obtains underwater terrain coordinate data by fusing a robot state predicted by a non-linear model in NMPC. And according to the predicted underwater terrain information, an underwater terrain following control problem is converted into an optimization problem with constraints by using NMPC, and terrain following control of the underwater robot can be realized by solving the optimization problem. On the basis, a task-triggered underwater-water surface unmanned system cooperative control strategy is constructed, and the tracking of the underwater robot by the water surface unmanned ship is realized. And finally, the cooperative control problem of the underwater-water surface unmanned system for unknown sea area exploration is successfully solved.

Aspects relate to signaling relating to a non-linearity model for power amplifier circuitry of a transmitting device. The power amplifier circuitry may apply digital pre-distortion (DPD) to a signal prior to amplification and transmission of the signal. A receiving device may apply digital post-distortion (DPoD) to a signal received from the transmitting device where the DPoD is based on the non-linearity model. The transmitting device may send to the receiving device non-linearity parameters for the non-linearity model.

The invention discloses a ship control method and device in an inland river scene, electronic equipment and a storage medium, and can be applied to the technical field of intelligent navigation of ships. According to the method, the ship traveling wave energy distribution rule is calculated according to the first state information and the second state information which are acquired in real time, then the first following distance is determined, and then path planning is performed by combining the ship energy consumption, the ship traveling wave energy distribution rule, the safety margin and the total navigation time which are acquired in real time to obtain the first navigation path of the target ship; a first sailing control instruction of the target ship is predicted by adopting a nonlinear model control method so as to control the sailing process of the target ship in the inland river scene, and meanwhile, the sailing process of the target ship in the inland river scene is controlled by a second sailing control instruction generated by dynamically adjusting the first sailing path according to the channel environment change information. Therefore, the balance between the energy efficiency and the sailing efficiency can be realized in the whole sailing process of the target ship, and emergencies in the sailing process can be effectively coped with.

The invention relates to a multi-target control method for a fuel cell hydrogen supply system based on switching type model prediction. The method comprises the following steps: S1, constructing a high-order nonlinear model of the fuel cell hydrogen supply system; s2, obtaining a high-load linear sub-model and a low-load linear sub-model; s3, converting the high-load linear sub-model and the low-load linear sub-model into a high-load model prediction controller and a low-load model prediction controller respectively; and S4, acquiring the actual load current of the electric pile, judging the adopted model prediction controller based on a switching strategy judged by a load current threshold, outputting a control variable by the adopted model prediction controller, controlling the fuel cell hydrogen supply system based on the control variable, and repeating the step S4 until the control is finished. Compared with the prior art, the hydrogen supply system has the advantages that the combined precise adjustment of the anode pressure and the hydrogen passing ratio is realized, so that the dynamic response performance of the hydrogen supply system and the overall operation stability of the system are improved, and the like.

The invention is suitable for the technical field of viscosity monitoring and control, and provides a viscosity monitoring and control system for the blockage risk of a cyclized rubber solution conveying pipeline, the viscosity monitoring and control system comprises a tank body for storing a cyclized rubber solution, a hydraulic pump and a conveying pipeline, a liquid inlet of the hydraulic pump is communicated with the tank body, and a liquid outlet of the hydraulic pump is communicated with the conveying pipeline; a stirring device for stirring the cyclized rubber solution is arranged in the tank body, and the flow channel state evaluation module is used for constructing a flow channel state evaluation model according to the cross sectional area of the conveying pipeline, the density of residual solidified colloid on the inner wall of the conveying pipeline and the sealing performance of a connector of the conveying pipeline and outputting a flow channel state coefficient. According to the method, the pipeline blockage risk degree can be dynamically evaluated in real time, the influences of different inducements such as hydraulic pump load abnormity, pipeline structure degradation and solution fluidity reduction are accurately distinguished, and quantitative output of the risk level is achieved through the nonlinear model.

The invention discloses an adhesion type ducted flight operation robot and a control method thereof, and relates to the field of flight operation robot control. The operation task planning module identifies an operation task target according to a ground station instruction, and plans an expected task trajectory of an operation mechanism in a three-dimensional space according to the position of the operation task target; the tail end track integrated control module determines the position of a flight platform, a yaw angle reference value and all joint angle reference values of a mechanical arm through a nonlinear model. The flight platform pose control module estimates and compensates the coupling effect of the operation mechanism acting on the flight platform in real time, and the operation mechanism pose control module compensates the nonlinear coupling effect between the joints of the mechanical arm and between the mechanical arm and the flight platform in real time; and the tail end physical contact control module performs admittance control in an adhesion performance space of the operation mechanism according to a ground station instruction after the operation mechanism reaches a task target position. The invention aims to improve the stability control precision of the robot.

The invention belongs to the field of wind power, and discloses a floating wind turbine generator thrust constraint robust adaptive pitch control method, which comprises the following steps: establishing a floating wind turbine generator nonlinear model considering coupling between rotor rotation dynamics and platform pitching motion; setting a control target based on comprehensive consideration of power adjustment, platform stability and limit load constraint; designing a self-adaptive propeller pitch control method based on a fast self-adaptive law; and designing a thrust envelope protection system based on thrust limit detection and a self-adaptive thrust avoidance strategy, and integrating the thrust envelope protection system into the self-adaptive pitch control method, so that the wind turbine generator can operate within an allowable thrust range. The self-adaptive propeller pitch control method can achieve efficient power adjustment and platform pitching motion suppression at the same time, is superior to a traditional method in the aspects of transient response and robustness, and is particularly suitable for the severe marine environment.

A method includes determining a portion of a flight path of an aerial vehicle. The method also includes determining an attribute value representing an operating condition expected to be experienced by the aerial vehicle at the portion of the flight path. The method additionally includes determining, based on the attribute value and using a non-linear model, a power value representing an amount of power expected to be consumed by the aerial vehicle in connection with the portion of the flight path. The method further includes determining, based on the power value, an energy value representing an amount of energy expected to be consumed by the aerial vehicle in connection with the portion of the flight path. The method yet further includes determining the flight path based on the energy value.

The invention discloses a cross arm and insulator installation robot motor control method based on a T-S fuzzy model, and the method comprises the following steps: building a permanent magnet synchronous motor nonlinear model according to electromagnetism and motor dynamics; based on a sector nonlinear method, establishing a permanent magnet synchronous motor T-S fuzzy model; designing an observer to obtain a load torque estimated value; designing an L-infinity robust controller to ensure that the angular velocity of the motor can track a reference instruction; and an anti-control gain matrix solving criterion is formulated, and it is ensured that the system meets performance indexes. According to the method, the nonlinear control problem of the permanent magnet synchronous motor system is effectively solved, the influence of continuous interference signals on the system can be effectively suppressed, and the robustness of the system is enhanced.

The invention provides an anti-interference control method for the concentration of a main reactant in a continuous stirring reaction kettle process, belongs to the technical field of chemical process control, and solves the problem that in the chemical process of producing cyclopentanol by catalyzing electrophilic water with cyclopentadienoic acid, the concentration of the main reactant is not influenced. The technical problem that the concentration of a main reactant is difficult to track a set value without steady-state errors under the conditions of fluctuation of feeding concentration, uncertainty and external disturbance of the continuous stirring reaction kettle is solved. According to the technical scheme, the method comprises the steps of constructing a model pre-estimation compensator and an anti-interference control structure, designing a four-dimensional decision model, carrying out parameter collaborative optimization by adopting a multi-target genetic optimization algorithm, and carrying out optimal solution selection based on a TOPSIS method. Under the condition of feed concentration fluctuation, the dynamic response speed and the anti-interference capability of the system can be remarkably improved, and faster adjustment time, smaller overshoot and stronger robustness can be realized in linear and nonlinear models.

The application provides a photovoltaic power station photovoltaic power prediction method and system. Among them, a cloud layer movement time sequence library is constructed based on historical cloud charts, and a nonlinear model of cloud speed and output attenuation is established. The light sensor array monitors the cloud layer thickness and aerosol density in real time, combines the model to calculate the shadow area light intensity attenuation gradient, and predicts the remaining time of the cloud group reaching the core area. According to the remaining time, the power prediction confidence interval is divided, the satellite data is input into the multi-scale meteorological fusion architecture, and the meteorological feature extraction path of the attention mechanism is adjusted based on the cloud density. The ground reflectivity correction value calculated by fusing the light sensor scattering data is used to generate a short-term power prediction sequence of the sudden cloud layer scene. The sequence is input into the energy management system, combined with the reactive power compensation parameters of the grid-connected point voltage deviation and the grid rules, and the dual-objective scheduling instructions of active and reactive power are output. The technical scheme provided by the application improves the cooperative scheduling accuracy of the photovoltaic power station under the sudden change of the cloud layer.

The invention belongs to the technical field of virtual oscillator control of power electronic inverters, and particularly discloses an AHO virtual oscillation controller stabilization method and an adaptive inertia method. The stabilization method comprises the following steps: establishing a nonlinear model of a GFM inverter in a single-inverter grid-connected and multi-inverter interconnection mode; acquiring a balance point by using a numerical analysis method, linearizing the balance point, extracting a linearized Jacobian matrix, and further designing a multi-target fitness function taking eigenvalue real part minimization as a core; and in combination with a particle swarm intelligent search algorithm, parameters influencing the stability in the AHO virtual oscillation controller are optimized, and the dynamic stability of the power system under the wide working condition is ensured. The AHO-VOC controller using the particle swarm optimization method can ensure stability under switching of various working conditions; compared with inertia-free and fixed inertia methods, the adaptive inertia method shows better transient characteristics.

The invention discloses a soybean moisture content online detection system and method based on capacitance sensing, and relates to the field of agricultural machinery intelligent detection, and the system comprises a sampling mechanism, a control unit and a detection unit; the sampling mechanism is arranged on the outer side of a grain conveying channel of the soybean combine harvester and used for collecting soybeans in the grain conveying channel; the detection unit is arranged in the sampling mechanism and is used for detecting the weight, temperature and dielectric signals of the soybeans; the control unit is connected with the detection unit and the sampling mechanism, obtains a dielectric signal of the soybean and the temperature of the soybean when the weight of the soybean reaches a preset value, converts the dielectric signal of the soybean into a voltage signal, and controls the temperature of the soybean on the basis of the temperature of the soybean and the voltage signal; and calculating the moisture content of the soybeans by adopting a built-in moisture content-breakage rate nonlinear model. The soybean crushing rate is reduced, and the detection efficiency is improved.

The present application relates to the field of intelligent medical treatment, in particular to a tumor treatment dosing regimen Pareto optimization method based on forward-backward stochastic differential equation. The present application unifies the terminal tumor load, dosing cost and treatment risk into the same stochastic control framework, can establish the Pareto optimality condition corresponding to the nonlinear model, and further converts into the implementable closed-loop state feedback control law in the LQ case. Through the combination with the numerical example and the Pareto frontier analysis, an implementable, interpretable and scalable technical path is provided for the multi-objective individualized treatment regimen design, which can solve the problem that the existing technology is difficult to uniformly process the terminal therapeutic effect, dosing consumption and cumulative risk, and difficult to obtain the implementable closed-loop feedback strategy under the forward-backward stochastic control framework.

A vehicle includes a system and method of modeling and controlling tractive effort of a wheel of the vehicle. The system includes an observer, a predictive controller, and an online solver. The observer receives dynamic model parameters of the wheel and determines an estimate of a wheel speed and an uncertainty of the wheel speed using a nonlinear model of the wheel. The predictive controller determines an average gain and a differential gain from the estimate of the wheel speed and the uncertainty of the wheel speed. The online solver calculates a motor torque and a wheel brake torque for increasing tractive effort of the wheel on a road based on the average gain and the differential gain. The motor torque and the wheel brake torque are applied on the vehicle.

The invention discloses a buoy-unmanned aerial vehicle cooperative fault-tolerant docking control method based on event-triggered communication, and belongs to the field of unmanned aerial vehicle navigation, guidance and control. The method comprises the following steps: firstly, establishing a buoy-unmanned aerial vehicle affine nonlinear model considering the fault influence of an execution mechanism; then, a communication mechanism based on an event triggering strategy is designed, and in the collaborative docking process, aperiodic information interaction is carried out between the buoy and the unmanned aerial vehicle through the event triggering mechanism; then, based on a backstepping control theory, a controller is designed for the position, track, attitude and angular velocity loop of the buoy-unmanned aerial vehicle and the velocity loop of the unmanned aerial vehicle, and based on an observation-compensation idea, a lumped nonlinear item containing airflow disturbance and execution mechanism fault information is estimated and compensated; and finally, realizing cooperative fault-tolerant docking of the buoy and the unmanned aerial vehicle through the controller. According to the method, the accurate cooperative docking of the unmanned aerial vehicle and the buoy can still be realized under the conditions of limited communication resources, execution mechanism faults and complex airflow disturbance.

The invention discloses a distributed control method with unknown saturation hysteresis input, and relates to the technical field of distributed control systems, and the method comprises the following steps: S1, training a meta-learning model through a meta-learning algorithm, learning parameters and structures of a dynamic adjustment robust controller, and forming an offline meta-training stage; s2, real-time system feedback data are received, controller parameters are adjusted through the meta learning model, nonlinear characteristics such as hysteresis and saturation are dynamically compensated, and an online adaptation stage is formed. The parameters of the controller are adjusted on line through the meta-learning model, the nonlinear influence caused by unknown saturation hysteresis can be dynamically compensated, and an accurate nonlinear model is not needed. And in combination with Lyapunov stability analysis and a parameter constraint mechanism, the stability of a closed-loop system in transient and steady-state processes is ensured, and system oscillation caused by parameter adjustment is avoided. And the online adaptation stage can quickly adapt to a new nonlinear scene, and the response speed is improved.

The application provides a thermal power plant flexibility reconstruction and deep peak regulation optimization control method and system, relates to the technical field of control engineering, and comprises the following steps: collecting all-around operation data of the thermal power plant to construct a multilevel dynamic nonlinear model of the thermal power plant, performing real-time identification on model parameters, generating an identification result, and establishing a multidimensional operation characteristic database of the thermal power plant; based on the operation characteristic database of the thermal power plant, designing a reconstruction scheme, performing visual simulation and optimization to obtain flexibility reconstruction results; constructing a multi-objective optimization function, formulating an adaptive deep peak regulation optimization control strategy, performing short-term and medium-term prediction on power grid load, generating an initial control strategy based on the prediction result, performing online optimization and adjustment, and continuously evaluating and optimizing the control strategy.

The invention discloses a curative effect prediction method based on multi-organ metastasis genome data, and belongs to the technical field of medical models, and the method specifically comprises the following steps: collecting clinical pathological characteristics, multi-organ metastasis genome data and a treatment scheme of a breast cancer patient, and recording a metastasis part and a load state; dimensionality reduction is conducted on high-dimensional genome data through a regularization algorithm, feature importance is evaluated in combination with a nonlinear model, and clinical, treatment and genome features related to treatment response are screened out; inputting the screened features into a machine learning and deep learning framework, randomly dividing a training set and a test set in a layered manner, optimizing hyper-parameters through cross validation, and constructing a classic machine learning set model and a deep learning model based on an attention mechanism; disturbing test queue treatment scheme data, evaluating the consistency of model recommendation and an actual scheme, and verifying the prediction capability and clinical practicability of the model; according to the method, multi-dimensional data are integrated, and the curative effect prediction accuracy of the metastatic breast cancer is improved.

A method includes determining a portion of a flight path of an aerial vehicle. The method also includes determining an attribute value representing an operating condition expected to be experienced by the aerial vehicle at the portion of the flight path. The method additionally includes determining, based on the attribute value and using a non-linear model, a power value representing an amount of power expected to be consumed by the aerial vehicle in connection with the portion of the flight path. The method further includes determining, based on the power value, an energy value representing an amount of energy expected to be consumed by the aerial vehicle in connection with the portion of the flight path. The method yet further includes determining the flight path based on the energy value.

A method for separating load effects on a dedicated track bridge comprises the following specific steps: 1. Identifying the train passing phase; 2. Using a nonlinear model to separate train load and temperature load effect data; 3. Using an adaptive algorithm with a memory effect to remove temperature load effect data; 4. Using the multi-scale singular value decomposition (MS-SVD) method to process monitoring data; 5. Using a joint distribution model based on probability theory to optimally estimate the separation of temperature and train load effect data; and 6. Describing the temporal dynamics of train load and temperature load using a state transition matrix. The present invention improves the accuracy of monitoring data: Through adaptive data separation technology, the present invention effectively distinguishes between train load effects and temperature load effects in bridge health monitoring data, avoiding interference from slowly changing factors such as degradation effects, and significantly improving the accuracy of bridge structure health assessment results.

The invention relates to the technical field of communication transmission, and provides an equivalent simulation method and device for transmission performance evaluation of an optical fiber communication system, and the method comprises the steps: generating a signal power density spectrum of each span according to a link parameter; calculating a nonlinear noise power density spectrum according to the link parameters and the signal power density spectrum of each span; and taking the signal power density spectrum as an input parameter of the equivalent simulation model, and calculating a nonlinear damage index at the tail end of the optical transmission system to obtain transmission performance evaluation. A simulated signal power density spectrum is used for replacing a tested signal power density spectrum, the nonlinear damage simulation precision is still kept under the condition that model parameter dependence is reduced, and a nonlinear model is more flexibly applied to various scenes. According to the calculation method, the algorithm is optimized and upgraded from the two aspects of operation efficiency and input parameter dependency, the problems that a non-linear noise algorithm is low in operation speed and input parameters are difficult to obtain are solved, and the algorithm implementation complexity is reduced while the algorithm simulation precision is guaranteed.

The invention relates to the technical field of biochemical wastewater treatment and automatic control, in particular to a comprehensive utilization and treatment system for dioscorea zingiberensis saponin wastewater. The data acquisition module comprises a data acquisition module, a prediction module, a quantification module, a risk hedging module and a control execution module; the system estimates key enzyme activity by adopting a non-linear model by collecting a water inlet fingerprint vector and a reactor state; the core of the method is that a system safety distance is determined according to enzyme activity and flora health degree, and control weights of three targets of collapse risk, effluent compliance and resource recovery are dynamically adjusted according to the system safety distance; when the safe distance changes, the system executes compound control through a multi-objective optimization algorithm; according to the method, prospective early warning of the core biochemical state is realized, and the defect of response lag of a traditional method is overcome.