102 results about "Least squares support vector machine" patented technology

The invention discloses an improved integrated learning rock debris element content analysis system and method based on laser spectrum information. The method comprises the following steps: step 1, collecting a rock debris element LIBS spectrum data set; 2, dividing the rock debris element LIBS spectral data set into a training set and a test set, and carrying out noise reduction processing on original spectral data; 3, selecting characteristic spectral lines of the rock debris analysis elements according to the correlation coefficients, then selecting correlation interference spectral lines of the analysis elements, respectively combining the characteristic spectral lines with different correlation interference spectral lines to obtain more than one spectral line combination, and taking the spectral line combination as input of an integrated learning device; 4, constructing an ant colony optimization algorithm to optimize and improve the least square support vector machine ensemble learning base learner as an ensemble learner; and 5, designing a weighted distribution integration strategy of the base learners, and completing the optimal combination of the base learners. According to the method provided by the invention, a strong learner is constructed by combining a plurality of base learners, so that the accuracy and robustness of prediction are improved.

The invention discloses an electricity-heat-vibration multi-dimensional data fusion oil-immersed transformer abnormal state identification method, which comprises the following steps: analyzing multi-physical field coupling data on a transformer, extracting key characteristic parameters, and carrying out abnormal state identification by using an improved BES-LSSVM model. According to the method, a multi-source fusion data mining technology and a machine learning algorithm are combined, parameters are optimized by using a hybrid model of a BES optimization algorithm and a least square support vector machine, and classification and identification of abnormal working conditions are realized through a k-means clustering algorithm. According to the method, the accuracy and reliability of transformer abnormal state evaluation can be remarkably improved, and the fault occurrence rate and the maintenance cost are effectively reduced.

The invention discloses a combined model pollutant concentration prediction method and system based on multiple strategies, a storage medium and electronic equipment. The method comprises the following steps: firstly, performing multi-pollutant signal decomposition and feature fusion; then realizing data set division and standardization processing; time sequence window division and data tensor conversion are carried out; then training and preliminarily predicting an LSSVM (Least Square Support Vector Machine) model based on DBO optimization; and finally, carrying out residual error compensation and model performance improvement of an error correction module. According to the method, the hybrid kernel function least square support vector machine and the computational fluid mechanics correction model are innovatively combined, and the improved dung beetle algorithm is utilized to perform multi-objective optimization on the kernel parameters and the error compensation coefficient; and multi-modal feature mapping is realized through a dynamic weight distribution layer, and a photocatalytic reaction kinetic equation is introduced to implement prediction value inversion. The method successfully overcomes the technical bottlenecks that a traditional model lags in response to sudden emission and is insufficient in multi-pollution coupling effect modeling, and provides accurate decision support for air pollution prevention and control.

The invention belongs to the technical field of underwater acoustic navigation, and provides an underwater acoustic navigation enhancement method and system, a storage medium and equipment, and the method comprises the steps: carrying out the standardized preprocessing of obtained sonar data and seawater parameter data, carrying out the partitioning of the preprocessed data, considering the propagation time of sound waves, constructing a time observation equation, and solving the time observation equation. Joint estimation is carried out on the position of the seabed beacon of the azimuth and the nadir total delay error parameter; taking the estimated nadir total delay error parameter and the corresponding time sequence as a training sample set, constructing and training a nadir total delay error prediction model by adopting a least square support vector machine method, and taking the model as a target function by minimizing the sum of double absolute values of all error variables and weight vectors; and correcting the sound velocity time-varying error in the long-baseline underwater acoustic navigation by using the trained model, and carrying out navigation calculation by using extended Kalman filtering based on the corrected sound velocity time-varying error. According to the invention, the precision of underwater acoustic navigation is improved.

The invention discloses a thermal power plant boiler combustion control method and system, and relates to the field of boiler combustion system control technical equipment. By collecting control loop data of a boiler combustion system in real time, dynamic characteristics, stability and response efficiency of a loop can be analyzed through a multi-dimensional performance evaluation model (including six indexes of steady-state tracking performance, static tracking performance, maximum control deviation, steady-state error, over-limit time and comprehensive tracking performance); based on the evaluation result, the air-coal ratio, the oxygen amount set value and the over-fire air door opening degree are dynamically adjusted through a combustion linkage algorithm, and intervention correction is conducted on set values of loops such as superheat degree control, primary air, secondary air and the coal amount of a coal mill when the performance of the control loop is deteriorated; combining a least square support vector machine to predict NOx concentration and boiler efficiency; and finally, combustion optimization is realized through closed-loop control. The problems that traditional PID control lags behind and loop coordination is poor are solved, boiler efficiency is improved, and pollutant emission is reduced.

The invention relates to the field of vehicle route planning, in particular to a dynamic multi-target vehicle route planning method and system based on prediction and recombination, and the method comprises the steps: building a mathematical model of dynamic multi-target vehicle route planning, initializing a population through real number coding, adjusting the population to meet a constraint, carrying out the iteration through a genetic algorithm, and obtaining a candidate solution set; a Pareto front set is obtained and clustered, and a clustering center is recorded as a special individual; and when environment change is detected, predicting individuals in a new environment by using a gray model or a least square support vector machine, and constructing a new population until an optimal solution set is output. According to the method, the convergence speed of the algorithm is high, the response to the environment change is more sensitive and timely, the dynamic multi-target vehicle routing problem is effectively solved, and the application effect and the economic benefit of the vehicle routing in the actual life are effectively promoted.

The invention relates to the technical field of power cable intelligent monitoring, and aims to solve the technical problems that in an existing cable state monitoring system, multi-source heterogeneous data fusion is insufficient, the coupling relation between physical quantities is difficult to reveal, and insulation aging, current-carrying capacity and fault probability cannot be quantitatively predicted according to evaluation results. The invention discloses a cable state multi-source data fusion and prediction system. The system comprises a data acquisition module, a feature processing module, a prediction analysis module and a cloud management platform. The system collects partial discharge, surface temperature and vibration data of the cable through the distributed terminal; performing spectrogram decomposition and fusion of the multi-source data by adopting a non-subsampled contourlet transform algorithm to generate multi-scale fusion features; building a prediction model based on a particle swarm optimization least square support vector machine, and outputting an insulation aging state prediction value, a current-carrying capability evaluation value and a fault probability prediction value of the cable; and data management and full-life-cycle service are realized through the cloud platform.

The application discloses a method for monitoring error-related potentials in a brain-computer interface based on mutual information, and relates to a method for identifying error-related potentials, and aims at solving the problem of low recognition rate of error-related potential signals in the existing brain-computer interface. The application extracts time domain features by pre-processing original electroencephalogram signals and performing non-overlapping sliding window analysis; extracts frequency domain features from the signals by using the Welch method; combines the time domain features and the frequency domain features; uses mutual information as a measure between features and positive and negative categories; calculates the mutual information of the features and the categories and sorts them; filters out the features with high rankings; uses a least squares support vector machine to classify initial error-related potentials; performs leave-one-out cross-validation on samples; obtains and retains individual optimal models; and obtains the accuracy rate of final error-related potential classification. The application has the beneficial effect of improving the recognition accuracy of error-related potentials.

The invention provides a dynamic modeling simulation method and system for the energy efficiency ratio of a solar seawater desalination system, and belongs to the technical field of seawater desalination and system modelling. Membrane surface resistance, selective permeability and direct-current bus voltage ripple data in the photovoltaic electrodialysis process are firstly obtained; secondly, Fourier transform is carried out on ripple data, then the ripple data and membrane parameters are spliced to generate an input matrix, the matrix is imported into a deep belief network, and a restricted Boltzmann machine is used for extracting an unsteady-state ion impedance vector reflecting the influence of voltage fluctuation; a nonlinear regression model of the vector and unit water production energy consumption is established through a least square support vector machine; finally, the water production rate and the energy efficiency ratio are calculated according to the predicted energy consumption and the photovoltaic power, and a dynamic simulation curve is generated. According to the method, the nonlinear influence of the photovoltaic voltage ripples on the membrane impedance can be quantified through deep learning, and the precision of predicting the energy efficiency ratio of the seawater desalination system under the fluctuating power supply working condition is remarkably improved.

The application discloses a kind of power forest fire intelligent monitoring method, device and equipment based on through remote fusion, and storage medium, it is related to forest fire intelligent monitoring technical field, comprising: obtaining the position information of first monitoring node, and obtaining the temperature and humidity data and smoke concentration data corresponding to position information as environmental data;Position information and environmental data are input into least square support vector machine model for processing, and fire risk probability is obtained;When fire risk probability is greater than preset risk threshold, position information, environmental data and fire risk probability are combined to obtain fire preliminary screening information, and fire preliminary screening information is uploaded to cloud server, so that cloud server fuses satellite remote sensing data to complete fire confirmation, realize the non-blind area monitoring coverage of power forest fire along transmission line, realize the preferential transmission of key fire data, reduce communication resource waste, improve system response speed and transmission reliability of key data.

The present disclosure belongs to the technical field of power systems, and particularly relates to a load ultra-short-term prediction method and system based on a least squares support vector machine, which comprises the following steps: obtaining historical load of a load point; constructing a prediction model by using a least squares support vector machine; predicting the ultra-short-term load of the load point based on the obtained historical load and the constructed prediction model to obtain a first load ultra-short-term prediction result; calculating the error of the obtained first load ultra-short-term prediction result; predicting the obtained error to obtain an error prediction result; and obtaining a load ultra-short-term prediction result based on the obtained first load ultra-short-term prediction result and the error prediction result.

The application discloses a load prediction method and system based on big data, and relates to the technical field of power grid load prediction.The method comprises the following steps: based on a least squares support vector machine, pre-processing load historical data; through decomposing the load historical data with reduced complexity, obtaining a zero-crossing rate and sample entropy, and determining the multi-frequency components of the load data; through a preset hybrid algorithm and in combination with the multi-frequency components of the load data, training a multi-factor weighted combination analysis model; based on the multi-factor weighted combination analysis model and according to an improved grey wolf algorithm, determining the weight of the load prediction result of each prediction factor module; and weighting and combining the load prediction results of each prediction factor module to obtain a final load prediction result.The application improves the robustness of the model, dynamically updates the model output result, optimizes the weight proportion among the factor modules, and improves the adaptability of load prediction in a complex environment.

The application discloses a kind of bridge crane stress history acquisition method and system, the method includes obtaining the characteristic data of crane actual working state under preset time;The hoisting load data of preset time is expanded by Latin hypercube sampling technique, obtain the hoisting load data of crane inspection cycle, the hoisting load data of inspection cycle is input into the pre-trained tentacle-least squares support vector machine prediction model, and the predicted working cycle number is obtained;The hoisting load data of inspection cycle and the predicted working cycle number are converted into the trolley wheel pressure of equal sample size;The finite element model of crane girder is established, and the trolley is simulated with random load at rated speed from the side span of girder to the other side span, after traversing all trolley wheel pressure load, the stress history of crane girder at any position in inspection cycle is obtained.The application provides high-precision stress characteristic data for crane structure fatigue life assessment.

The application provides a power quality disturbance event correlation type identification method, and relates to the technical field of power system analysis, and comprises the following steps: first, obtaining feature information of any two power quality disturbance events at a same target monitoring point; then, processing the feature information of the any two power quality disturbance events to obtain correlation feature information between the any two power quality disturbance events; finally, inputting the correlation feature information into a correlation type classification identification model for identification to obtain a correlation type identification result between the any two power quality disturbance events. The correlation feature information obtained through processing and a multi-classifier based on a least square support vector machine (LS-SVM) can effectively identify the correlation type between the any two power quality disturbance events, and improve the identification accuracy.

The invention discloses a battery SOH estimation method based on a GAF-CNN-GWO-LSSVM, and belongs to the technical field of battery management systems. The method comprises the following steps: extracting a voltage time sequence in a partial charging process of the lithium ion battery, converting the sequence into a two-dimensional image by utilizing a Gramian angular field (GAF) method, and constructing a GASF image and a GADF image; inputting the obtained image into a two-dimensional convolutional neural network (2D-CNN) to extract deep image features; searching an optimal hyper-parameter of a least square support vector machine (LSSVM) model by using a grey wolf optimization algorithm (GWO); and inputting the extracted feature vectors into an LSSVM model to carry out SOH value prediction. The method has both characteristic expressive power and modeling precision, has relatively high adaptivity and engineering practicability, and is suitable for online health state evaluation of the power battery and the energy storage system.

The invention discloses a renewable energy consumption potential prediction method based on a combined model, and the method comprises the steps: obtaining a renewable energy penetration potential index, and carrying out the single-model prediction of the index through a long-short-term memory network model, a multiple linear regression model, a least square support vector machine model, and a random forest model; establishing a combined prediction model based on a single model prediction result, constructing a constraint condition taking a minimum average absolute percentage error as a target function, and introducing a discount factor matrix; a discount factor matrix is optimized through a quantum coding harmony algorithm, a new harmony solution is generated through iteration, and a quantum coding harmony memory bank is updated; and calculating a weight coefficient according to the optimized discount factor matrix, and outputting a joint prediction result of the quantum coding and sound memory library and the combined prediction model. The problems of insufficient multi-factor comprehensive consideration, limited application range of a single model and inconsistent data features in different prediction periods in the existing renewable energy consumption potential prediction can be solved.

This invention discloses a method and system for monitoring the operating status of power transmission and transformation equipment. First, real-time operating data of the power transmission and transformation equipment is collected and standardized for preprocessing. Then, feature extraction is performed on the preprocessed data to obtain feature data of the power transmission and transformation equipment. A pre-trained least squares support vector machine model is used to obtain the equipment operating status prediction result. Next, the equipment operating status prediction result is converted into a state confidence score. The state confidence scores are then fused using an improved D-S evidence theory method based on Euclidean distance to obtain a comprehensive confidence score for the equipment operating status. Finally, based on the comprehensive confidence score, an exponentially weighted moving average algorithm is used to construct a dynamic threshold for graded early warning of the power transmission and transformation equipment. This invention solves the technical problem that simple models cannot cope with the nonlinear correlation of multiple parameters in power transmission and transformation, and accurately adapts to the actual operation and maintenance needs of multi-parameter collaborative monitoring of power transmission and transformation systems.

The invention discloses a short-term load prediction method and system based on similar days and a combination model, and relates to the technical field of electric power, and the method comprises the steps: determining the meteorological influence factors of a multi-element load through a Pearson's correlation coefficient method, carrying out the similar day selection of the meteorological factors, the load date type and a price mechanism, constructing a historical data set, and carrying out the prediction of the similar days. A historical data set is decomposed into intrinsic mode function components under different frequencies by adopting improved variational mode decomposition, the intrinsic mode function components are predicted by adopting an improved least square support vector machine model, and an optimal short-term load prediction result is output. According to the method, meteorological influence factors are determined, similar days are selected, a historical data set is constructed, the historical data set is decomposed into intrinsic mode function components, the intrinsic mode function components are predicted, an optimal short-term load prediction result is output, a prediction model with higher adaptability is provided, and the accuracy and efficiency of short-term load prediction are improved.

The application relates to a sewage treatment process optimization control method based on a multi-objective sparrow algorithm, which comprises the following steps: adopting a least square support vector machine to establish a soft measurement model of total energy consumption and effluent water quality, and taking the soft measurement model as an optimization objective function; improving the sparrow algorithm from five aspects of population initialization, finder position updating, alarm position updating, optimal position disturbance strategy and external archive updating mechanism to obtain a multi-objective sparrow algorithm; optimizing the optimization objective function by using the improved multi-objective sparrow algorithm, obtaining a Pareto solution set, and optimizing the set values of dissolved oxygen concentration and nitrate nitrogen concentration; the scheme is improved according to the sparrow algorithm, and is applied to the sewage treatment process to optimize the set values of dissolved oxygen concentration and nitrate nitrogen concentration, so that the effluent water quality is improved and the energy consumption is reduced.

The invention discloses a method for optimizing and controlling the concentration of dissolved oxygen in an aerobic tank for petrochemical wastewater treatment. The method comprises the following steps: preprocessing field data by utilizing a Pauta criterion and a mean filtering method; an aerobic tank dissolved oxygen concentration prediction model based on a least square support vector machine is provided, optimal regularization parameters and hyper-parameters of a regression model of the least square support vector machine are obtained by using a particle swarm optimization algorithm, and the problem of low prediction accuracy of the aerobic tank dissolved oxygen concentration is solved; aiming at the problem of large fluctuation of inflow water quantity and water quality in the petrochemical wastewater treatment process, performing optimization calculation on a dissolved oxygen concentration set value in an aerobic tank by utilizing a particle swarm optimization algorithm; a model predictive controller based on a least square support vector machine algorithm is designed, rapid tracking control over a set value of the dissolved oxygen concentration in an aerobic tank is completed, the dissolved oxygen concentration in the petrochemical wastewater treatment aerobic tank is stabilized, and finally energy consumption of aeration equipment is reduced while the discharged water quality is stabilized to reach the standard.

The invention discloses an intelligent early warning method for support displacement of a highway-railway dual-purpose bridge based on long-term monitoring data. The intelligent early warning method comprises the following steps: acquiring a temperature index and support displacement monitoring data; calculating longitudinal, vertical and transverse temperature differences of the steel truss girder structure; principal component analysis is carried out on the temperature indexes, and principal components influencing support displacement are extracted; establishing an optimal least square support vector machine prediction model; determining a support displacement early warning threshold value; calculating a displacement residual error of the test sample; and intelligent early warning is carried out on support displacement. The method can be used for accurately predicting the support displacement of the highway-railway dual-purpose bridge, and intelligent early warning of the support displacement under the influence of environmental factors is realized; the method can be implemented in a programmed manner, is simple and quick to operate and has wide engineering application value; the method is of great significance to guarantee driving safety of high-speed trains and overall performance of bridge structures.

The invention provides a fault identification method, system and device for gas insulated switchgear, and a medium. The fault identification method comprises the following steps: collecting a discharge signal of the gas insulated switchgear; decomposing the discharge signal into eigenmode function components through a time-varying filtering empirical mode decomposition algorithm; wherein algorithm parameters of the time-varying filtering empirical mode decomposition algorithm are optimized through a chaos subtraction optimization algorithm; screening out eigenmode function components with the highest correlation with the discharge signals, calculating the fault features of each screened eigenmode function component, and inputting the fault features into a least square support vector machine for classification and recognition to obtain the fault type of the gas insulated switchgear. According to the invention, the accuracy and stability of signal decomposition can be effectively improved.

The application discloses a road condition recognition method based on multi-source heterogeneous data fusion. Firstly, the application adopts multiple sensors to collect road image data and vehicle speed, acceleration and wheel speed data, then pre-processes each part of data and extracts features by using different algorithms, then performs feature-level fusion and dimension reduction processing on the extracted feature vectors by using a multi-source heterogeneous data space-time fusion strategy, and finally trains and tests by using a least squares support vector machine based on particle swarm optimization to establish a road condition recognition model. Through the pre-processing, feature extraction and recognition model establishment of the data collected by multiple sensors, the application realizes accurate and efficient recognition of road conditions caused by different severe weather. The application can fully exploit multiple sensor resources, and then comprehensively analyze the collected data, so as to improve the road condition classification recognition accuracy and environmental adaptability.

A closed-loop intelligent optimization control system for a boiler combustion process comprises four modules: a system calculation module, a system monitoring module, a system communication module and a system interface module. A system calculation module calculates the optimization quantity of secondary air, burnout air, coal feeding amount offset and oxygen amount fixed value of each layer of the hearth, and meanwhile, if the modeling error is large, an online support vector machine model is updated; in each control period, a system calculation module needs to judge whether a current optimization system is input or not, if yes, the output control quantity is calculated according to the process, and if not, the output control quantity of an original control system is tracked; boiler combustion optimization is researched by means of a neural network and the like; and an online least square support vector machine is developed to establish a boiler dynamic model system, and intelligent closed-loop dynamic combustion optimization control of the boiler is promoted.

The present application discloses a method for equipment component failure mode identification for multi-source uncertain sparse samples, which relates to the technical field of equipment failure identification. The method comprises: considering that the equipment contains multi-source uncertain noise during actual service, applying model-independent meta-learning to an adaptive filter to filter the multi-source uncertain noise; then combining short tree transformation with a dual-stream spatiotemporal attention deep neural network to obtain time-frequency and multi-scale features from multi-channel data, thereby enhancing the ability to extract failure features and reducing the dependence of failure identification on data volume; finally, applying a least squares support vector machine for failure mode identification, and adopting a crested porcupine algorithm to determine the optimal hyperparameter combination, thereby improving the failure identification capability of the model under complex working conditions during actual service of equipment components, and effectively solving the problem in the prior art that failure modes of sparse data containing multi-source uncertain noise are difficult to identify.

A radio interference prediction method based on granular reduction of meteorological information and a least squares support vector machine (LSSVM) algorithm comprises the following steps: Step 1: obtaining radio interference values ​​and corresponding meteorological information from a measured city over the past three years as a total data set based on actual transmission line measurements; Step 2: determining and filtering out data sets within extreme weather intervals from the total data set based on temperature criteria, and dividing the total data set into a training set and a validation set with a ratio of 9:1. This invention provides a radio interference prediction method based on granular reduction of meteorological information and a least squares support vector machine algorithm, which can relatively accurately predict radio interference values ​​and has good adaptability to extreme weather conditions, providing insights for the research of radio interference value prediction methods in practical engineering projects.

The application provides a tool state detection method based on a main shaft current signal, comprising the following steps: designing an orthogonal experiment related to a cutting speed, a feed rate and a cutting depth; collecting a single-phase current of a main shaft motor driver by using a current sensor; collecting a wear amount of a rear tool face of a milling cutter after each experiment, fitting a tool wear curve according to the collected wear amount; performing denoising on the collected original signal of the single-phase current of the main shaft motor driver based on a third-fourth quantile method of a sliding window; extracting time-frequency domain features and time-frequency joint domain features of the single-phase current signal of the main shaft motor driver based on the preprocessed data; selecting n features from the time-frequency domain features and the time-frequency joint domain features as input of a prediction model by using grey correlation degree analysis; establishing a prediction model based on a least square support vector machine regression model, optimizing the prediction model by using a genetic algorithm, and predicting the tool wear amount based on the optimized prediction model.

The invention discloses an aflatoxin G1 content detection method based on terahertz metamaterial resonance enhancement, a terahertz metamaterial absorber is composed of a plurality of periodic structure units, and each periodic structure unit is of a square structure. The periodic structure unit comprises four cross-shaped structures located in the middle and four strip-shaped structures located on the peripheries of the four cross-shaped structures, terahertz spectrum information of aflatoxin G1 solutions with different concentrations is collected based on the terahertz metamaterial absorber, and it is found through analysis that the terahertz spectrum information of the aflatoxin G1 solutions with different concentrations is within the frequency band of 0.5-3.0 THz. Along with the increase of the concentration of the aflatoxin G1 solution, the amplitudes of characteristic peaks at 0.7 THz and 2.1 THz are regularly attenuated and slightly blue-shifted, in the established least square support vector machine model, the performance of the model which is subjected to non-information variable elimination characteristic extraction and adopts an RBF kernel function is better, the RP is 0.9451, and the detection limit LOD is 2.18 * 10 <-6 > mu g / ml. With the adoption of the method, rapid, nondestructive and high-sensitivity detection of the aflatoxin G1 can be realized.

The application provides a short-term power load prediction method based on Spearman-IPSO-LSSVM, comprising the following steps: obtaining historical power load data and corresponding historical meteorological data of a power grid area to be measured; determining the correlation between the historical power load data and the historical meteorological data based on the Spearman correlation coefficient method, and selecting historical meteorological data with a correlation greater than a preset correlation threshold as key meteorological data; constructing a least squares support vector machine prediction model based on the key meteorological data; optimizing the least squares support vector machine prediction model by using an improved particle swarm optimization algorithm to obtain a least squares support vector machine optimization model; and predicting power grid load feature data at a time to be predicted based on the least squares support vector machine optimization model to obtain a prediction result of the power grid load, which can improve the generalization ability and parameter optimization efficiency of the least squares support vector machine optimization model and improve the accuracy of the prediction result.

The invention relates to the technical field of textile machinery control, in particular to a key quality characteristic cooperative control method for an air-jet vortex spinning machine, and aims to solve the core problems of low strength, poor evenness and high fiber falling rate of air-jet vortex spun yarns in China. The method comprises the following steps: firstly, constructing a'strength-evenness-fiber falling rate 'multi-quality characteristic coupling relation model by utilizing a cross coupling technology, measuring the coupling degree of the three parts through an entropy coupling algorithm, and clarifying an interaction rule among the characteristics; then designing a fuzzy controller based on the Lie group theory and T-S fuzzy logic, tracking tension-detail-weak ring online data and solving an optimal'balance point '; combining expert knowledge and a least square support vector machine to construct a quality characteristic analysis model and a data deviation model, and integrating through a generic relationship theory to obtain a model controller; and finally, fusing the two controllers by using an H-infinity robust control theory to form a combined controller to realize cooperative control.