434 results about "Fuzzy neural" patented technology

The present application relates to the field of wire laying control technology, and discloses a composite material automatic wire laying forming optimization method, system, medium and wire laying machine. The method comprises: inputting dynamic tension control data into a fuzzy neural network calculation engine to obtain a dynamic compensation model for the retraction and release speed; based on the dynamic compensation model for the retraction and release speed, using a reinforcement learning engine to calculate the shearing sequence of multiple bundles and generate an intelligent shearing instruction set; constructing a re-delivery trajectory mapping relationship based on the intelligent shearing instruction set, and forming a re-delivery path matrix after iterative calculation by a digital twin engine; establishing a temperature field distribution prediction model based on the re-delivery path matrix, and constructing a temperature field dynamic control sequence after finite element analysis and calculation; substituting the temperature field dynamic control sequence into a multi-objective constraint equation, and outputting a laying strategy data packet after deep reinforcement learning iterative optimization. The present application realizes the precise coordination of multiple process parameters such as tension control, speed compensation, shear control, and temperature control.

The invention relates to a multi-sensor cross-scene dynamic preferential fusion positioning and mapping method, and the method comprises the steps: obtaining the data of a plurality of sensors, and completing the unification of the time-space relation of the data of the plurality of sensors; processing the data, carrying out loopback detection on image key frame data acquired by a camera, constructing to obtain an I MU pre-integration factor, a visual inertial odometer factor, a laser radar odometer factor, a GPS inertial odometer factor, a UWB factor, a GNSS factor and a loopback detection factor, and adding the factors into a factor graph for optimization; a global positioning pose and a map are obtained; and optimizing the multi-sensor data fusion strategy based on a deep fuzzy neural network. According to the multi-sensor cross-scene dynamic preferential fusion positioning and mapping method provided by the invention, high-precision positioning and navigation of an agricultural robot in different scenes are realized through real-time fusion of various sensor data, and the problems of scene dependence and insufficient precision of an existing single sensor scheme are solved.

The invention provides an electric bicycle charging method and system based on a mutual recognition mechanism, and relates to the technical field of battery management, and the method comprises the steps: detecting the state of a battery through a multi-sensor fusion algorithm, predicting the endurance mileage and charging demands through deep reinforcement learning, and predicting the destination and arrival time through track analysis; a charging pile is optimized based on a multi-constraint path planning model and an improved multi-target genetic algorithm, a target charging pile adopts dual encryption identity authentication, a self-adaptive charging control algorithm based on a fuzzy neural network is used for optimizing a charging process, and data is uploaded to a charging scheduling server.

The invention discloses a ship intelligent collision avoidance decision-making method and system, and relates to the technical field of ship intelligent navigation, and the method comprises the steps: collecting radar data and AIS information of a ship; building a ship dynamic prediction model according to the radar data and the AIS information, predicting the future navigation trajectory of the target ship through a Kalman filtering algorithm, and calculating the meeting parameters of the two ships in combination with the navigation parameters of the ship; based on the meeting parameters, establishing a risk assessment matrix, and performing risk level judgment on the current navigation situation by using a fuzzy neural network algorithm; based on the risk level judgment result, collision avoidance maneuvering parameters are calculated through an adaptive particle swarm optimization algorithm, and a collision avoidance action scheme is output. According to the invention, the whole process automation from information perception, situation prediction, risk assessment to decision execution is realized, the workload of navigation personnel is greatly reduced, and the navigation safety level is improved.

The invention discloses an intelligent storage equipment health state assessment and predictive maintenance method and system, and the method comprises the steps: arranging a multi-mode sensor network on equipment, collecting vibration, stress, temperature, current, voltage and image data in real time, carrying out the collection and preprocessing of edge data, and transmitting the data to an analysis layer; and the analysis layer performs online self-learning calibration by using a digital twin model, fuses the data in combination with a fuzzy neural network for dynamic weight adjustment, and calculates a health index and a residual life prediction value of the equipment. The system can give out an early warning before a fault symptom occurs, and automatically generates a maintenance decision and a task scheduling scheme, thereby achieving the real-time monitoring and predictive maintenance of the equipment state, and remarkably improving the operation efficiency of a warehousing system and the reliability of the equipment.

The invention relates to the field of industrial automation and intelligent control, and discloses an intelligent temperature automatic control system for a digital glass mold. The method comprises the following steps: acquiring mold surface temperature and heat flow data through a thermocouple array and a thermal infrared imager, and constructing heat flux and a disturbance coefficient; forming state vectors are established in combination with the forming process parameters and the heat flow distribution, and forming stability levels are generated through support vector regression; building a heat balance deviation model based on the thermophysical parameters and the environment variables, and predicting a temperature trend; a temperature control instruction is generated through the fuzzy neural controller, and temperature dynamic closed-loop control is achieved. The system improves the temperature control precision and thermal field balance of the glass mold, and is suitable for intelligent temperature control management in the glass container forming process.

The invention provides a river crab feed feeding mixed monitoring method, system and device and a medium, and belongs to the field of aquaculture intelligent monitoring. Data is collected and normalized, and a chain type data collection system is constructed; the edge computing and block chain technology is adopted to realize data distributed storage and transmission; a fuzzy neural network hybrid model is constructed, fuzzification processing, feature mining and defuzzification output are integrated, and a feeding amount prediction value is generated; associating the feed proportioning library with the knowledge graph, and iteratively training the model and generating a multi-target feeding decision parameter; feeding equipment is controlled to execute actions according to the decision parameters, and the real-time monitoring model outputs and triggers an early warning signal; and updating the knowledge graph and the feed matching library based on the feedback data to form closed-loop self-adaptive regulation and control. According to the method, various factors can be comprehensively considered, scientific and intelligent feed feeding decision is realized, feed waste and insufficient feeding are effectively avoided, and the growth quality of the river crabs is improved.

The invention relates to the technical field of lithium battery safety management, and particularly discloses a lithium battery thermal runaway early warning and protection method based on an adaptive neural network, which comprises the following steps: acquiring temperature, internal resistance and voltage data of a lithium battery in real time, extracting multi-dimensional features based on discrete wavelet transform, singular value decomposition and principal component analysis algorithms, and performing early warning and protection on the lithium battery thermal runaway. The influence of internal resistance and voltage on abnormal temperature fluctuation is quantified; constructing a feature vector through the comprehensive influence coefficient, inputting the feature vector into an adaptive fuzzy neural network prediction model, outputting a temperature anomaly fluctuation coefficient, and accurately predicting the thermal runaway risk of the lithium battery; according to the lithium battery thermal runaway prediction method, multi-level protection measures such as early warning response triggering, system automatic charging and discharging strategy adjusting, power output reducing and heat dissipation system starting are taken based on the prediction result, development of the thermal runaway risk is restrained, multi-dimensional data fusion and an intelligent algorithm are combined, and the lithium battery thermal runaway prediction accuracy and the risk control real-time performance are improved.

The invention relates to the technical field of gas steel cylinder pressure control, in particular to a gas steel cylinder multi-stage pressure control method based on a self-adaptive threshold value, which can dynamically correct the influence of ambient temperature on a pressure reference value through a constructed pressure-temperature compensation function, eliminate measurement errors caused by temperature drift, and improve the accuracy of pressure control. A pressure fluctuation entropy value is calculated in real time based on a sliding window algorithm, a self-adaptive threshold value adjustment coefficient alpha is generated in combination with historical working condition database matching, a control threshold value is dynamically adjusted along with gas flow velocity fluctuation and equipment aging degree, and the pressure over-limit risk is reduced; according to the method, fuzzy neural network control is introduced in a critical adjustment stage through a multi-stage pressure adjustment strategy, an improved radial basis function dynamic updating mechanism can adapt to a pressure sudden change mode online, the control response speed is increased, and the steady-state error is controlled within + / -1.5%.

The invention relates to the technical field of slope settlement monitoring and early warning, in particular to a visual slope settlement monitoring and early warning method and platform. The method comprises the following steps: acquiring slope settlement monitoring data; preprocessing the acquired slope settlement monitoring data; respectively constructing a multi-scale slope digital twinborn model and a hybrid intelligent prediction model; constructing a multi-level early warning index system; constructing a fuzzy neural network early warning model based on a multi-stage early warning index system through a multi-scale slope digital twinborn model and a hybrid intelligent prediction model; and performing visual slope settlement early warning by using the fuzzy neural network early warning model. According to the space-air-ground integrated monitoring network constructed by the invention, a satellite InSAR, an unmanned aerial vehicle LiDAR and distributed optical fiber sensing are fused, and full-scale monitoring from regional macroscopic deformation to slope surface microcracks and deep soil displacement is realized.

The invention discloses a motion data management method and system based on AI body measurement, and relates to the technical field of artificial intelligence and machine learning, and the method comprises the steps: collecting motion data of each user, and carrying out the preprocessing; dividing user intervals by using a K-means clustering algorithm, constructing a load prediction model by using a fuzzy BP neural network, and obtaining a final load prediction value by using an improved adaptive genetic algorithm; and constructing a regression model by using a QHD reinforcement learning algorithm, dynamically adjusting a discount factor, outputting a training load adjustment strategy, and adjusting the discount factor according to feedback. User training load intervals are accurately divided through a K-means clustering algorithm, distribution of personalized training loads is ensured, the load prediction precision is improved by using a fuzzy BP neural network and an IAGABP algorithm, the weight and bias of the network are optimized, a local optimal solution is avoided, the prediction accuracy is improved, a QHD reinforcement learning algorithm and a dynamic discount factor are combined, and the prediction accuracy is improved. And a strategy of dynamically adjusting the training load is realized.

The invention belongs to the technical field of laser welding, and discloses a real-time feedback control method and system for laser welding penetration stability, and the method comprises the steps: obtaining plasma electron temperature characteristics in a laser welding process in real time through a spectrum monitoring system, and enabling the plasma electron temperature characteristics to be associated with penetration fluctuation as a core input signal of feedback control. The signal has better real-time performance and more accurate feature extraction capability in real-time feedback control of laser welding by virtue of broadband coverage and multi-dimensional information acquisition capability of the signal in combination with a more efficient data analysis mode. A parallel type self-learning fuzzy neural network controller is used for executing real-time feedback control output of laser welding penetration fluctuation. On a control architecture, a traditional PD controller and a fuzzy neural network are connected in parallel and are respectively used as a PD control module and a fuzzy neural network control module. And the process database is embedded into the forepart structure of the neural network control module in a fuzzy rule form.

The invention relates to an MPPT (Maximum Power Point Tracking) control method based on dynamic optimization of a lightweight convolutional neural network, and belongs to the field of artificial intelligence control power tracking. The method comprises the following steps: S0, constructing a convolution-fuzzy neural network model which at least comprises an input layer, a convolution layer, a fuzzy control layer, a deep reinforcement learning layer and an output layer; s1, key parameters of photovoltaic equipment are collected in real time to serve as input data of an input layer; s2, performing convolution operation on the input data by using a convolution layer, extracting a feature vector, and transmitting the feature vector to a fuzzy control layer; s3, combining a fuzzy control layer with a deep reinforcement learning layer, and dynamically adjusting an optimization process of a fuzzy rule through a reward signal; and S4, the fuzzy control layer generates a control signal, and the control signal is transmitted to a DC / DC converter of the photovoltaic system through the output layer. The control method is suitable for a dynamic environment of a photovoltaic power generation system, can be expanded to a grid-connected system, and provides a new thought and technical support for efficient operation of photovoltaic power generation.

The invention discloses a fuel cell output voltage control method and system based on fuzzy neural network PID, and the method comprises the steps: building an air-cooled fuel cell voltage model based on an MATLAB / Simulink simulation platform, and obtaining the temperature changes of an air-cooled fuel cell under different working conditions; establishing an air-cooled fuel cell test bench, and obtaining a polarization curve of output voltage changing along with current; according to experimental data, calibrating the semi-empirical equation by adopting a nonlinear least square method, and comparing experimental results to verify the accuracy of the model; the input hydrogen flow is changed through the PID controller, so that stable control of the output voltage of the galvanic pile is realized, and the power generation performance of the battery is improved; a fuzzy neural network is adopted to optimize a PID controller, control parameters of PID are adjusted in a self-adaptive mode according to the error between expected voltage and actual voltage and the error change rate, and therefore a better temperature control effect is achieved. The method has the advantages of high robustness, high response speed and the like, and ensures that the voltage output of the fuel cell stack is maintained at a fixed preset value.

The invention provides a wind power plant ultra-short-term power prediction optimization method based on a laser radar, and the method employs the laser radar to scan a wind power plant region, and obtains the wind speed and direction data based on a coherent Doppler wind measurement principle. The wind speed extrapolation prediction is carried out by adopting an algorithm combining EMD and ARCH, the EMD is firstly used for decomposing a wind speed time sequence, then an ARCH model is established for each intrinsic mode function, and finally the ultra-short-term wind speed is obtained by combining prediction results. Then, an algorithm combining SVR and GA is used for power preliminary prediction, the SVR establishes a nonlinear relation between input parameters and power output, and the GA optimizes SVR parameters; then, the preliminary prediction result is processed and corrected based on an FNN algorithm, and the laser radar prediction wind speed and the preliminary power prediction value are input into a fuzzy neural network, so that the prediction accuracy is improved. And performing precision evaluation on the correction result according to specification requirements, and if the result does not reach the standard, adjusting parameters or improving the method. And when the precision reaches the standard, outputting a wind power forecast time sequence power value in the future 3-5 hours.

The invention provides a hilly and mountainous area tracked vehicle intelligent chassis system and algorithm based on environmental perception and road surface recognition, and belongs to the technical field of agricultural mechanical equipment. The system comprises a mechanical structure part and a control system part. According to the algorithm, multi-modal sensing and fusion decision making of the mobile terminal are achieved, the FPN + ResNet50 algorithm, the IPM image inverse perspective algorithm, the 3D sparse convolutional graph neural network, the TSDF truncation signature distance algorithm, the MDU algorithm, the gating modal sensing convolutional network and the Pi-Sigma fuzzy neural network learning algorithm are innovatively combined, the recognition precision and the decision making reliability are guaranteed, and meanwhile, the fusion decision making efficiency of the mobile terminal is improved. And the calculation amount and the calculation cost are reduced, and the recognition efficiency is improved, so that more accurate, timely and reliable recognition perception and feedback control are realized. The algorithm comprises the steps of image acquisition, image feature extraction, feature fusion, feature comparison and the like, and the method is not only suitable for field operation of tracked vehicles in complex hilly and mountainous areas, but also suitable for transfer driving of the tracked vehicles in a working environment.

The invention discloses an expressway hard shoulder dynamic opening method and system based on an NFGD model and a simulation platform. Comprising the following steps: collecting and adopting a dynamic confidence mechanism to carry out weighted fusion on multi-source real-time sensing data to obtain fused traffic state characteristics; a hard road shoulder dynamic control decision is made based on an NFGD model comprising a fuzzy-neural hybrid controller, a multi-target genetic algorithm controller and a reinforcement learning controller; and real-time interaction with a simulation platform is realized, and control instruction issuing, feedback acquisition and online strategy evaluation are realized. Compared with a traditional method based on a fixed empirical threshold value, the fuzzy-neural hybrid controller, the multi-target genetic algorithm controller and the reinforcement learning controller are fused, self-adaptive modeling and open discrimination are achieved, and decision-making precision and scene adaptability are improved. And meanwhile, by combining with a prediction adjustment feedback type step length control structure, simulation time drift is effectively inhibited, the timeliness of a control strategy is enhanced, and the method has relatively high practical application value.

The invention provides a wind power plant bird trajectory prediction and fan linkage control method based on a neural network, and relates to the technical field of intelligent power grids, and the method comprises the steps: recognizing a bird target in real time, generating trajectory data, processing the trajectory data through three-dimensional Hilbert-Huang transform and an adaptive decomposition algorithm, extracting feature parameters for prediction analysis, and obtaining a bird trajectory prediction result. A collision risk is predicted and evaluated based on a trajectory, a cooperative avoidance control algorithm is constructed by adopting an artificial potential field method, a control strategy is optimized by combining an adaptive fuzzy neural network and a sliding mode controller, the operation state of a fan is monitored in real time, normal operation of the fan is recovered after birds fly away safely, and intelligent bird protection of a wind power plant is realized.

The invention relates to the technical field of program control, in particular to a precise powder supply control system, which is characterized in that multi-cycle differential processing of a rotating speed fluctuation ratio of a spiral feeder is performed on multiple key parameters such as a powder batch particle size value, a batch density value, an accumulated discharging amount and a feeding cycle; abnormal fluctuation numbers are extracted in different periods and combined according to the abnormal fluctuation numbers to generate a feeding feature set, a fuzzy neural network is utilized to improve the fitting precision of a discharging rate change trend under a nonlinear condition, the screening capability of extreme value interference is improved, a fluctuation difference value sequence is associated with symbol consistency and a density mean value, and the accuracy of the fluctuation difference value sequence is improved. Analyzing the variation trend of the discharge deviation in the symbol direction and the numerical slope, matching the numbers to establish a feed deviation grade section sequence, adopting a generative adversarial network to construct a target and actual discharge quantity difference value sequence, dividing symbol consistent sections, extracting the coupling trend of the fluctuation slope and the average density value, and obtaining a target discharge quantity difference value sequence; and misjudgment caused by deviation mode covering is avoided.

The invention relates to an intelligent production management system and method for high-concentration polyacrylamide fracturing fluid, and the method comprises the steps: obtaining multi-source heterogeneous data through designing a unique algorithm model and an innovative data processing flow: an innovative data collection and fusion strategy, and designing a comprehensive and high-precision data collection system; based on intelligent control algorithm innovation of a fuzzy neural network and MPC, the fuzzy neural network and model prediction control are combined to carry out production process control, a robust optimization method is introduced to enhance the robustness of a control strategy, and meanwhile, a rule-based emergency processing mechanism is established; intelligent mechanism innovation of quality monitoring and optimization is realized, and real-time monitoring and analysis of product quality are realized by establishing a quality prediction model based on combination of support vector machine regression (SVR) and a particle swarm optimization (PSO) algorithm.

The invention provides an underwater propeller control method and system based on tensor recognition and fuzzy control. The underwater propeller control method and system are suitable for improving the propelling efficiency and adjustment intelligence in a complex flow field. According to the method, a wake flow simulation model is constructed based on geometric parameters and boundary conditions of a propeller, a rotation tensor and a strain tensor are derived after flow field data are obtained, and a tensor field index is extracted through function space mapping so as to determine a target grid region. A high-rotation candidate area is screened through a spectral clustering algorithm, a vortex structure is identified, and multi-dimensional features such as the scale, the strength, the axial direction and the vortex core position of the vortex structure are extracted. And constructing a state vector by combining the current propulsive efficiency and flow field disturbance parameters, inputting an adaptive fuzzy neural network model, reasoning a relationship between a vortex and an operation state, outputting a propeller rotating speed and an attack angle adjusting quantity, and realizing intelligent response and energy efficiency optimization of wake flow disturbance.

The invention discloses an unmanned helicopter control method based on a fuzzy neural network extended state observer, and belongs to the technical field of transverse unmanned helicopter flight control, and the structure of a transverse unmanned helicopter flight controller comprises an FNNESO-RBSC controller, a control strategy and a transverse unmanned helicopter flight dynamics model; the FNNESO-RBSC controller calculates transverse, longitudinal, course and vertical channel control quantities according to system input and output information, the control quantities are distributed to all control surfaces of the transverse unmanned helicopter through a control strategy and act on a controlled object of the transverse unmanned helicopter, object state response of the transverse unmanned helicopter is changed under the action of aerodynamic force, and the control surfaces of the transverse unmanned helicopter are controlled. And the target flight state is controlled. According to the method, the fuzzy neural network is adopted to transform the extended state observer, and the adaptability of the extended state observer is improved, so that the unknown total disturbance needing to be compensated in the backstepping control law is more effectively obtained.

The invention belongs to a robot control method, and provides a human-guided robot-environment interaction self-adaptive control method and a related device for solving the technical problems that an existing human-guided robot-environment interaction control method is insufficient in motion stability, low in human operator safety and low in control precision. And estimating an optimal impedance parameter through a state space model, and learning a human-guided reference trajectory through a neural network in combination with the optimal impedance parameter. Then, in combination with a robot Jacobian matrix and a human-guided reference trajectory, a reference joint speed and an estimated acceleration of a joint space are obtained through closed-loop inverse kinematics, then network parameters of a robot dynamic model are obtained through approximation of an uncertain dynamic model, and finally, the robot dynamic model is obtained through combination with the reference joint speed and the network parameters of the joint space. And the mechanical arm control torque is obtained through the offset width fuzzy neural network. Accurate and compliant control under uncertain motion and dynamics conditions is realized.

The invention discloses a quantum fuzzy neural network adaptive to high-dimensional input and a classification method, and relates to the field of quantum calculation and fuzzy neural networks and the field of computer vision. The network input layer receives high-dimensional data, amplitude coding, forward and reverse enhanced chain entanglement layer, parameterized quantum transformation and fuzzy set mapping are carried out through a quantum fuzzy feature extraction module, and dynamic dimension fuzzy features are output; high-dimensional neural features are extracted through a DNN feature extraction module to adapt to quantum fuzzy feature dimensions; dynamically distributing the weights of the quantum fuzzy features and the classic neural features through an adaptive feature fusion module; and carrying out Softmax classification on the fusion features through a classifier, and outputting a category probability. According to the method, the high-dimensional data coding efficiency can be effectively improved, the complex fuzzy logic relation learning capability of the quantum part and the quantum state correlation stability are enhanced, the uncertainty of the data is represented, and accurate classification of high-dimensional uncertainty images is realized while noise interference is reduced.

The determination of a production index for a selected well using a fuzzy logic and neural network model (a “fuzzy-neural model”). Input data may be obtained from one or more producing wells and preprocessed for use in training and testing. The preprocessed data may be fuzzified into fuzzy values using fuzzy sets, membership functions, and a rule base. The neural network may be trained using the fuzzy values from the fuzzification to output the production index. The trained fuzzy-neural model may then be used to determine a production index for new data from the selected well.

The invention discloses a cross-working-condition bearing fault diagnosis method and device based on a multi-scale convolutional fuzzy neural network migration model, and the method comprises the steps: inputting a vibration signal of a to-be-diagnosed bearing into a trained cross-working-condition bearing fault diagnosis model, and outputting a diagnosis result; wherein the trained cross-working-condition bearing fault diagnosis model is obtained by training a multi-scale convolutional fuzzy neural network migration model by adopting a training data set, and a total loss function in the training process comprises classification loss guided by error samples, depth domain adaptive loss and confusion loss of an adversarial network domain; the training data set comprises a vibration signal from the bearing under the source domain working condition and a corresponding fault label, and a vibration signal from the bearing under the target domain working condition; the migration model based on the multi-scale convolutional fuzzy neural network comprises a multi-scale convolutional neural network module, a gated recursive convolutional attention module, an improved adaptive fuzzy inference system and an adversarial network module which are connected in sequence. The objective of the invention is to solve the problem of poor bearing fault diagnosis accuracy under variable working condition migration conditions.

The invention provides an electro-hydraulic servo pump control method and system for controlling hydrogen compression based on fuzzy nerve, and relates to the technical field of hydrogen compression, and the method comprises the steps that the current state of a hydraulic cylinder controlled by an electro-hydraulic servo pump is determined, and an input control variable is obtained; determining an output control variable, establishing a fuzzy control rule, and dividing a basic discourse domain; performing composite defuzzification, and obtaining a corrected weight coefficient through a feedforward neural network; the discrete rule amplitude correction factor and the weight coefficient correction are dynamically optimized and corrected; neural network training is established, a loss function evaluation model is utilized, and hydrogen compression control is achieved through a double-buffering mechanism. Fuzzy PID control is adopted, composite defuzzification is carried out according to the real-time state change of a system in combination with fuzzy logic, a membership function output by the fuzzy PID is divided according to regions, a control signal of a driver is synthesized, and hydrogen compression control is achieved; system output is adjusted according to different working conditions, power loss is reduced, and system control precision is improved.

The invention discloses an industrial equipment energy consumption optimization method based on sensor data and a fuzzy neural network, and belongs to the technical field of industrial automation control, and the method comprises the specific steps: S1, collecting process data including workpiece temperature and equipment power in real time through sensors disposed on industrial equipment and a workpiece, a state estimation algorithm based on a Kalman filtering theory is adopted to carry out online calibration on key state variables of a pre-constructed multi-physics field coupling digital twin model so as to generate a system state vector capable of accurately reflecting high-dimensional information such as a predicted temperature and a curing degree in a workpiece; the method ensures the long-term effectiveness and accuracy of the digital twinborn model, and can reduce the comprehensive energy consumption while obviously reducing the residual stress of the product and shortening the curing period.

A decoupling control method for a six-axis vibration table and a system for implementing the decoupling control method for a six-axis vibration table, capable of performing real-time adjustment on the vibration table according to specific conditions, thereby enhancing the robustness of the system, and achieving high reliability and good accuracy. The method comprises: acquiring data information of a target six-axis vibration table; representing a dynamic coupling model of the target six-axis vibration table in a global linearization manner to construct a koopman predictor; using a deep neural network to obtain a feature function and an operator matrix of the predictor; performing training to obtain a deep koopman estimator, and obtaining state information of the target six-axis vibration table; using the deep koopman estimator as a prediction model to design a multi-dimensional model prediction controller; using a fuzzy neural network to perform online tuning; and using the tuned multi-dimensional model prediction controller to control the target six-axis vibration table, so as to complete decoupling control over the target six-axis vibration table.