36 results about "Nonlinear dynamic systems" patented technology

This application discloses a multivariate time series prediction method and apparatus, relating to the field of data prediction technology. The method includes: constructing a network model based on a graph convolutional neural network according to the topology of a target nonlinear dynamic system; the network model includes an adaptive graph module, an encoder, a spatial neural differential equation module, a temporal neural differential equation module, and a decoder connected in sequence; training the network model using a historical dataset of the target nonlinear dynamic system to obtain a multivariate time series prediction model; introducing state feedback through the spatial neural differential equation module to reveal the evolution pattern of the spatiotemporal time series in the spatial dimension, and introducing neural differential equations based on nonlinear state transition theory to simulate the state evolution at the temporal level, thereby improving the effectiveness of data prediction by fusing spatial features while suppressing feature oversmoothing.

This invention discloses a monitoring method and system for the preparation process of ternary cathode materials based on RVAE. It constructs a nonlinear dynamic system model of the sintering process based on a variational autoencoder; assigns different weights to samples at different times in the constructed nonlinear dynamic system model of the sintering process, derives the loss function of the nonlinear dynamic system model of the sintering process, and trains the model parameters through backpropagation; defines the statistics of the nonlinear dynamic system model of the sintering process based on the cyclic variational autoencoder, and obtains the control threshold of the nonlinear dynamic system model of the sintering process through kernel density estimation; collects online data as a test set for the nonlinear dynamic system model, calculates the monitoring statistics online and compares them with the control limits to determine whether a fault has occurred. This invention can significantly improve the fault detection rate and false alarm rate, providing a strong guarantee for the stable operation of the sintering process.

The application relates to a configuration design method of a three-dimensional orbiting sail with posture stability, and belongs to the field of spacecraft attitude dynamics and structure design.The application is realized by the following method: considering the environmental perturbation of atmospheric resistance, atmospheric resistance moment and gravity gradient moment, an orbit attitude coupling dynamics model of a three-dimensional orbiting sail system is established based on a position vector and Euler angles; the three-dimensional orbiting sail and a spacecraft body are regarded as rigid bodies, and the windward area is considered to be blocked by airflow so as to more accurately describe the orbit movement and attitude movement of the system in the orbiting process; the attitude movement is simplified for the attitude dynamics of the system; the attitude stability of the system in the orbiting process is analyzed by using a phase plane method, a Lyapunov energy function method and other stability analysis methods of nonlinear dynamics systems; the relationship between important structure parameters such as a cone angle and a support rod length of the three-dimensional orbiting sail and the attitude stability is further constructed; and the three-dimensional orbiting sail satisfying the preset attitude stability requirement is obtained based on the relationship, so that the configuration design is realized.

The invention discloses a pathological speech feature processing method and system based on event driving, and the method comprises the steps: obtaining a pathological modal signal, carrying out the phase-space reconstruction through the pathological modal signal, obtaining a phase-space trajectory, and building a nonlinear dynamic system model based on the pathological modal signal and the phase-space trajectory; calculating a Lyapunov index of the system model to determine system characteristics; calculating a phase locking value between the pathological modal signal and an acoustic reference feature of a preset voice regulation and control system so as to couple the intensity; determining a target coding mode of each pathological modal signal based on the system characteristics and the coupling strength, and coding the pathological modal signals to generate a corresponding pulse event sequence; and all pulse event sequences are fused based on the phase locking value to generate a time event stream reflecting a pathological regulation mechanism, so that nonlinear pathological components in the pathological speech are effectively extracted, the pathological modality is coded into an event stream structure, and accurate and explainable pathological speech evaluation is supported.

The invention discloses a Hamiltonian theory-based perturbation multi-symplectic numerical solution containing a disturbance system, and belongs to the technical field of numerical calculation of a nonlinear dynamic system. The method comprises the following steps: firstly, establishing a mathematical model containing a disturbance mechanical structure, converting the mathematical model into a Dosin Hamiltonian system, introducing small parameters to unfold the system into a power series form, and decomposing an original system into a series of linear Dosin Hamiltonian equations through a perturbation theory; and carrying out numerical solution on the perturbation equation of each order by adopting a Preissmann Box Dosin difference format, and finally synthesizing a dynamic response solution of the system. According to the method, the influence of small disturbance on long-term dynamic behaviors is effectively captured while the sympathetic structure of the system is maintained, the method has the characteristics of high precision and strong structure maintenance, the method is suitable for dynamic response analysis of disturbance-containing mechanical systems and generalized nonlinear systems, and an efficient and reliable numerical tool is provided for design and optimization of engineering structures such as aircrafts.

The application discloses a kind of light reserve pool computing performance promotion method and system based on deviation physical model, including the error data of deviation physical model of initial data as input data import input layer, mismatch between deviation physical model and reserve pool state is analyzed in reserve pool layer to train output weight, the data between based on deviation physical model and reserve pool is weighted, and output weight is obtained by ridge regression, and prediction data is output by output layer based on output weight.The application combines the scheme of time delay semiconductor laser reserve pool based on deviation physical model and light injection, considers the data-assisted prediction effect of predicting chaotic nonlinear dynamic system, breaks through the instability of single prediction based on deviation physical model and the mutual exclusion relationship between virtual node interval and the number of virtual nodes in single time delay optical reserve pool system of light injection, maintains the prerequisite of high-speed information processing rate of system, and improves the prediction performance of system.

The invention provides a mechanical arm motion system identification method based on an improved RLS algorithm, and relates to the technical field of industrial control process system identification, and the method comprises the following steps: S1, constructing a mechanical arm motion system model; and S2, constructing an identification process of a hierarchical least square algorithm based on unscented Kalman filtering. According to the mechanical arm motion system fractional order modeling and interaction estimation method disclosed by the invention, the problem of low mechanical arm parameter identification precision is solved. According to the method, firstly, a mechanical arm fractional order discrete state space model is built, then an identification process combining unscented Kalman filtering and an improved RLS algorithm is built, the system state and parameters are estimated through interactive iteration of the unscented Kalman filtering and the improved RLS algorithm, and an error monotone decreasing strategy and a self-adaptive forgetting factor optimization algorithm are added. The method is high in convergence speed, high in identification precision, capable of processing noise interference, moderate in calculated amount, capable of achieving online real-time identification, adaptive to the strong nonlinear characteristic of the mechanical arm and capable of being popularized to other fractional order nonlinear dynamic systems.

The invention discloses a depth Koopman modeling method and system fused with differential quadratic programming, and the method comprises the steps: collecting and processing the historical operation data, state data and input data of a nonlinear dynamic system, and dividing the data into a training set and a verification set; a depth Koopman modeling framework integrated with a differentiable quadratic programming layer is constructed; and training the modeling framework by using a historical operation data set, and optimizing to-be-trained parameters through training to finally obtain a dimension raising mapping function of the modeled nonlinear dynamic system and an optimal high-dimensional global linear dynamic model corresponding to the function. According to the method, the dimension raising mapping function can be automatically learned and optimized, and the current optimal high-dimensional global linear dynamic model is calculated in real time by utilizing the differentiable programming layer in the learning process, so that the manual selection process of the dimension raising mapping function with subjectivity and blindness is avoided; and the optimality of the obtained high-dimensional global linear dynamic model can be effectively ensured. Therefore, the modeling precision of the method is effectively improved.

The invention discloses a social network inference method and a security monitoring method, and relates to the technical field of network identification. A social network inference method comprises the following steps: collecting income time sequence characteristic data of all nodes in a game dynamics system; calculating a reference degree sequence formed by all node reference degrees; inhibiting the behavior of the node i, and obtaining new income time sequence characteristic data of all nodes after the node i is inhibited; obtaining a new reference degree sequence based on new income time sequence feature data of all nodes obtained after the suppression of the node i; and comparing a new reference degree sequence obtained after the node i is inhibited with the reference degree sequence of the nodes, finding out the nodes with changed reference degrees so as to obtain network nodes connected with the node i, judging the number of hidden nodes connected with the node i, and finally inferring the network structure of the whole social network. A complex network structure in a nonlinear dynamic system is captured, connection information between nodes is deeply mined, and accuracy and comprehensiveness are improved.

The invention provides a two-part synchronization method and system of a hybrid coupling fuzzy cooperative competition quaternion neural network, and belongs to the field of nonlinear power systems. The method is used for realizing that a cooperative competition hybrid coupling fuzzy quaternion neural network and a leader model can reach a two-part synchronous state under the action of an event triggering sampling machine controller under the non-periodic DoS attack, and comprises the following steps: S1, designing a two-part synchronous controller; s2, introducing the two synchronous controllers into a cooperative competition hybrid coupling fuzzy quaternion neural network model; and S3, designing algorithm iteration, and obtaining the maximum DoS attack rate allowed by the model after introduction of the two controllers. According to the invention, an event triggering sampler controller under the aperiodic DoS attack is designed, so that a cooperative competition hybrid coupling fuzzy quaternion neural network and a leader can reach a two-part synchronization state, and a two-part synchronization error system can reach a stable state under the aperiodic DoS attack; and the method is of great significance to research on dynamics of a nonlinear system.

The invention discloses a piezoelectric ceramic driver control method based on a multi-grid method, and belongs to the technical field of precise driving control. Aiming at the problems of low positioning precision caused by inherent nonlinear characteristics of hysteresis, creep and the like of a piezoelectric ceramic driver, many parameters of a traditional complex model and large calculation amount, the method comprises the following steps: firstly, establishing an electromechanical coupling nonlinear power system model, and accurately converting an original system into a linear system through a feedback linearization technology; then constructing an optimal control problem, deducing a regular equation set by using a Pontryagin minimum principle, and performing time discretization by using an implicit Euler format to ensure numerical stability; and finally, introducing a multi-grid algorithm to efficiently solve the large-scale discrete system. According to the method, the calculation complexity is remarkably reduced while the model precision is reserved, the convergence speed is increased by more than 8 times compared with a traditional iteration method, the robustness to parameter changes is high, a precondition device does not need to be adjusted, the positioning error can be controlled within the range, and the method is suitable for the high-end manufacturing fields such as precise positioning and optical focusing.

This invention relates to the field of engineering machinery safety technology, specifically disclosing a load stability prediction system for telescopic boom forklifts based on the force exerted by the boom. The system collects motion sensing data and stress wave sensing data at the boom hinge; calculates a cross-spectral coherence function based on the motion data to generate a hinge coherence disorder index; performs time-frequency transformation on the stress wave data and automatically identifies energy impact patches, extracting their peak energy, center frequency, and duration; and generates a transient collision intensity spectrum index based on sensitive frequency band mapping and aggregation; constructs a time-series comprehensive feature vector from the two indices, inputs it into a pre-trained multi-layer gated recurrent unit structure for nonlinear dynamic system identification, and outputs a fused stability assessment state quantity; maps this state quantity to a virtual potential energy surface for multi-step forward extrapolation, calculates the dynamic stability margin value, and issues an early warning when the margin is lower than an adaptive threshold; this invention achieves early and accurate prediction of nonlinear jump instability.

The invention relates to a dynamic nonlinear system having a plurality of degrees of freedom. The system has at least one potential element, and eigenmodes of the system are produced by means of the potential element. A potential is produced by means of the at least one potential element, and the potential causes an acceleration tangential to the basic trajectories of the system, a basic trajectory being a trajectory of the potential-free system.

The application discloses a traction system fault detection method based on neighborhood restriction generalized autoencoder and belongs to the technical field of fault diagnosis. In view of defects such as great information loss, insufficient interpretability of potential variables and poor adaptability to nonlinear dynamic systems in existing high-speed train traction system fault detection methods, the method realizes high-precision fault detection through two stages of offline learning and online detection. In the offline learning stage, the Mahalanobis distance is used to determine a sample neighborhood set and weights, a neighborhood restriction generalized autoencoder loss function is constructed by fusing local linear reconstruction error and mutual information regularization terms, and optimal encoders and decoders are trained. Finally, normal state residuals are calculated, and a fault detection threshold is determined based on statistics. In the online detection stage, data are collected in real time and stacked data are constructed, real-time residuals are calculated by using the trained neighborhood restriction generalized autoencoder, and fault detection is realized by comparing statistics and the threshold.

The invention provides a recovery method for autonomous diagnosis of a marine communication link fault, which belongs to the technical field of marine communication, and comprises the following steps: establishing a link stability prediction mechanism based on a nonlinear dynamics theory through four steps of multi-dimensional parameter acquisition, nonlinear dynamic characteristic analysis, stability prediction and early warning and preventive recovery control; the method realizes advanced prediction, early warning and preventive recovery of a maritime communication link fault, and specifically comprises the following steps: acquiring multi-dimensional parameter data of a physical layer, a link layer and a transmission layer; constructing a nonlinear dynamic characteristic model representing a link stability state; calculating a stability index and a fluctuation index based on the hierarchical prediction framework, and generating fault early warning information; according to the method, a maritime communication link is regarded as a nonlinear dynamic system, and measures can be taken before a fault actually occurs.

This invention relates to the fields of computer graphics and image processing technology, specifically to a 3D reconstruction system and method for a 3D unfolded image. The system includes a topological feature extraction module, a folding axis positioning module, a coordinate mapping module, a mesh generation module, an error warning module, and a 3D rendering module. First, it acquires images of the folded paper image and the target unfolded image, extracts topologically invariant feature points, and determines the position parameters of the folding axis in 3D space based on the feature point matching results. It then establishes a mapping relationship from 2D coordinates to 3D coordinates, constructs a triangular mesh, and detects fold lines. The mesh is classified, and the relationship between the normal vector and the center point is analyzed to identify erroneous folding regions. Finally, it generates a 3D model visualization result. Topologically invariant feature extraction and manifold mapping techniques are used to improve the accuracy of feature matching. Lie group transformation theory is introduced to construct an accurate coordinate mapping relationship, and an error warning mechanism based on nonlinear dynamic system theory is developed, effectively improving reconstruction accuracy and efficiency.

The invention relates to the technical field of nonlinear power system uncertainty analysis, in particular to a method for predicting dynamic response of a pumped storage wind power interconnection grid-connected system under the influence of uncertain parameters. Comprising the following steps: S1, establishing a mathematical model reflecting the dynamic characteristics of the pumped storage wind power interconnection grid-connected system; s2, selecting each parameter in the mathematical model constructed in the step S1 as an uncertain parameter; s3, calculating the amplitude distribution of the dynamic response of the system state variable under the influence of the uncertain parameters based on a Fourier amplitude sensitivity test expansion method; s4, calculating uncertainty according to the amplitude obtained in the step S3; s5, predicting the dynamic response of the system based on the calculated uncertainty; and an important theoretical basis is provided for uncertainty analysis and performance optimization of the system.

The invention discloses a position determination method and device in the braking process of a magnetically levitated train based on digital twinning, relates to the technical field of digital twinning of rail transit, and mainly aims to solve the problem that accurate parking positions cannot be guaranteed based on manual observation and operation in the parking process. The method mainly comprises the steps that a linear and nonlinear combined train position forecasting model is constructed, and the output of an unknown nonlinear dynamic system is obtained through forecasting based on a trained online deep learning forecasting model; in response to the train parking preparation instruction, acquiring real-time operation data of the target train; inputting real-time operation data into the linear model, and calculating a linear forecast value; performing prediction processing on the real-time operation data based on the trained online deep learning prediction model to obtain a nonlinear prediction value; and adding the linear forecast value and the nonlinear forecast value to obtain a position forecast value of the target train, and outputting the position forecast value to an interaction terminal. The method is mainly used for determining the train position in the braking process.

The invention discloses a load flow calculation model and method based on track joint optimization and physical information, and belongs to the technical field of electric power. The model comprises a KAN-based physical information model and a trajectory joint optimization algorithm; comprising the following steps: applying an interpretable KAN to load flow calculation; designing a physical constraint loss function to realize embedding of a physical mechanism; adopting a trajectory joint optimization algorithm to convert the training of the KAN into a dynamic system evolution problem; the physical information model based on the KAN is of a double-layer architecture, the first layer calculates a node voltage amplitude, the second layer outputs a voltage phase angle, the physical constraint network layer calculates node power through a power flow network, and an output result is constrained to meet a power balance equation through residual calculation; according to the trajectory joint optimization algorithm, parameters are guided to evolve and converge to an optimal solution along an energy descending direction by constructing a nonlinear dynamic system. According to the invention, the limitation of a black box network can be broken through, the problem of large-scale KAN network training challenge is solved, and the precision is improved.

The invention provides an intelligent gridding region division method and system based on multi-dimensional data analysis, and the method comprises the steps: firstly reconstructing the multi-dimensional dynamic data of each key node in a target power grid into a phase space of a multi-dimensional nonlinear power system; and a dynamic topology hypergraph of the target power grid is constructed based on the dynamic coupling relationship of the node state trajectory. A Hough-Laplacian operator is defined on the dynamic topology hypergraph, and an energy flow field in a target power grid is decomposed into a circulation component and a gradient flow component. Respectively delineating an energy closed-loop oscillation area and a risk potential energy conduction area based on the spatial distribution characteristics of the circulation component and the gradient flow component, and defining the areas as risk grids; and other nodes are clustered to form a stable grid. And finally, combining all the risk grids and the stable grids to complete combination segmentation of the target power grid. The final gridding result can accurately reflect the dynamic response mode and the risk propagation path of the power grid under real disturbance.

The application relates to an echo state network-based adaptive fault-tolerant control method for a non-strict repetitive system, which comprises the following steps: constructing a nonlinear dynamic system with an actuator fault, and setting a hypothesis condition; introducing a desired error trajectory, constructing a dynamic error equation, and defining a nonlinear function in the equation; approximating the nonlinear function by using an echo state network; proposing an adaptive iterative learning fault-tolerant algorithm, combining a barrier Lyapunov function, deducing a control input, constructing a controller, and filtering redundant batches; and constructing a barrier composite energy function to verify the stability and convergence of the designed adaptive iterative learning fault-tolerant algorithm. The nonlinear dynamic system constructed by the application can still realize effective tracking and control of the system state to the desired trajectory under the conditions that the actuator has additive or multiplicative faults, the system state is limited, and external disturbances exist.

The invention discloses a TE process fault diagnosis method based on a deep nonlinear dynamic system comprehensive model, computer equipment and a medium. The TE process fault diagnosis method comprises the following steps: constructing a fault detection model based on an SAE-LDLVS model based on a training set constructed by normal data and fault data of a TE industrial process, determining model parameters, and determining a threshold line; calculating the logarithmic posterior probability of each sample in the test set and comparing the logarithmic posterior probability with a threshold line to realize fault detection; constructing a plurality of fault diagnosis models based on an SAE-LDLVS model based on a training set reconstructed by the normal data and the fault data, and determining model parameters; and calculating posterior probability values of a current test sample in the corresponding test set relative to a plurality of different modes by using the fault diagnosis model, and judging the belonging fault mode by using the maximum value of the posterior probability of the current test sample. The method can effectively extract various features of the data.

The invention discloses a nonlinear dynamic system mechanism modeling method based on multi-scale Koopman and comparative learning, and the method combines a multi-scale Koopman neural operator with physically guided sparse regression and comparative learning, and automatically discovers a control equation from chaotic high-dimensional observation data. Firstly, multi-scale representation is constructed through frequency domain decomposition and Hankel embedding, and linear modeling of nonlinear dynamics is achieved through a Koopman operator. Then, a system derivative is calculated in combination with automatic differential, a candidate function library is constructed, and a sparse explicit form of a control equation is obtained through sparse regression; in the process, a positive sample contrast learning strategy is introduced, and robustness and consistency of potential representation are ensured through enhancement means such as space cutting, noise injection and time disturbance. Aiming at the limitations of strong black box performance, poor interpretability, instability under high noise and the like in the existing method, the method integrates the advantages of operator theory, automatic differentiation, sparse regression and self-supervised learning, can consider precision, physical consistency and generalization, and has important theoretical value and engineering application significance for nonlinear dynamics mechanism discovery.

The invention discloses an FMCW laser ranging light source spectrum degradation suppression method based on strategy online deployment. Belongs to the laser radar field. The objective of the invention is to solve the technical problems of spectrum degradation and measurement precision reduction caused by frequency modulation nonlinearity of an FMCW laser ranging light source. Comprising the following steps: building an FMCW laser nonlinear dynamic system platform, and constructing a deep learning model to simulate a dynamic environment; designing a nonlinear correction process as a Markov decision process, and training a reinforcement learning agent by using a TD3 algorithm; carrying out lightweight processing on an Actor network in the policy network, wherein the lightweight processing comprises pruning and quantization operations so as to reduce the complexity of the model; a lightweight strategy network is deployed on a hardware platform, strategy network forward reasoning is achieved on an FPGA, the strategy network is cascaded with a state extraction module and an action output module to form a closed-loop correction system, and dynamic modulation current optimization is achieved. According to the invention, frequency spectrum degradation caused by frequency modulation nonlinearity can be inhibited on line, and the measurement precision is improved.

The present disclosure relates to a method and device for topology analysis of dynamic complex networks and a storage medium. The method comprises: obtaining observation data of a biological system; performing filtering processing on the observation data to obtain latent state data; determining a time network sequence based on the latent state data, the time network sequence comprising first nodes and first directed edges between the first nodes, the first nodes representing different latent state variables, and the first directed edges representing time sequence relationships and dependency relationships between the first nodes; and determining a topology analysis result based on the time network sequence. According to the embodiments of the present disclosure, high-order regulation structures formed by multiple genes can be revealed, stable latent state estimation of a nonlinear dynamic system (such as the biological system described above) can be performed in a noisy environment, and unified analysis of direction, time evolution and high-order topological characteristics can be achieved, thereby improving the explanation capability of a network with directionality, time variability and complex topological characteristics.

The application discloses a kind of based on nonlinear dynamics multipath fading robustness radiation source signal characterization method, comprising: S1, receive the signal data of radiation source and carry out data preprocessing, obtain signal x (t) ;S2, calculate time delay parameter tau and embedding dimension m, and reconstruct the phase space to the signal x (t) and obtain matrix S;S3, to matrix S is carried out row direction transformation and channel elimination and obtains matrix S4, to matrix is carried out column direction transformation and logarithmic transformation and obtains reconstructed phase space matrix two-dimensional Fourier transform logarithmic spectrum Θ.The application combines the advantages of nonlinear analysis and spectral analysis, designs and constructs the phase space two-dimensional Fourier transform logarithmic frequency spectrum, by the frequency domain transformation of two-dimensional row and column direction between phase points, the characteristic analysis of nonlinear dynamic system is carried out in frequency domain, on the basis of realizing the elimination of channel influence by the frequency domain mean value between cancellation phase points, without channel estimation and channel compensation, and without affecting the measurement of radiation source fingerprint information.

The invention relates to the technical field of parameter sensitivity analysis of a nonlinear power system, in particular to a parameter sensitivity evaluation method of a pumped storage wind power coupling system under multiple application scenes. Comprising the following steps: S1, establishing a mathematical model reflecting the dynamic characteristics of the pumped storage wind power coupling system; s2, selecting an uncertain parameter, and calculating a dynamic process of the uncertain parameter to a sensitivity index of a state variable in a time domain; s3, calculating a first-order sensitivity index and a total sensitivity index; s4, calculating sensitivity evaluation indexes of the system parameters in the plurality of application scenes, wherein the sensitivity evaluation indexes comprise first-order stability sensitivity, total stability sensitivity, maximum first-order average sensitivity, maximum total average sensitivity, first-order stability average sensitivity, total stability average sensitivity, maximum first-order average coupling index and maximum total average coupling index; s5, performing parameter sensitivity analysis on the system; and the parameter sensitivity of the pumped storage wind power coupling system in various operation scenes is comprehensively reflected.

The invention relates to the field of electric power, and discloses a method for evaluating and analyzing the deep peak regulation capability of a coal-fired power generating unit, which is used for introducing a nonlinear dynamic system theory into the field of evaluating the deep peak regulation capability of the coal-fired power generating unit. According to the method, the dynamic behavior of the system is described by using the phase space reconstruction technology, the stability boundary is quantitatively analyzed in combination with the Lyapunov index and the Hamiltonian system theory, and the evaluation model considering the multi-time scale coupling effect is established. The peak regulation limit is accurately recognized from the dynamic characteristic level in the unit, the problems that in an existing method, the mechanism is not clear, system analysis is isolated, and real-time performance is poor are solved, and guarantee is provided for improving the peak regulation capacity of the unit, guaranteeing safe and stable operation of a power grid and promoting renewable energy consumption.

The invention belongs to the technical field of system control, and particularly relates to a composite interference compensation control method based on online learning and multi-model fusion, which comprises the following steps: S1, establishing a discrete time state space model of a nonlinear dynamic system influenced by composite interference; s2, designing a nonlinear interference observer, and estimating lumped interference suffered by the system on line in real time; s3, decomposing the lumped interference estimated value in real time to obtain a plurality of interference components with different dynamic characteristics; s4, aiming at the dynamic characteristics of each type of interference components obtained by decomposition, designing a corresponding special compensator, and forming a parallel multi-model compensation architecture; s5, dynamically fusing the outputs of the three compensators according to the current interference characteristics through an intelligent arbiter, and generating a composite interference compensation control vector; and S6, the composite interference compensation control vector and the nominal control vector are added to obtain a final total control input vector, and the final total control input vector is applied to a controlled object after being subjected to amplitude limiting processing of an execution mechanism.