407 results about "State variable" patented technology

The invention relates to the technical field of industrial control, in particular to an industrial stacker intelligent control system based on machine vision, and the system comprises a state resolving module which is used for carrying out the feature region decoupling of original observation data, and resolving a position deviation matrix and a deformation state variable for a rigid subspace and a flexible subspace respectively; the dynamic modeling module is used for identifying the first-order inherent frequency and the damping ratio of the mechanical structure on line and establishing a second-order impedance contact model; the impedance control module is used for generating an expected speed based on the position deviation and adaptively adjusting an impedance parameter according to a deformation state so as to carry out compliant modulation on a speed vector; and the estimation shaping module is used for correcting system time lag by using a Kalman observer and inhibiting residual vibration in combination with a zero-vibration input shaper. Through multi-dimensional physical field fusion and dynamic modeling, the problem that a rigid control model is mismatched with a flexible operation object is solved, and self-adaptive compliant control and vibration-free accurate operation of the stacking machine in the unstructured environment are achieved.

The invention discloses an engine state estimation and system modeling correction method based on double-layer variational inference, which relates to the field of engine state estimation and comprises a variational inference stage aiming at component performance states and kinetic model parameters; a system output prediction stage based on an observation equation; and a solving stage of performing objective function optimization through an evidence lower bound. The structure clearly presents information flow and key calculation links of the proposed algorithm in state estimation and model learning. According to the method, combined reasoning of state variables and model parameters is achieved by building a probability modeling structure, the modeling problem when system dynamics is partially or completely unknown is solved by combining a modeling method of a stochastic differential equation, and while the state variables and the model parameters are optimized, the modeling efficiency is improved. Precise inference of component states and reliable identification of fault features are achieved, the fault detection accuracy of sudden gas circuit abnormity reaches the standard, and meanwhile the performance is better in the aspect of tracking long-term performance degradation.

The invention discloses a PMSM (permanent magnet synchronous motor) control method for a mining electric locomotive. The method comprises a cooperative control framework of a fractional order sliding mode controller and a nonlinear extended state observer. Wherein the fractional order sliding mode controller constructs a novel sliding mode surface by introducing a terminal factor and a fractional order calculus theory, and introduces a state variable to design an adaptive fractional order reaching law, so that buffeting is effectively inhibited and controlled while system error convergence is accelerated; and the nonlinear extended state observer is used for estimating the uncertainty of a system model and the disturbance of an external load in real time, and introducing the uncertainty and the disturbance as feedforward compensation quantity into a control loop, so that the anti-interference capability of the system is remarkably enhanced. Through deep fusion of the fractional calculus theory, the sliding mode control and the nonlinear extended state observer, the problems of large buffeting, slow response, poor anti-interference capability and the like of a traditional control method of the mining electric locomotive under complex working conditions are solved, and the dynamic and steady state performance and the control precision of the system are remarkably improved.

The invention relates to the technical field of geotechnical engineering and geological disaster monitoring, in particular to a rockburst tendency dynamic discrimination method, and aims to solve the limitation caused by the fact that a dynamic evolution process is simplified into a quasi-static attribute in traditional rockburst tendency evaluation. According to the method, rockburst tendency is defined as a hidden state variable evolved along with time, a physical constraint state space model containing a hidden state kinetic equation and a multi-modal observation equation is constructed, continuous stress-strain and discrete acoustic emission data are fused, hidden state probability distribution is estimated in real time through online Bayesian inference, and the probability distribution of the hidden state is estimated in real time. And in combination with adaptive information weight updating and physical consistency constraint, predicting a future state trajectory, calculating a risk probability exceeding a critical threshold, and outputting a dynamic rockburst tendency level. According to the scheme, continuous, dynamic and prospective judgment of the rockburst tendency is realized, and the accuracy and reliability of early warning are improved.

The invention discloses a confidence embedded model predictive control method, a medium and equipment, and belongs to the technical field of suspension control. According to the method, a kinetic equation of a suspension system is established, a data-mechanism dual-drive uncertainty quantification method is adopted, original operation data of a maglev train and suspension state information measured on line are utilized, and dynamic characteristic learning modeling is conducted on the suspension system under the constraint of the kinetic equation; state quantity distribution of suspension air gaps, vertical speed, electromagnet current and the like at future moments is predicted, and prediction mean values and prediction standard deviations of corresponding states are obtained. In model prediction control optimization, a nominal trajectory of the suspension system is constructed by a predicted state mean value, and a penalty term proportional to prediction uncertainty is added into a cost function; meanwhile, confidence coefficient constraint is constructed based on the predicted standard deviation, and dynamic constraint tightening of key physical quantities such as suspension air gaps, vertical speed and coil current is achieved. According to the invention, real-time sensing and self-adaptive closed-loop control of the operation risk of the suspension system are realized.

The invention relates to the technical field of temperature control, in particular to a multi-dimensional compensation ablation system temperature control system, method and device and a medium. The system calculates a medium texture index representing the structural change of the load medium by synchronously acquiring electrical parameters and ultrasonic image data; constructing an anti-interference control model comprising an extended state observer, and defining energy transmission loss caused by contact thermal resistance as an extended state variable; calculating an adaptive correction factor based on the texture index, dynamically correcting the state updating process of the observer to obtain an energy loss correction estimated value, and constructing a compensation control model to adjust the output power according to the energy loss correction estimated value; according to the invention, the technical problem of incomplete heating caused by false high temperature of the sensor and system misjudgment due to state deterioration of the contact interface is effectively solved; accurate reconstruction and closed-loop compensation of effective heat flow in a non-ideal contact environment are realized, sufficient energy penetration can still be maintained even if contact thermal resistance is sharply increased, and the integrity of an ablation area is guaranteed.

The invention discloses an engineering measurement error dynamic correction method based on multi-sensor data fusion, and relates to the technical field of engineering measurement. Three error sources of sensor physical characteristics, environmental interference and manual operation are defined as estimable state variables, and differentiation processing is performed according to different characteristics of errors: a dynamic model is established to embed the sensor and environmental errors into a state space, so that real-time compensation based on the model is realized; according to the scheme, a gross error recognition mechanism based on data-driven statistical characteristics is established, real-time diagnosis and processing are carried out on personal errors, joint optimal estimation is carried out on unified state vectors by adopting a recursive estimation algorithm, and high-precision corrected physical quantities and estimated values of all error states are synchronously output. The measurement precision and reliability are obviously improved; by means of robust processing of gross errors, stability of the system in a non-ideal environment and online self-diagnosis and health monitoring of the measurement system are enhanced.

The invention relates to a PTFE extrusion model prediction control method and system based on a time convolution network. The method comprises the steps of collecting multi-dimensional time sequence data of an operation variable, a state variable and a controlled variable of a pushing production line in real time; performing data preprocessing; constructing a supervised learning sample by adopting a sliding time window; training the time convolution network; constructing a cost function, and setting constraint conditions; acquiring the actual die orifice outer diameter, the traction speed, the pushing speed and the pushing pressure at the current moment, and inputting the actual die orifice outer diameter, the traction speed, the pushing speed and the pushing pressure into the trained time convolutional network to obtain a prediction result of the future die orifice outer diameter; based on the prediction result, solving the cost function to obtain an optimal control sequence used for offsetting the die orifice outer diameter deviation caused by abnormal fluctuation of the pushing pressure; real-time data are collected again, the prediction and optimization process is repeated, and rolling prediction and optimization control are achieved. The problem that the outer diameter is out of tolerance due to fluctuation of raw materials is solved, and unmanned and intelligent production is achieved.

The invention relates to the field of nonlinear system modeling, and particularly provides a global linear modeling method and system for a nonlinear system based on Lie derivative and sparse recognition, a terminal and a medium, and the method comprises the steps: obtaining an explicit nonlinear kinetic equation of a target system, calculating the Lie derivative of a state variable or an output variable of the system based on the kinetic equation, constructing a candidate observation function library directly associated with the physical mechanism of the system; performing screening and dimension reduction on the candidate observation function library by adopting a sparse recognition method to obtain a low-dimensional observation function set; and on the basis of the screened observation function and system operation data, a finite-dimensional global linear model of the target system in the dimension raising observation space is obtained through identification by using a data driving algorithm. According to the method, the online solving efficiency and the control real-time performance are improved, and efficient and reliable multi-target collaborative optimization control of systems such as a wind driven generator is realized.

The invention relates to the technical field of coal-fired unit energy flow analysis and prediction, in particular to a coal-fired unit boiler energy flow analysis and prediction method under rapid variable load, which specifically comprises the following steps of: firstly, combining a thermodynamic physical framework with a recurrent neural network, and representing a function relationship between a time-varying parameter and a state variable in a mechanism model through a neural unit; rapid fluctuation of energy in the boiler is accurately captured; secondly, a multi-mode switching strategy based on an attention mechanism is introduced, flexible switching is carried out between a strong coupling state and a weak coupling state, and the prediction precision under high-speed load change is guaranteed; and finally, introducing an error compensation model, and dynamically correcting a hybrid model prediction residual error. According to the method, physical constraints of mechanism modeling, multi-mode switching of an attention mechanism and an error compensation model are integrated and fused, so that the problems that most of hybrid modeling studies in the prior art mainly focus on steady-state or finite transient prediction, and studies on energy flow dynamic characteristics under rapid load change are still relatively deficient are solved.

The invention provides a passenger compartment air conditioner control method based on model predictive control. The method comprises the steps that a passenger compartment model is constructed, and an original data set containing environment parameters, air conditioner control parameters and passenger compartment response parameters is obtained through experimental design; preprocessing the data; a neural network model is trained through the preprocessed data and serves as a prediction model to learn the nonlinear mapping relation among the air conditioner operation parameters, the environment parameters and the passenger compartment states; calling the prediction model to execute MPC rolling prediction, and generating a future state variable sequence; constructing a target function containing a state tracking error and control energy cost, obtaining an optimal control sequence through optimization solution, and acting the first control quantity on an actuator; and dynamically adjusting a prediction model or a control parameter according to real-time feedback. The technical problems that physical modeling is difficult due to high nonlinearity of a passenger compartment air conditioning system, and a traditional control strategy cannot give consideration to high-precision prediction and multi-working-condition adaptability can be solved.

The invention discloses a thermodynamic system analysis method and system based on a physical information neural network. The method comprises the following steps: constructing a thermodynamic system mathematical model; converting a solving problem of the mathematical model of the thermodynamic system into an optimization problem taking a minimum control equation set residual error as a target function; constructing a physical information neural network; constructing a composite loss function based on the control equation set residual sum of squares, and performing end-to-end training on the physical information neural network to obtain a trained physical information neural network; taking the trained physical information neural network as an approximate solver, and for any given thermodynamic system input working condition, predicting a group of approximate solutions as initial predicted values through one-time forward propagation; and inputting the initial predicted value into a traditional numerical solver for accurate solving to obtain a to-be-solved state variable. According to the method, a feasible technical path is provided for applications with strict requirements on the analysis and calculation speed of the thermodynamic system, such as real-time simulation, online optimization and digital twinning.

The invention relates to a spacecraft collaborative formation safety control method and system based on an adjustable self-adaptive control barrier function. The method comprises the following steps: establishing a six-degree-of-freedom model for describing relative motion of a spacecraft formation; multiple safety key constraints which need to be met by the spacecraft formation are established, and the constraints are formalized into a zero-hyperlevel set function of a state variable; a disturbance observer is designed, and unknown lumped disturbance existing in the six-degree-of-freedom model is estimated and compensated online; a safety controller is constructed, the safety controller comprises an adjustable self-adaptive control barrier function used for generating a safety constraint boundary changing along with time, and parameters of the adjustable self-adaptive control barrier function are adjusted online through the auxiliary dynamic system; and constructing a quadratic programming problem, and solving to obtain an optimal safety control instruction which minimizes the deviation between the actual control input and the nominal control input. Compared with the prior art, the method has the advantages of improving the task success rate and safety of the spacecraft formation in a complex, dynamic and uncertain environment and the like.

The invention belongs to the field of vehicle trajectory tracking control, and discloses an intelligent vehicle trajectory tracking control method and system based on differential programming, and the method comprises the steps: constructing a vehicle dynamics model; a reference trajectory time-varying curvature is introduced, delay of a steering execution mechanism is expressed as pure delay plus a first-order inertia link, and construction of a road tracking model is completed; assuming that the system is in a steady state, calculating reference values of a state quantity and a control quantity; constructing an MPC optimization problem, converting the MPC optimization problem into a QP problem, and calculating to obtain an MPC loss value; constructing a differential programming training calculation network diagram; a hypercube is used for sampling the state quantity of a vehicle, the driving speed and the time-varying reference items of a road, and construction of a network training initial state data set is completed; carrying out MPC trajectory tracking task integration, and constructing a physical model in network training; using a full-link layer network, adding tire slip angle soft constraints, completing the construction of a strategy network, carrying out end-to-end network training, and completing the control of a turning angle instruction. According to the method, the MPC online solving optimization problem can be avoided.

The invention provides a control method and system for a five-degree-of-freedom marine crane under a non-inertial system, and relates to the technical field of automatic control of cranes. The method comprises the following steps: acquiring a real-time state variable of the crane under a non-inertial system; the state variables are input into a self-adaptive radial basis function neural network, a lumped disturbance estimated value is obtained, and the neural network is designed based on a five-degree-of-freedom dynamical model established by utilizing a Lagrange equation and a virtual work principle under a ship coordinate system and considering ship six-degree-of-freedom motion and installation position offset; calculating a tracking error function according to the state variable and the expected trajectory; and inputting the disturbance estimation value and the error function into a feedback controller, calculating to generate a control torque, and outputting the control torque to an execution mechanism to drive the crane to track an expected trajectory and suppress load swing. According to the method, direct modeling and control under a non-inertial system are realized, the problems of coordinate transformation disturbance and singularity caused by inertial system modeling are solved, and the trajectory tracking precision and the anti-interference robustness are improved.

The invention relates to the technical field of power system simulation, and discloses a power system time sequence simulation system considering security constraints. The system comprises a power grid topology analysis unit, a dynamic security domain calculation unit, a time sequence constraint modeling unit, a simulation track generation unit and a check feedback unit. The power grid topology analysis unit obtains element connection relations and electrical parameters, and generates a topology description file containing a node admittance matrix; the dynamic safety domain calculation unit obtains the voltage stability limit and the power angle stability boundary of each working condition, and forms a safety domain boundary set; a time sequence constraint modeling unit combines the set with a predefined time interval to build a security constraint time sequence model containing a transient process; the simulation trajectory generation unit solves a differential algebraic equation set by using an implicit trapezoidal integral method, and outputs a state variable trajectory with a timestamp; and the check feedback unit compares the track with the security domain boundary, marks an abnormal segment and generates a correction instruction. The system realizes fusion of security constraint and time sequence simulation.

An anomaly detection method includes determining state variables of an industrial control system based on a system design of the industrial control system; determining invariants governing the state variables based on the system design; receiving historical measurement data of the state variables of each invariant from the industrial control system; constructing a set of behavioural models for each invariant using a set of machine learning algorithms and the historical measurement data of the respective state variables, the behavioural models representing normal behaviour of the respective state variables; predicting measurement data of the state variables of each invariant using the behavioural models and the historical measurement data of the respective state variables; receiving current measurement data of the state variables during operation of the industrial control system; and detecting the anomalies based on deviations between the current measurement data and predicted measurement data of the state variables of each invariant.

According to the electromechanical transient estimation method and system based on robust H infinite unscented particle filtering, on the basis of a dynamic estimation model of a generator, unscented particle filtering iteration solution is adopted; determining an estimation error covariance matrix by using a positive scalar parameter of a system uncertainty error defined based on a robust H infinity theory, a state prediction error covariance matrix and a cross covariance matrix of a predicted value and a measured value, and updating a Kalman gain; based on the state variable, estimating an error covariance matrix to establish suggested distribution, and resampling from the suggested distribution to obtain a new particle set; updating a dynamic estimation model of the generator by using the corrected system noise covariance matrix and the observation noise covariance matrix, and solving an obtained state variable estimation value by adopting unscented particle filter iteration; taking the state variable estimation value and the weighted mean value of the weight thereof as an electromechanical transient estimation result of the generator; the problems of accuracy and robustness caused by communication noise and uncertainty of model parameters in the dynamic estimation process of the generator are solved.

The invention discloses a heavy-load mechanical arm obstacle function adaptive sliding mode control method and device based on a disturbance observer, and the method comprises the steps: building a dynamic model which considers the model error, an unmodeled part, friction and lumped disturbance caused by an external load; converting a lumped disturbance estimation problem into a linear system state estimation problem of defined auxiliary state variables and unknown input driving, and designing a sliding mode disturbance observer used for estimating and compensating lumped disturbance; for a disturbance estimation error, a bounded adaptive control gain of a controller is designed by adopting a barrier function, and the error is kept within a preset range while control jitter is avoided. Through systematic control architecture design, the key problem that high-precision tracking and smoothness control of the excavator in a complex working environment are difficult to consider at the same time is effectively solved.

The invention provides a permanent magnet synchronous motor rotating speed control method and system, and belongs to the technical field of motor control. The method comprises the following steps: firstly, constructing a discrete time mathematical model of a motor and an external source system comprising a lumped reference signal and lumped interference, and establishing a composite system model according to the discrete time mathematical model; then, constructing a dimensionality reduction observer to estimate an external source signal, and performing state and input transformation in combination with a regulator equation; thirdly, constructing a constraint set of the transformation system, and decomposing a state variable into a nominal component, a deterministic error component and an uncertain error component; a time-varying constraint tightening set is dynamically constructed according to the error, a model prediction control optimization problem with dynamic tightening constraint is solved online, and a nominal control quantity is obtained; and finally, combining the nominal control quantity and the estimated external source signal to synthesize an actual control voltage. According to the method, interference can be effectively suppressed, system security constraints are strictly ensured, recursion feasibility and closed-loop stability of control are ensured, and the dynamic response speed is increased.

This application provides an automatic parking trajectory tracking control method, storage medium, and electronic device. The method includes: determining the current nearest point and shift point closest to the origin of the vehicle coordinate system from a sequence of discrete trajectory coordinate points; determining at least one preview point based on the current nearest point, and obtaining the vehicle's lateral deviation and heading angle deviation based on the preview point; establishing kinematic equations with lateral deviation and heading angle deviation as state variables, and obtaining discretized state-space equations; constructing an offline parameter table of different vehicle speeds and corresponding state feedback values ​​based on the discretized state-space equations and a preset linear quadratic regulator; obtaining the corresponding vehicle rotation value according to the real-time vehicle speed and the offline parameter table; and performing automatic parking control based on the gear information of the current nearest point and the current vehicle rotation value. This application effectively solves the technical problem of difficulty in simultaneously achieving accuracy, real-time performance, and robustness in existing automatic parking trajectory tracking control methods.

This application provides a vehicle control method, device, electronic device, and medium, relating to the field of autonomous driving technology. It constructs a three-part loss function comprising decision variables, state variables, and control variables, and sums the outputs of these three components at each time step to obtain a first sum. The first sums from multiple time steps are then accumulated to form a unified target sum. Under the constraints of a first vehicle's dynamic mathematical model and first constraints, a sequence of driving variables that minimizes this target sum is jointly solved. Finally, the vehicle is directly controlled using this optimal target driving variable sequence. This effectively bridges the coupling channel between discrete decision-making (i.e., lane decision) and continuous trajectory planning, enabling lane decision-making and trajectory generation to work synchronously and collaboratively under the same optimization objective. This improves the safety, driving efficiency, and ride comfort of autonomous vehicles in complex traffic scenarios, achieving superior overall driving performance compared to traditional fragmented algorithm designs.

The invention discloses a high-speed aerodynamic derivative analysis modeling method and system based on symbolic regression, and belongs to the technical field of aircraft aerodynamic modeling and artificial intelligence cross, and the method comprises the steps: obtaining an aerodynamic data set of a high-speed aircraft, and carrying out the preprocessing; defining grammar for generating candidate symbol expressions and constructing an expression tree; modeling based on the training set by adopting a layered symbol regression framework to obtain an analytical model; the prediction precision of the analytical model is verified on the test set, and physical consistency analysis is carried out; carrying out uncertainty quantification on the analytical model to obtain predicted uncertainty estimation of the analytical model; and finally, outputting an analytic model in a mathematical expression form for describing the relationship between the aerodynamic derivative and the flight state variable and uncertainty estimation of the analytic model. The method overcomes the defects of a traditional method in precision, generalization and interpretability, realizes full-automatic efficient modeling from data to the analytic model, and improves the modeling efficiency. The method is suitable for control design, stability analysis and real-time simulation of the high-speed aircraft.

The invention discloses a data center intelligent liquid cooling system based on multi-source data fusion and a control method thereof, and relates to the field of intelligent control. The method comprises the following steps: constructing a feature vector of each water cycle; performing clustering analysis on the feature vectors, and classifying the water circulation according to a clustering result; an optimization control model of the clustering category to which the water circulation belongs is constructed, and the optimization control model takes the characteristic parameters as state variables, takes the water inlet temperature adjusting quantity and the water inlet quantity adjusting quantity as control variables, and takes the minimum equipment temperature difference and energy consumption as optimization targets; performing optimization control on the control variables by using a model predictive control algorithm; solving the optimization control model by using a nonlinear optimization solver to obtain an optimal control law; according to the optimal control law, the water inlet temperature and the water inlet amount of each water circulation are adjusted, so that the equipment temperature in the same water circulation is kept at the same level, the equipment temperature uniformity is improved, the PUE value of the data center is reduced, and compared with a traditional liquid cooling system, more energy is saved.

To predict a state quantity accurately while keeping a cost down using a prediction model obtained by adjusting a learned prediction model corresponding to another plant.SOLUTION: A state quantity prediction device predicts a state quantity of a plant corresponding to an input parameter using a prediction model including a machine learning model and a physical model. The state quantity prediction device stores a first prediction model corresponding to a first plant, creates a second prediction model corresponding to a second plant by adjusting the first prediction model using measurement data of the second plant, and predicts a state quantity of the second plant using the second prediction model. The creation of the second prediction model is performed by regularizing and learning a difference between machine learning parameters of the first prediction model and the second prediction model.SELECTED DRAWING: Figure 1

The embodiment of the invention discloses an extended target detection method, belongs to the technical field of radar detection, and solves the problem that the detection performance is reduced due to the fact that excessive noise is introduced or target energy is omitted during energy accumulation in an existing extended target detection method. Comprising the following steps: constructing a dynamic planning accumulation detection model of a distance extension target based on an echo matrix; on the basis of the minimum energy loss criterion, balancing parameters are configured for the value function, a double-probability joint constraint optimization model about the target energy and the noise energy is established, and the balancing parameters are determined by solving the optimization model; updating a value function and a state transition matrix of the joint state variable among the plurality of pulses through recursive operation based on the trade-off parameter; and for the value function obtained after updating is completed, extracting a corresponding energy ridge line, constructing an approximate probability model of the variation amplitude of the energy ridge line, and obtaining a target extension length value by solving an optimal variation threshold of the approximate probability model under a preset false alarm probability.

The invention relates to a multi-aircraft control method and system considering thrust saturation of a piston engine, and the method comprises the steps: carrying out the dynamic modeling of a cruise type guidance aircraft, and obtaining a kinematical equation and a kinetic equation under a trajectory system; modeling a multi-aircraft system to obtain a leader following type formation control model; an average value model is established for the two-stroke piston engine, a transfer function is obtained through linear processing, and a rotating speed controller is designed based on the active disturbance rejection control theory; constructing a non-singular terminal sliding mode surface matrix with fixed time convergence according to the formation error state; taking the deviation between the actual control input and the expected control input as a state quantity to construct a saturation suppression system; and combining a saturation suppression system with the nonsingular terminal sliding mode surface matrix, and designing an anti-saturation fixed time convergence nonsingular terminal sliding mode formation control law, so that the formation error is converged to zero within fixed time. The formation time can be shortened, and the formation control precision is improved.

The present application belongs to the technical field of power system, and particularly relates to a time-delay power system stability analysis method and system. The method firstly establishes a load frequency control system model including time delay and nonlinear load disturbance, wherein the nonlinear load disturbance is a nonlinear disturbance taking unknown exogenous load disturbance as current and delayed state variable; then an augmented Lyapunov-Krasovskii functional is constructed, and an analysis method based on time-delay dependent matrix free matrix integral inequality and anti-convex combination inequality is adopted to analyze the stability of the load frequency control system, so as to obtain the stability criterion of the load frequency control system. The present application plays an important role in improving the calculation accuracy and time delay upper limit, and is helpful to expand the stable operation area of the power system.

The application discloses a mover motion control method and device, equipment and storage medium, and relates to the technical field of magnetic suspension. The method comprises the following steps: a mover motion model is established according to Newton's second law; a state space equation is determined according to the mover motion model and preset state variables; the preset state variables comprise the position of the mover and the speed of the mover; the state space equation is subjected to discretization processing to obtain a mover motion model in a discrete space; the mover motion result at each prediction time is determined according to a preset prediction step and the mover motion model in the discrete space; a cost function is determined according to the mover motion result at each prediction time; the cost function is used for representing the position deviation of the mover and / or the current consumption; the cost function is subjected to optimization processing to obtain a current output value, and the mover motion is controlled according to the current output value. Therefore, the technical problem that the control of the motor has relatively low efficiency in the related art can be solved.

The invention discloses a permanent magnet synchronous motor full-order active-disturbance-rejection control method based on a disturbance differential predictor, and aims to solve the problem of disturbance compensation lag of traditional active-disturbance-rejection control. Comprising the following steps: establishing a d-q coordinate system mathematical model of the permanent magnet synchronous motor and defining total disturbance; designing a full-order expansion state observer, inputting the converted current, voltage and mechanical angular velocity, and outputting a system state variable and a total disturbance observation value; inputting the total disturbance observation value into an original disturbance differential estimator, and outputting a total disturbance first-order differential estimation signal through backward differential discretization; combining the current tracking error, the total disturbance observation value and the differential estimation signal to design a linear state error feedback control law, and generating a control voltage; a rotating speed-current double closed loop is constructed, and inverter driving signals are generated through inverse transformation and SVPWM to drive a motor. According to the invention, the conversion from feedback compensation to feed-forward suppression is realized, the compensation lag is solved, the dynamic performance and robustness are improved, and the structure is clear and easy to realize.