723 results about "State variable" patented technology

The invention discloses a forklift operation control optimization method and system based on a disturbance observation technology, and relates to the technical field of electric forklift control, and the method comprises the steps: collecting the operation data of a forklift operation process in real time, sensing and fusing through multi-source sensing information, analyzing the state variable of forklift movement, and building a dynamic parameter mapping relation; taking the measured value and the predicted value of the forklift all-condition dynamic model as input, constructing a high-order sliding-mode observer, evaluating disturbance source interference, and generating a motion compensation correction amount; a feed-forward-feedback fused composite controller is constructed, a yawing moment pre-compensation instruction is generated based on a disturbance source disturbance evaluation result, and a forklift trajectory tracking error is dynamically corrected; and monitoring the distance between the operation point of the forklift and the instability boundary in real time, and triggering a torque redistribution strategy when the distance is close to the instability boundary. Through deep fusion of real-time sensing data, dynamic modeling and an intelligent control algorithm, the working performance of the forklift under complex working conditions is remarkably improved.

The invention discloses a system and a method for analyzing stability and oscillation transmission of a network-forming converter system, and belongs to the technical field of virtual synchronous generator control, and the system comprises an active power control loop which takes an active power deviation value as input, and outputs a power angle of a VSG system through an active integration part and a feedback part; the reactive power control loop takes the reactive power deviation ratio as input, and outputs the voltage amplitude of the VSG system through an active integration part and a feedback part; according to the analysis method, whether each state variable is stable or not is judged firstly, and then the intensity of oscillation transmission between the state variables is judged. According to the method, the stability of the system can be evaluated more accurately, and the accuracy of system stability analysis under dynamic disturbance is ensured; an oscillation transfer effect function is introduced, and the mutual influence between different state variables is quantitatively analyzed; and the dynamic fluctuation range of each state variable in the system can be accurately evaluated.

The invention provides an overhead and portal crane lifting appliance control method based on multi-stage model predictive control, and relates to the field of data processing. The method comprises the following steps: firstly, acquiring a control parameter and an initial state variable so as to define an operation boundary and an initial state, and establishing a dynamic model containing a coupling relationship among a trolley acceleration, a sling rope length acceleration and a sling swing angle; acquiring obstacle distribution by using space sensing equipment, and calculating the dynamic safety height of the lifting appliance by combining obstacle parameters and control parameters; judging whether the lifting appliance is in an obstacle avoidance stage or a target tracking stage according to the initial state variable and the dynamic safety height; constructing a model prediction controller and dynamically adjusting a state weight matrix and a control input weight matrix; finally, a control input sequence is generated through an optimization algorithm, and track safety and attitude stability control of the lifting appliance is achieved. According to the technical scheme, the swing angle of the lifting appliance is restrained, meanwhile, the obstacles are effectively avoided, and the safety of the lifting appliance in the whole operation process is guaranteed.

The invention relates to the technical field of industrial mechanism models, in particular to a shock absorber performance optimization control method based on model fusion, which comprises the following steps: extracting a low-frequency disturbance state variable and inputting the variable-topology industrial mechanism model to generate a nominal reference state trajectory; utilizing a depth state observation network fused with energy passivity constraint to calculate a non-linear model mismatch compensation amount and an adaptive weighting parameter; performing dynamic fusion on the nominal reference state trajectory and the compensation amount based on the adaptive weighting parameter to generate a generalized state estimation value; and executing dynamic multi-objective optimization based on the generalized state estimation value, and generating mixed mode control input acting on an execution end. According to the invention, through adaptive fusion of a mechanism model and a data driving method, physical consistency, calculation real-time performance and robustness of a control process are considered.

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 relates to a coaxial multi-rotor unmanned aerial vehicle control method based on a Koopman operator, and the method comprises the steps: constructing a deep neural network based on the Koopman operator, and mapping a state variable to a high-dimensional observable space through an encoder; in a high-dimensional observable space, state variables are predicted through a linear connection layer based on dimension rising state variables and rotor rotating speeds; the decoder restores the prediction result to an original state space; training a deep neural network by using historical flight data to obtain codec parameters and a weight of a linear connection layer, and constructing a linear dynamic model of the observation function and the unmanned aerial vehicle; and the prediction controller predicts state variables of future multiple steps of the unmanned aerial vehicle by using a linear dynamics model, and enables the unmanned aerial vehicle to track a given trajectory by minimizing a cost function to obtain optimal control input. Compared with the prior art, the method has the advantages that complete data-driven modeling is realized, and the method can adapt to unmanned aerial vehicles with different sizes, loads and flight conditions.

The invention relates to the technical field of industrial control systems, in particular to an ink path control system of a full-color printer, which comprises the following specific implementation steps of: measuring a plurality of physical quantities in a controlled physical process in real time, and outputting the physical quantities as state variables and external disturbance signals; receiving a state variable and an external disturbance signal, performing future state prediction through a nonlinear dynamic process model, and generating a prediction result; constructing a process performance index function, optimizing the performance index function through a sparrow search optimization algorithm based on a prediction result, and generating an optimal target set value; through a model based on Bayesian reasoning, parameters of a PID control law are updated in a self-adaptive mode according to external disturbance signals; the controller is used for receiving the optimal target set value and the adaptive PID control law parameters, executing the PID control law in combination with a preset undisturbed switching module, and generating a final control instruction; and receiving a final control instruction, and converting the final control instruction into driving signals acting on a plurality of physical actuators so as to realize closed-loop feedback control of the controlled physical process.

The invention discloses an LSTM (Long Short Term Memory)-based harmonic frequency coupling model optimization method for a network construction type converter, which considers the influence of a dead zone and establishes a converter model controlled by a virtual synchronous generator based on a harmonic state space method. In the power electronic modeling process, due to approximate simplification, switch neglecting and other nonlinear relations, the model is not accurate enough, meanwhile, in the harmonic state space model modeling process, system state variable parameters need to be considered, and the precision of the model affects harmonic transmission characteristics and coupling analysis, so that the modeling efficiency is greatly improved. Errors caused by power electronic modeling are reduced through an LSTM neural network algorithm, the harmonic amplitude estimation difference is reduced, state variable parameter values in a system model are accurately estimated, the model precision is further improved, and then the harmonic coupling condition under the dead zone influence is effectively and accurately analyzed.

The invention discloses an online self-calibration filtering method for inertial navigation, and relates to the technical field of inertial navigation, and the method comprises the following steps: obtaining multi-modal data, and establishing an inertial device error mathematical model and an inertial navigation system error mathematical model; carrying out recursive estimation on the state variables by the extended Kalman filtering model to obtain error parameters of the inertial device and the navigation system; constructing a fuzzy adaptive model based on the error parameters, generating an observation noise correction factor through a fuzzy logic rule, and correcting an observation noise covariance matrix in the extended Kalman filtering model in real time; carrying out online estimation and compensation on error parameters of the inertial navigation system by utilizing an extended Kalman filtering model, and outputting compensation parameters containing zero offset and scale factors in real time; correcting the original data based on the compensation parameters, injecting the original data into the navigation solution, and updating the fuzzy adaptive model according to the compensated error data.

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 discloses a permanent magnet synchronous motor full-speed-domain position-sensorless hybrid control method and a permanent magnet synchronous motor full-speed-domain position-sensorless hybrid control system, and the permanent magnet synchronous motor full-speed-domain position-sensorless hybrid control method is constructed by taking a permanent magnet synchronous motor nonlinear dynamic system model as a starting point. A hybrid control method combining high-frequency pulsating square wave signal injection under a linear dynamic system model, a rotor polarity judgment and rotor initial position estimation algorithm and an unscented Kalman filtering extension algorithm in an additive noise form under a nonlinear discrete dynamic system model is adopted; and obtaining the optimal estimation observation value of the system state variable in the static, low-speed, medium-speed and high-speed full-speed domain range under the condition that the permanent magnet synchronous motor is not provided with a mechanical position sensor. The optimal estimation observation value is used for compensating and feeding back a permanent magnet synchronous motor drive control system adjusting strategy, disturbance of load torque is improved, the noise amount and disturbance amount introduced by three-phase current measurement are eliminated, and efficient and accurate control operation of the permanent magnet synchronous motor is achieved.

The invention discloses a deep neural network probabilistic load flow calculation method and system, and belongs to the field of power system analysis. The method comprises the following steps: acquiring power system parameters and confidence levels, and determining state variables and orders; establishing and linearizing a nonlinear power flow equation to obtain a Jacobian matrix; a node voltage vector is used as input, branch admittance is used as physical information to be embedded into a deep neural network model based on a multi-head self-attention mechanism and semi-invariant guidance, a Jacobian matrix is solved, and a sensitivity matrix is calculated; calculating and aggregating semi-invariants, and transmitting the semi-invariants to a state variable; and after standardization, obtaining a distribution function by using a six-order Cornish-Fisher series, and calculating a fluctuation interval in combination with a confidence level under opportunity constraint to realize quantitative evaluation. According to the method, the probability load flow calculation complexity is reduced, the limitation of a traditional method in processing the network topology complexity problem is solved, and quantitative evaluation of the probability load flow is realized.

The invention discloses a power distribution network state estimation method and device based on high-order volume Kalman filtering, and the method comprises the steps: firstly calculating a volume point of a state variable of a power distribution network at a current moment, calculating a propagation volume point according to a state equation of the power distribution network, and calculating the state variable of the power distribution network according to the propagation volume point; and calculating a state predicted value at the next moment and a predicted value of the state error covariance matrix, and performing volume transformation on the propagation volume point to obtain a volume point at the next moment. Then, propagation is carried out on a volume point at the next moment through a measurement function, and a measurement predicted value at the next moment, a measurement error covariance matrix and a cross covariance matrix are calculated; and finally, calculating a Kalman filtering gain according to the measurement error covariance matrix and the cross covariance matrix, correcting a state prediction value by adopting the calculated Kalman filtering gain, and updating the state error covariance matrix, so that the state estimation precision is improved, and the estimation accuracy is improved. And the robustness and the precision of the system facing bad data are effectively enhanced.

The invention relates to a self-adaptive longitudinal vehicle speed estimation method, which comprises the following steps of: establishing detailed vehicle dynamic models, including a vehicle longitudinal dynamic model and a wheel speed dynamic model; a Sage-Husa self-adaptive unscented Kalman filtering algorithm is combined with a vehicle dynamics model, a state equation and an observation equation are designed, the longitudinal vehicle speed of a vehicle serves as a state variable, and observation vectors are constructed through data collected by a wheel speed sensor, an acceleration sensor and the like; divergence calculation is introduced for detecting and correcting divergence phenomena in the filtering process; and on the basis of a self-adaptive mechanism of a Sage-Husa algorithm, the filtering gain is adjusted in real time, and the vehicle speed estimation process is optimized. The improved Sage-Husa adaptive filter can dynamically adjust the filter parameters according to the change of the system state and the difference of noise, detects transient disturbance in combination with divergence calculation, reduces the influence of the transient disturbance on longitudinal vehicle speed estimation, and effectively improves the estimation precision.

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 provides a self-adaptive regulation and control method for a hydrogen production system based on real-time parameter feedback, and relates to the technical field of process control, the method comprises the following steps: collecting an input instruction signal and a controlled state variable signal of the hydrogen production system, and comparing a fluctuation energy index with a preset identification dead zone threshold; if the fluctuation energy index is lower than the identification dead zone threshold value, the current controller parameter is kept unchanged; if the fluctuation energy index is higher than the identification dead zone threshold value, activating an online parameter identification step; calculating a dynamic confidence interval of process model parameters; judging whether the process model parameters estimated in real time fall into the dynamic confidence interval or not; if the process model parameters fall into the dynamic confidence interval, converting the process model parameters estimated in real time into control parameters of a PID (Proportion Integration Differentiation) controller, and updating the control parameters into a control loop of the hydrogen production system; and if the process model parameter exceeds the dynamic confidence interval, resetting an internal state vector of the recursive algorithm in the online parameter identification step by using a boundary value.

The invention discloses a distributed driving electric vehicle path tracking and stability control method and system, and belongs to the technical field of electric vehicle active safety control. The method comprises the following steps: constructing a state-space equation based on a driver path tracking model and a vehicle three-degree-of-freedom motion model; establishing a dynamic envelope region based on a phase plane method, inputting the current parameters of the vehicle into the dynamic stable envelope region, and outputting a weight; constructing a controller based on the state-space equation, and taking the weight output by the dynamic stable envelope region as the control weight of the controller; inputting the vehicle state variables and the vehicle parameters into a controller, and outputting a longitudinal driving torque, an additional yaw torque, an additional front wheel turning angle and a driver intention turning angle required by driving; the additional front wheel turning angle and the driver intention turning angle are superposed to serve as input of a steering motor; a torque distributor is designed to distribute a longitudinal driving torque and an additional yaw torque, and the distributed torques are used as given values of the four hub motors.

The invention provides an integrated navigation method combining starlight refraction positioning with inertial navigation in a near space, and belongs to the technical field of starlight refraction navigation positioning. The method comprises the following steps: firstly, determining a state model of starlight refraction positioning in a near space by taking an error equation of an inertial navigation system of a near space aircraft as a state equation; designing a near space starlight refraction positioning algorithm, identifying a refraction star by using a star sensor on the aircraft, acquiring a refraction angle as a measurement, and establishing a measurement model of starlight refraction positioning in a near space; and obtaining the optimal estimation value of the system state variable by using an extended Kalman filter (EKF) method. The method has the advantages of being suitable for near space, high in reliability, high in autonomy and the like, and can be finally used for pure astronomical positioning and astronomical / inertial integrated navigation.

The invention belongs to the technical field of motor control, and particularly relates to a current harmonic suppression method based on a generalized active disturbance rejection controller. Comprising the following steps: S1, establishing a permanent magnet synchronous motor current loop mathematical model containing disturbance, and constructing a d-axis current equation and a q-axis current equation containing disturbance by taking respective current components of a d-axis and a q-axis as state variables; s2, defining the lumped disturbance of the d axis and the q axis as an extended state variable, and constructing an extended state observer to estimate the lumped disturbance; s3, constructing an improved resonance controller; and S4, establishing an active-disturbance-rejection control law, feeding back the lumped disturbance estimated in the step S2 to the active-disturbance-rejection control law for aperiodic disturbance compensation, and expanding the improved resonance controller in the step 3 and embedding the expanded improved resonance controller into the active-disturbance-rejection control law for periodic harmonic compensation. According to the invention, harmonic interference generated in the operation process of permanent magnet synchronization can be suppressed, so that the robustness of a motor control system is improved.

The invention provides an output feedback robust control method, system and device for a humanoid robot, and belongs to the technical field of humanoid robot control. The method comprises the following steps: establishing a kinetic model of a robot limb end system, and defining a state variable, a control variable and an output variable; determining a first robust control equation based on a state feedback optimal control principle, the kinetic model and an output feedback controller constructed by the output variables; converting the first robust control equation into a second robust control equation based on a mapping relation between a state variable and an output variable in the kinetic model, determining a to-be-estimated parameter vector according to the second robust control equation, and performing online estimation based on a preset adaptive law to obtain a control gain matrix; a second robust control equation is constructed based on the output variables; and based on the control gain matrix and the output variable obtained in real time, generating a corresponding control variable to be applied to the robot acra joint so as to carry out robust control on the robot acra system.

The invention discloses an active steering and braking integrated control system based on roll angle feedback, and belongs to the technical field of vehicle dynamics control. In order to solve the problems of slow control response and multi-actuator action conflict of the vehicle under the limit working condition, the method quantifies the comprehensive instability risk of the whole vehicle by constructing a dynamic virtual potential field. The potential field is based on key state variables such as a roll angle and a side slip angle, and the topological structure (namely the weight of each variable) of the potential field is remodeled in real time by a dynamic risk factor reflecting the intention urgency degree of a driver and the environmental danger level. The system evaluates the instantaneous control efficiency by calculating the contribution degree of each actuator to the reduction of the potential field gradient in a distributed manner. According to the method, the complex control problem is converted into the process of finding the minimum potential energy point, prospective suppression of the instability trend and conflict-free efficient cooperation between the actuators are achieved, and the limit stability and safety of the vehicle are remarkably improved.

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 provides a transient stability analysis method for a grid-connected system. The method comprises the following steps: establishing a mathematical model of a heterogeneous new energy grid-connected system comprising a network construction type new energy unit, a network following type new energy unit, a synchronous generator and a receiving end power grid, and defining a state variable set of the system; solving a stable equilibrium point and an unstable equilibrium point of the system according to the operation condition of the system; and by taking the unstable equilibrium point as a starting point, calculating by adopting a numerical integration method or a numerical continuation method to obtain a stable manifold starting from the unstable equilibrium point. The method provided by the invention realizes global and accurate depiction of the transient stability domain, overcomes the defects that a traditional small signal linearization method can only analyze local stability and a traditional energy function method depends on an artificial construction function and is limited in applicability, fundamentally changes an analysis normal form of the transient stability domain, and improves the analysis accuracy of the transient stability domain. And a more effective analysis tool is provided for safe and stable operation of the high-permeability new energy grid-connected system.

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.

A general decoupling method and system for electromagnetic transient simulation of a VSC are provided. The method includes: determining state equations of equivalent circuits and switch state tables of different VSCs; splitting the state equations, and decoupling and delaying capacitor elements and inductor elements to determine split equations; determining a recurrence relation of a state variable group with respect to a time sequence based on each split equation; obtaining a topological structure of a to-be-simulated VSC, and determining a decoupling model circuit; determining a switch state of the to-be-simulated VSC at a current simulation moment, and constructing a final decoupling model circuit; determining an external port voltage based on the final decoupling model circuit; and updating the state variable group at the current simulation moment based on the external port voltage and the parameters of the decoupling model circuit corresponding to the switch state.

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 discloses a vehicle spatial position uncertainty modeling method based on a factor graph, and relates to the technical field of automatic driving. Establishing a factor graph model, and constructing state variable nodes and factor nodes; an objective function of the factor graph optimization problem is converted into a nonlinear least square problem, and optimal state estimation is obtained through iterative solution by adopting a Gauss-Newton method; on the basis of an objective function under a Gaussian Newton method framework, a posterior covariance matrix is obtained through calculation of a Hesse matrix and inversion; performing incremental optimization on the factor graph, and updating a global Jacobian matrix through matrix decomposition and Givens transformation; and carrying out confidence ellipse geometric quantization, and representing the positioning uncertainty in a geometric form. Based on a factor graph and a confidence ellipse theory, through multi-sensor space-time constraint fusion and geometric interpretable representation, a vehicle positioning uncertainty modeling framework is constructed, and the safety and robustness of an automatic driving system downstream decision planning algorithm are improved.

The invention relates to an automobile thermal management system control method based on multi-target model predictive control, and belongs to the technical field of automobile thermal management systems. The method comprises the steps of establishing a discrete state space model of the thermal management system, setting a state variable, a control variable, a disturbance variable and a state space equation of the discrete state space model of the thermal management system, and then establishing a multi-target cost function and constraint conditions with stability, safety and low energy consumption of the thermal management system as targets; and then, based on fuzzy control logic, combining with an actual working condition, dynamically calculating a weight of each cost in the multi-target cost function, and finally solving an optimization problem according to the multi-target cost function under the determined weight in a discrete state space model of the thermal management system to obtain an optimal control input. And the optimal control input is transmitted to an actuator to regulate and control the automobile thermal management system. The system can adapt to various working conditions and environments, battery safety and comfort of a passenger compartment can be guaranteed, and energy efficiency and the service life of the actuator can be both considered.