1062 results about "Control variable" patented technology

The invention provides a pre-control and monitoring method for a whole process of actual grouting engineering based on digital geological model, comprising: extracting discontinuous fracture surfaces in a geologic body structure, to build a underground geologic body structure model; build a multi-source geologic body attribute model, optimizing and solving grouting simulations of a disaster high-risk region by using multiphase flow calculation method, and controlling grouting equipment by using optimized grouting parameters to carry out an actual grouting engineering; obtaining a multi-factor diffusion range of variable coefficients by adjusting physical values of the parameters in the grouting simulation and attribute data structure model by using a control variable method, learning and capturing complex mapping relationship between different parameters by using a neural network, optimizing the grouting parameters in the actual engineering in real-time, so as to realize the pre-controlling, monitoring and optimization of the whole process of the actual grouting engineering.

The invention relates to the technical field of cable production automation control, and provides a crosslinked cable production control method based on an improved PID (Proportion Integration Differentiation) control algorithm, which comprises the step of constructing an optimal reference model by establishing a cooperative control framework of temperature, pressure and traction speed and taking the temperature of a vulcanization pipe as a main control variable and the pressure and the traction speed of an extruder as slave control variables. A main controller tracks a temperature set value in real time, PID parameters are dynamically set according to technological parameters such as the thickness deviation of an insulating layer and the crosslinking degree of a material, and precise control is achieved through a three-ring cooperative coupling improved PID controller. A temperature control ring enables the steady-state error of the actual temperature and the set value to be converged to zero. The pressure and speed control loop converges the fluctuation amplitude to a process allowable range. The temperature stability, the pressure uniformity and the traction synchronism of crosslinked cable production can be improved, the process fluctuation and the defect rate are reduced, and the product quality and the production efficiency are improved.

The invention relates to the technical field of control systems, in particular to an intelligent control method and system for sewage treatment, and the method comprises the steps: collecting sewage treatment data; preprocessing the sewage treatment data; introducing a control variable, and constructing an incidence matrix between the control variable and the sewage treatment data; the control variables and the sewage treatment data serve as input, and the water quality is predicted through the water quality prediction model; determining a real-time control variable in the sewage treatment process through a reinforcement learning algorithm according to the incidence matrix and a water quality prediction result; and according to the real-time control variable, controlling the sewage treatment equipment to carry out sewage treatment. According to the method, relevance of all links of sewage treatment is represented through the incidence matrix, system states and system actions are constructed through the incidence matrix, optimization is carried out through a reinforcement learning method, regulation and control parameters meeting the water quality requirement are given, all-directional regulation and control of sewage treatment from beginning to end are achieved, and the sewage treatment efficiency is improved. And the management of the sewage treatment system is more refined and intelligent.

A data-driven multi-objective optimization control method for a municipal solid waste incineration (MSWI) process. The method comprises: first, establishing an MSWI process whole-process model for pollutant emission, wherein the model includes controlled object models, i.e. a furnace hearth temperature model, a boiler steam flow model and a flue gas oxygen-content model, and CO, CO2 and NOx pollutant emission index models; then, using single-neuron adaptive PID to establish a multi-input multi-output loop controller for key controlled variables, so as to realize stable control over a combustion process; and finally, in view of expert knowledge in the field, realizing adaptive solving of a key controlled variable optimization set value based on a multi-objective particle swarm optimization algorithm. Multi-objective optimization control over an MSWI process can be realized, existing running factories are guided, the pollutant emission concentration can be reduced, and the efficiency of combustion is improved.

An SCR flue gas denitration intelligent control method based on multivariable collaborative optimization specifically comprises the following steps: S1, collecting multi-key variable data of SCR flue gas denitration, processing the multi-key variable data, and storing the processed multi-key variable data into a historical database; s2, on the basis of the processed data, constructing a multivariable correlation model, and predicting operation states of the denitration system under different working conditions; s3, according to the actual operation condition of the system and the equipment performance, determining constraint conditions and optimization targets of SCR flue gas denitration; s4, performing multivariable collaborative optimization calculation in combination with the multivariable correlation model, the optimization target and the constraint condition, and searching an optimal control variable combination; and S5, intelligent control is implemented according to the optimal variable combination, and re-optimization is performed by monitoring feedback deviation in real time. The system can keep stable control precision in different operation scenes, and denitration efficiency fluctuation or parameter adjustment lag caused by sudden change of working conditions is avoided.

The invention discloses a simulation and optimization method for a natural gas industrial system in a rough environment, and relates to the technical field of intelligent optimization, and the method comprises the steps: collecting environment data, constructing a physical constraint diffusion model, and generating an extreme environment data set through embedding a hydrodynamic equation and a thermodynamic law; in combination with real sensor data of a plurality of gas field nodes, pre-training a federated transfer learning framework, eliminating cross-environment data distribution difference through resistance feature alignment, and constructing a global environment threat prediction model; based on the simulation result and the real-time sensor data flow, a multi-target fitness function is constructed, and control variables are generated through population evolution and dynamic strategy adjustment; and inputting the control variable into a pre-trained digital twinborn verification model, comparing a simulation result with a residual error of a real-time sensor data stream, triggering an incremental learning mechanism, and updating a global environmental threat prediction model. According to the method, the high-fidelity extreme environment data set is generated through the physical constraint diffusion model, and the physical consistency of multi-field coupling simulation is improved.

The invention discloses an air valve on-line self-adaptive adjusting method and system fusing digital twinning, and belongs to the field of air valve intelligent adjusting.The adjusting method comprises the following specific steps that firstly, edge devices collect and preprocess multi-source data of a corresponding air valve system, and based on the preprocessed multi-source data, the multi-source data of the corresponding air valve system are obtained; constructing a virtual simulation environment corresponding to the current air valve system; iI, in a virtual simulation environment, pre-training an air valve control model, identifying a causal relationship between each sensing parameter and an air valve adjustment result, and dynamically optimizing each control variable; according to the method, model prediction precision and simulation credibility are remarkably improved, dependence on a large amount of new data is reduced, cross-system rapid adaptive adjustment is realized, misjudgment and interference of confounding factors can be avoided during strategy optimization, interpretability and credibility of model decision are enhanced, data privacy is protected, convergence of a global strategy is accelerated, and the method is suitable for large-scale popularization and application. The overall intelligent level of the system is improved.

The invention discloses a variational deviation correction method and device, and relates to the field of weather forecast, and the method comprises the steps: obtaining the observation data of a current time window and an updated deviation correction coefficient of a previous time window; calculating a satellite observation deviation correction amount based on the deviation correction coefficient, the observation information and the mode background field, calculating a deviation between simulation observation and actual observation based on an observation operator, and carrying out correction and quality control on the deviation; according to the satellite observation data after quality control, on the basis of the improved target functional and the gradient thereof, in the minimization iteration process, under the joint constraint of the observation data and a numerical weather forecast mode, a deviation correction coefficient is updated online; wherein the improvement process of the target functional comprises the steps that a deviation correction coefficient is used as a control variable to be combined with a mode state variable to construct a new target functional, and variable transformation is carried out in an increment form. The assimilation analysis quality of observation data is improved, and the numerical weather forecast level is improved.

The invention relates to the technical field of life cycle management, in particular to a catalyst industry chain-oriented full life cycle traceability method and system, and the method comprises the steps: obtaining the state attribute data of the same batch of recovered products, and carrying out the analysis to obtain an efficiency feature vector; inputting the efficiency feature vector into a preset quality evaluation model for matching, and identifying quality deviation data of the recycled product; the batch identity codes are analyzed, and resume data of the production full life cycle are obtained; a multi-dimensional data association model is constructed, quality deviation data is taken as a target variable, resume data is taken as an input feature, an influence weight coefficient and an attribution confidence index of each production control variable on the quality deviation are analyzed, and key control elements are screened out; and performing production regulation and control optimization operation based on the key control elements, calculating target parameter offset for correcting quality deviation, dynamically calculating a dynamic tolerance constraint range of a production control variable in combination with an attribution confidence index, and packaging into an execution management and control protocol.

The invention relates to the technical field of deep learning, and discloses an earphone mold injection molding parameter optimization method, device and equipment, and the method comprises the steps: collecting temperature distribution data and mold cavity pressure data in an earphone mold cavity, and carrying out earphone shell injection molding dynamic analysis to obtain an earphone shell material flow curve; carrying out topological characteristic analysis on the earphone shell material flow curve, and constructing an earphone shell injection molding process parameter model; based on the earphone shell injection molding process parameter model, the mold cavity temperature and the mold cavity pressure serve as state variables, the heating power and the injection pressure serve as control variables, and an earphone shell injection molding optimization controller is constructed; the earphone shell injection molding optimization controller is connected with the injection molding machine control system, the heating power and the injection pressure of the earphone mold are adjusted in real time, the optimal technological parameters of the earphone mold are solved, a real-time feedback and parameter fine adjustment mechanism is adopted, the injection molding machine control system can quickly respond to technological fluctuation, and the consistency of product quality is guaranteed.

The invention discloses a carbon dioxide mineralization storage dynamic intelligent regulation and control and permeation enhancement optimization method and system. The optimization method comprises the following steps: collecting field monitoring injection parameters and related data of reaction products in a mineralization storage process in real time; according to injection parameters monitored on site and related data of reaction products, two optimization objective functions of mineralization rate and free CO2 volume are formed; constructing a mineralization sequestration multi-objective optimization model, and screening out an optimal injection parameter set value from the Pareto solution set to obtain an optimal condition parameter; optimal injection parameters in the Pareto optimal solution set are input into the constructed field enhancement regulation and control module, and control variables are adjusted in real time according to real-time changes of reservoir response, mineralization reaction process and injection working conditions; and fracturing transformation is conducted on the target storage rock mass, the seepage enhancement effect of the target storage rock mass is quantitatively evaluated, an injection scheme is dynamically updated based on the transformed reservoir parameters, and the mineralization regulation and control system is enhanced.

The invention provides a quadrotor unmanned aerial vehicle suspension swing suppression flight control method based on a DDPG. The method comprises the steps that a kinetic model of a quadrotor unmanned aerial vehicle suspension system is established; performing input shaping processing on an expected reference track signal by using a ZVD input shaper; the unmanned aerial vehicle suspension system is decoupled into position control of a position loop and attitude control of an attitude loop by adopting a cascade trajectory tracking control strategy, and a fuzzy PID controller of the position loop is designed based on a fuzzy theory; the shaped track signal is input into a fuzzy PID controller, and parameters of the PID controller are adjusted on line; performing attitude settlement on the virtual control quantity output by the fuzzy PID controller, and inputting the virtual control quantity into an active disturbance rejection controller; roll, pitch, and yaw torque control quantities are generated based on the virtual control quantity of the attitude loop and the control quantity generated by the DDPG-based tilt angle controller. Under a complex external interference environment, a preset track is tracked with high precision, load swing is effectively suppressed, reliable execution of a transportation task is successfully guaranteed, and meanwhile, the control precision and the anti-interference capability of the system are remarkably improved.

The invention discloses an intelligent blasting sequence control system for mixed loading explosives, which is provided with a data acquisition module, a data preprocessing module, a model construction module, a parameter optimization module, a multi-target control module and a data storage module to intelligently control the blasting sequence of the mixed loading explosives. Distance attenuation features, delay features, topographic features and blasting energy features of blasting data are constructed, the blasting prediction model is updated and iterated based on a neural network algorithm and historical blasting data information, and the performance of the blasting prediction model is evaluated. The number of hidden layers and the number of nodes in the blasting prediction model are updated and optimized through a control variable method, the blasting prediction model is optimized, when mixed explosive is adjusted and the blasting sequence is optimized, the optimal mixed explosive composition is searched through a search optimization algorithm, and the optimal mixed explosive composition is obtained through control signals in combination with real-time blasting data information. And the blasting sequence and delay are dynamically adjusted, so that the blasting of the mixed explosive is intelligently and accurately controlled in real time.

A valve timing control device includes: a unit for calculating a target valve timing for one of an intake valve and an exhaust valve in accordance with an operational state; a unit for detecting an actual valve timing of one of the intake valve and the exhaust valve; an actual valve timing control unit for generating a controlled variable for the VVT based on a difference between the target valve timing and the actual valve timing; a unit for learning a learning value of the controlled variable required for maintenance of the actual valve timing based on an integral correction value; and a unit for detecting a malfunction in a VVT from an amount of a shift in the actual valve timing when learning is not completed yet, and for detecting a malfunction in the VVT from the difference when learning is already completed.

The invention relates to an aircraft-trailer system coordination control method fused with reinforcement learning, and the method comprises the steps: constructing an aircraft-trailer system mathematical model, and determining the coupling relation between control variables and state variables, the control variables including trailer movement control variables and system steering control variables; aiming at trailer movement control variables, designing a model-based self-adaptive controller; aiming at the steering control variable of the system, designing an attitude compensation controller based on reinforcement learning; the self-adaptive controller and the attitude compensation controller are fused, corresponding control signals are output to the aircraft and the trailer, and motion control over the aircraft and the trailer is completed. Compared with the prior art, the self-adaptive control law based on the virtual variable is designed through the decoupling kinematics model; and aiming at system parameters and external uncertainty, designing a compensation method based on reinforcement learning. A series of simulation is carried out under different conditions, and results show that the method can ensure coordinated movement of the aircraft and the trailer under different conditions.

The invention relates to a thermocouple and Kalman filtering-based global mk-level precision temperature control method. The method comprises the following steps of S1, arranging a thermocouple temperature measurement network; s2, establishing a mathematical model of a temperature measurement and control system; s3, establishing a discrete temperature measurement and control system model containing process noise and a thermocouple observation model containing measurement noise; s4, acquiring a system temperature priori estimation value at the current moment, and calculating a priori state estimation error covariance matrix; s5, obtaining a system temperature posterior state estimation value, updating the posterior state error covariance matrix, and obtaining a temperature measurement and control system correction model; s6, obtaining an error vector of a temperature measurement and control system; s7, obtaining a full-domain control variable based on feedforward-feedback, and acting the full-domain control variable on the temperature control object through an execution component; and S8, repeating the steps S4-S7 to form closed-loop control so as to realize the global mk-level temperature control precision of the temperature measurement and control system. The method has the beneficial effects of good comprehensiveness and strong coordination.

The invention discloses a mine semi-autogenous grinding process analog simulation method and system based on discrete elements, and relates to the field of analog simulation, and the method comprises the steps: according to a crushing characteristic label and a parameter mapping matrix, fusing Internet of Things sensor data, constructing a particle size distribution prediction model and an energy consumption optimization scheduling strategy, and obtaining an energy-saving operation plan; comparing the simulation data set with field production data, adjusting multi-phase coupling particle model parameters, generating a calibration parameter set, researching the influence law of semi-autogenous mill operation parameters by adopting a control variable method based on the calibration parameter set, obtaining a performance index database and an analysis report, and fusing multi-sensor data and laser radar data to obtain a semi-autogenous mill operation parameter analysis result. An ore grinding process sensing mechanism and optimization control logic are constructed, and a visual interface is generated; through multiphase modeling and data driving optimization, the simulation precision and the process suitability are comprehensively improved, and an efficient and economical solution is provided for the mine semi-autogenous grinding process.

The invention belongs to the technical field of dry quenching, and provides a dry quenching boiler inlet temperature prediction method based on a DeepSeek large model. According to the method, the inlet temperature T6 of the coke dry quenching boiler is predicted by using the data, namely the process variable, changed in real time in the coke dry quenching process; the key control variable is a manually controlled variable, the change of the process variable is controlled / adjusted by changing the key control variable, and automatic control is realized by utilizing a prediction-control joint optimization method and by means of reinforcement learning. According to the prediction method provided by the invention, a complex nonlinear relationship and interaction among multiple variables can be captured, and strong coupling and nonlinear influence among multiple process parameters in the dry quenching process can be processed; through fine tuning on a large amount of historical data, the method can automatically learn and adapt to different working conditions, reduces the dependence on artificial feature engineering, and improves the generalization capability and prediction performance of the model.

The invention discloses a proportional-integral-derivative loop control method and a related device, and relates to the technical field of automatic control, after a set value and a current measurement value of a controlled object are obtained, the deviation of the controlled object is calculated, and the set value, the current measurement value and the deviation are input into a data analysis module; the current variation trend information of the controlled object is obtained, the current variation trend information and the current deviation state information are input into the parameter group selection unit, a sub-parameter group matched with the current working condition of the controlled object is determined, and the sub-parameter group is input into the proportional integral differential control unit to adjust the current measured value. And outputting the current operation value of the controlled object, and finally, controlling the controlled object by adopting the current operation value. According to the method, the sub-parameter group suitable for the current working condition can be matched for the controlled object in real time according to the trend change and the deviation state of the controlled variable, the current measurement value is adjusted by adopting the sub-parameter group, the current operation value is output to control the controlled object, and the parameter adjustment efficiency is high.

The invention discloses a metal flat plate thickness and conductivity eddy current measurement method and system. According to the method, an electromagnetic detection platform is built, and a coaxial excitation-receiving eddy current probe structure is designed. Establishing a coaxial eddy current detection analysis model, and deducing a dynamic calculation result of mutual inductance parameters of the sensor in a frequency sweeping mode; setting two PID (Proportion Integration Differentiation) controllers and initial control variables thereof, and inputting the control variables serving as a thickness value and a conductivity value into the analytical theoretical model to obtain a corresponding mutual inductance change result; calculating an instantaneous error term and an accumulative error term of the current iteration period in real time, and when the error exceeds a preset convergence threshold value, inputting the error into a PID controller and updating the two control quantities until the maximum iteration frequency is reached; otherwise, outputting a control variable value, wherein the two control variables output by the controller are the thickness and the conductivity of the metal plate respectively. According to the invention, the thickness and conductivity of the metal plate can be accurately and simultaneously measured.

The invention provides a multivariable decoupling control method and system for an air inlet system of a high-altitude simulation cabin, and the method comprises the steps: firstly building a precombustion chamber cavity thermodynamic differential equation, and building a state equation in combination with a regulating valve first-order inertia model and an engine model; constructing a decoupling matrix by using a differential flatness theory, and converting a pressure and temperature coupling system into an independent control channel; monitoring a system state in real time by using an extended state observer, uniformly estimating internal and external disturbances as total disturbances, and generating a compensation signal; the model prediction controller performs rolling optimization on future time domain control quantity according to deviation between a set value and an actual value, generates an initial instruction in combination with the compensation signal, and converts the initial instruction into an independent valve control signal through a decoupling matrix; the high / low-temperature airflow mass flow is controlled through the opening degree of the adjusting valve, the engine model outputs actual parameters to form closed-loop feedback, and the controller updates the control quantity in each cycle. The multi-variable strong coupling, the dynamic disturbance and the model uncertainty are effectively overcome, the control precision and the parameter robustness are both achieved, and the complex working condition requirements are met.

The invention discloses a photovoltaic photo-thermal coupling hydrogen production system control method for a hydrogen-burning heavy truck traffic network. The method comprises the following steps: S01, collecting operation data of a photovoltaic array, an electrolytic cell and a hydrogen storage tank; s02, based on a model prediction control algorithm, establishing a multi-objective optimization model containing an objective function and constraint conditions; s03, merging the sub-objective functions into a comprehensive objective function through a linear weighting method; s04, applying constraint conditions; S05, performing rolling optimization calculation by adopting a model prediction control algorithm; s06, outputting an optimal control variable; s07, adjusting corresponding parameters in the system; s08, monitoring the running state of the system; s09, whether the system meets constraint conditions is judged, according to the control method, through multi-target collaborative optimization and precise constraint control, the performance, efficiency, economical efficiency and safety of the photovoltaic photo-thermal coupling hydrogen production system can be remarkably improved, and clean, efficient and stable hydrogen energy supply is provided for a hydrogen combustion heavy truck traffic network.

The invention provides a traction power supply system control method based on multi-objective optimization, and the method comprises the following steps: S1, determining an initial control variable of a traction system, the initial control variable comprising converter power of an alpha power supply side and a beta power supply side of the traction power supply system and train braking resistor dissipation power of the alpha power supply side, the initial control variable is used as input to construct a regenerative braking energy MPC prediction model; s2, predicting a state variable of a next moment based on a regenerative braking energy MPC prediction model, wherein the state variable comprises output power of a traction substation, total dissipation power of a brake resistor, output power of an energy storage system and a charge state of the energy storage system; and S3, constructing a multi-objective optimization function based on the state variable at the next moment, converting the multi-objective optimization function into a quadratic programming form for solving to obtain a control variable sequence, and taking a first item of the control variable sequence as control input to control the traction power supply system to work.

The invention discloses a tension servo control method and system suitable for multiple scenes, and belongs to the technical field of tension servo control. Comprising the following steps: respectively installing a tension sensor and an encoder at specified positions based on structural characteristics of an execution mechanism, simultaneously acquiring a pressure signal # imgabs0 # of the tension sensor and a pulse signal generated by the encoder at a specified frequency, and converting the pressure signal # imgabs1 # into a digital signal to obtain a current tension value # imgabs2 #; based on the pulse signal, rotating speed conversion is carried out to obtain the current rotating speed # imgabs3 # of the tension roller, the current rotating speed # imgabs4 # is combined, a rolling diameter calculation formula is utilized to obtain the current rolling diameter # imgabs5 #, PID parameters are dynamically adjusted by adopting the current rolling diameter # imgabs6 #, and a self-adaptive fuzzy algorithm is constructed; and the current tension value # imgabs7 # and the current rotating speed # imgabs8 # serve as input, and a control variable # imgabs9 # is obtained through calculation. The tension control system is different from a tension control system based on a PLC in the prior art, signal identification and data output are directly carried out through the encoder, the PLC scanning period is omitted, and the control efficiency is improved.

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

The invention relates to the technical field of self-adaptive pipe network dynamic hydraulic balance online optimization, and discloses a self-adaptive pipe network dynamic hydraulic balance online optimization method. By constructing a node-pipeline topology model and defining a control variable and a threshold value, data input standardization is realized; the opening degree and flow of the valve are collected in real time, a hydraulic impedance and spectrum matrix is constructed, and the dynamic working condition is accurately reflected; decomposing the spectrum matrix, extracting global and local oscillation mode features, and endowing differentiated weights for adjustment; a residual error is calculated based on the water demand and the flow, a tolerance threshold and projection verification are set, and subsequent operation is triggered only during real unbalance; the opening adjustment direction is determined according to spectral analysis and sensitivity evaluation, and blind movement is avoided; the optimal step length is analyzed and solved, amplitude limiting is carried out, and convergence speed and impact suppression are both considered; the valve opening is updated according to branches and issued in a closed loop mode, continuous online optimization and automatic control are achieved, and the real-time performance, the stability and the intelligent level of a pipe network are remarkably improved.

The invention relates to the technical field of machine vision and food quality detection, in particular to a food defect real-time detection method and system based on image processing. The method specifically comprises the following steps: integrating multi-modal data, and adaptively adjusting a weight coefficient to improve data quality; multi-stage noise reduction is carried out, key frames are extracted, and a key frame verification mechanism is enhanced through a 2FA control variable; constructing a YOLO-BioNet model, and optimizing the detection efficiency and precision in a complex scene through dynamic convolution kernel allocation and a spiking neural network; integrating Kalman filtering, LSTM (Long Short Term Memory) time sequence modeling and a deep reinforcement learning model, analyzing a food surface change track and predicting a defect state; real-time data and historical information are fused, and a dynamic threshold value is calculated through a Bayesian network; performing linkage response between an automatic sorting instruction and a production line; a distributed storage and graph neural network optimization model is adopted, and defect tracing is supported. The method has remarkable advantages in the aspects of real-time performance, detection accuracy and self-adaptive capability.

The invention discloses a model predictive control-based train formation distributed cooperative control method, which comprises the following steps of: acquiring a train tracking speed running curve, train speed limiting information and a train running resistance coefficient, and establishing a train formation model; according to a preset model predictive control algorithm, in a set predictive control time domain, according to a train formation model, a control target and constraint conditions, state variables of positions and running speeds of all trains in a train formation and control variables of control forces of all the trains in the train formation are used to predict the train formation according to state variables of the positions and running speeds of all the trains in the train formation. Respectively generating optimization control problems of the leading train and the following train in the train formation; and based on a synchronization theory, solving an optimization control problem of each train to obtain a control force of each train in the formation, and inputting the control force into a train operation control system to realize distributed control of each train in the train formation. The method is based on a model predictive control algorithm, uses a synchronization theory, and reduces the use of computing resources and communication resources in practice.

The invention provides a method, a device and a system for solving a combinatorial optimization problem. The method comprises the steps of receiving a combination optimization problem input by a user; mapping the combinatorial optimization problem into a model; the model comprises a complex variable to be solved and a target function; the complex variables to be solved are used for describing solutions, meeting the requirements, of permutation and combination of things as discrete variables in a complex field; the objective function is used for describing permutation and combination of a plurality of things under a given constraint condition; determining a kinetic equation of the model in a complex field, wherein the kinetic equation is used for describing a time-based evolution process of a complex variable to be solved; the kinetic equation at least comprises a control variable, a multi-bifurcation parameter and an action parameter; solving the kinetic equation, and changing the value of the control variable to obtain q stable phases of the complex variable to be solved; the value of q is determined by a multi-bifurcation parameter; and obtaining a solution of the combinatorial optimization problem according to the q stable phases. The spin scale can be reduced and the solving time can be shortened when the combinatorial optimization problem is solved.

The invention relates to a four-wheel independent steering and driving vehicle path tracking control method based on a compound control framework, which comprises the following steps of: 1, establishing a seven-degree-of-freedom dynamical model of a four-wheel independent steering and driving vehicle, and realizing high-performance path tracking by reasonably distributing input parameters; step 2, establishing a deep reinforcement learning transition model, defining a vehicle state and an input parameter of an auxiliary controller, and designing a system reward function to optimize path tracking control performance; 3, selecting a twin delay depth deterministic strategy gradient algorithm to realize deep reinforcement learning, reducing the over-estimation problem of the algorithm, and improving the learning stability; 4, a double-layer control framework is adopted, an upper-layer controller calculates generalized control quantity based on nonlinear model predictive control, and a lower-layer controller distributes the generalized control quantity to each wheel through sequential quadratic programming so as to solve the transverse and longitudinal coupling problem of the vehicle in path tracking control; and step 5, the auxiliary controller based on deep reinforcement learning carries out real-time compensation on the control variable according to the external disturbance and the vehicle state, so that the control precision and stability of path tracking are improved. By means of the method, stable control over four-wheel independent steering and driving vehicle path tracking in a complex environment is achieved, and the adaptive capacity of the system to external interference is enhanced.