902 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.

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 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 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 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 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 power distribution network adaptive topological structure optimization method, system and device and a medium, and the method comprises the steps: obtaining related parameters of a power distribution network system, dividing the power distribution network system into different layers and different regions based on the related parameters of the power distribution network system, and constructing a layering and partitioning model; coordinating and solving the layering and partitioning model through an intelligent algorithm, and obtaining control variables of different regions; constructing an objective function based on the control variable, and solving the objective function by adopting an improved particle swarm optimization method to obtain a topological structure; and optimizing the topological structure by adopting a double-layer optimization strategy to obtain an optimization result, evaluating the optimization result, and feeding back the evaluation result to the target function. According to the method, the randomness and volatility challenges caused by multi-source resource access can be effectively dealt with, and the limitation of the traditional separation design is broken through the collaborative optimization of topology and control, so that the operation efficiency, the stability and the anti-interference capability of the power distribution network are improved, and the collaborative evolution of a topological structure and a control strategy is realized.

The invention discloses a standard system applicability evaluation method and system based on multi-source data, and relates to the field of standardization, and the method comprises the steps: collecting the historical operation data of equipment, and dividing the equipment into an experiment group equipment group and a control group equipment group; the method comprises the following steps: preprocessing historical operation data of equipment, and respectively constructing a performance index value sequence for quantitatively reflecting the achievement condition of a target standard for a technical target for an experimental group equipment group and a control group equipment group; and on the basis of the performance index numerical value sequences of the experimental group equipment group and the control group equipment group, a net effect value and statistical confidence of a target standard are calculated by adopting a double difference model, and a unit model and a geographical climate region are added into the double difference model as fixed effect control variables. According to the method, the corrected net effect value and the intermediary centrality score are fused into the comprehensive index, so that multi-dimensional dynamic evaluation combining data driving, causal identification and knowledge value of standard applicability is realized.

The invention discloses a heating heat exchange station optimization control method and system based on economic model predictive control. The method comprises the following steps: S1, collecting historical operation data and meteorological data of a heat exchange station, and constructing a training data set to establish a dynamic mathematical model of the heat exchange station and identify model parameters; and S2, based on the dynamic mathematical model of the heat exchange station established in the step S1, taking the primary or secondary network water supply flow as a control variable, considering the heat supply demand and operation constraint of a heat user, minimizing a heat exchange station operation cost objective function, designing an economic model prediction controller, and reasonably adjusting the operation condition of the heat exchange station in time. According to the method, accurate prediction of key heat supply parameters such as water supply and return temperature and pressure can be achieved, then optimization regulation and control of the heat exchange station are achieved, meanwhile, the optimal economic performance can be guaranteed on the premise that the heat supply requirement of a heat user and safe operation are met, and therefore the heat supply cost is reduced, and energy consumption is reduced.

The invention provides an effluent concentration regulation and control method and device of a sewage treatment plant, electronic equipment and a medium, the method is used for regulating and controlling a sewage treatment system, and the method comprises the following steps: acquiring water quality parameters of inflow water of the sewage treatment plant and actual working conditions of hydraulic operation conditions; determining the effluent concentration based on the water quality parameters and a pre-constructed bidirectional long-short term memory neural network; establishing a fuzzy rule base based on the actual working condition, the effluent concentration, a preset target working condition and a preset target effluent concentration; setting a fuzzy self-tuning PID control model based on the fuzzy rule base; based on the actual working condition, the effluent concentration, the target working condition and the target effluent concentration, a control variable is determined through a fuzzy self-tuning PID control model; the sewage treatment system is regulated and controlled through the control variables to reach the target working condition and the target effluent concentration. In the mode, the effluent concentration is automatically regulated and controlled, the regulation and control accuracy is improved, and the labor cost is reduced.

The invention relates to a trajectory tracking control method for a four-wheel unmanned vehicle. The method comprises the following steps: S1, acquiring the position and course angle of the vehicle; acquiring a vehicle reference position and a reference course angle corresponding to the vehicle reference track point; calculating a transverse error and a course angle error of the vehicle; establishing a vehicle trajectory tracking error model based on the vehicle lateral error and the course angle error; s2, obtaining a tire cornering stiffness estimation value at the current moment; s3, solving an optimization problem to obtain an optimization control quantity; s4, outputting a feed-forward compensation amount; s5, adding the optimal control quantity and the feed-forward compensation quantity to obtain a final control quantity; and S6, executing a control instruction based on the final control quantity, and performing trajectory tracking control on the four-wheel unmanned vehicle. Compared with the prior art, the method has the advantages that the number of control variables of MPC is reduced, the calculation complexity is remarkably reduced, and the trajectory tracking precision and the control real-time performance of the four-wheel unmanned vehicle are improved.

The invention provides a demand response performance evaluation method based on multi-dimensional uncertainty parameters. The objective of the invention is to evaluate the response performance of a building under the influence of uncertain parameters by identifying the uncertain parameters influencing the response performance. The method comprises the following steps: constructing an evaluation index system covering different time stages of demand response, wherein the evaluation index system comprises an intensity index, a capacity index and a rebound rate index; key influence parameters are identified and classified, a reasonable value range of the key influence parameters is defined, and the key influence parameters include eight key building design variables and three types of control variables including response duration, control temperature and response occurrence time; establishing an accurate building numerical simulation model, formulating a demand response strategy, and aiming at different design variables and control variables, performing simulation of the corresponding response strategy by utilizing EnergyPlus; the electrical load of the building is obtained through simulation, and the comprehensive response performance of the building is obtained in combination with the evaluation index system.

The invention discloses a reinforcement learning-based slurry balance shield construction attitude settlement collaborative optimization method and system, electronic equipment and a storage medium. The method comprises the following steps of: constructing state and action characteristics; a control strategy modeling step: constructing a composite reward function which takes incision pressure, partition propulsion pressure, tunneling speed and the like as control variables and fuses ground surface settlement and attitude control performance; designing a control strategy model based on reinforcement learning; a control strategy training step; a control parameter output step; in the shield tunneling process, control parameter setting is dynamically generated through current state information, and cooperative control over settlement and posture is achieved. According to the slurry balance shield construction posture settlement collaborative optimization method and system based on reinforcement learning, the electronic equipment and the storage medium, the adaptive capacity and self-learning capacity of the system can be improved, and the automation, intelligence and construction safety level of the slurry balance shield construction process can be improved.

The invention relates to the technical field of intelligent decision making, and discloses an intelligent decision making method and system based on a computing network integrated industrial PLC, and the method comprises the steps: carrying out the time slice distribution of control parameters of water supply equipment, and dividing the decision period of the PLC, and obtaining an initial control parameter queue; determining occurrence probability distribution of a normal operation state, an efficient control state and a decision conflict state to obtain a water supply parameter conflict probability table; redistributing the parameter control time resource to obtain a water supply parameter optimization control time sequence; converting the parameter groups which still have conflicts into mutually independent control variables, and respectively executing optimization calculation to obtain a multi-parameter parallel control instruction of the water supply equipment; and carrying out dynamic resource scheduling on the multi-parameter parallel control instruction of the water supply equipment to generate an energy-saving control scheme of the water supply equipment. The method effectively optimizes the dynamic distribution of control resources, remarkably shortens the response time of control parameters, and improves the overall throughput of the system.

The invention relates to the technical field of avionics and flight control, in particular to a multi-source fusion avionics control system and method, a multi-source acquisition and compression module synchronously acquires inertial measurement, star sensor and radar data under a unified time reference, and generates observation data through random projection compression; the fusion twinning module obtains a fusion state and a hypergraph based on compressed observation data, generates pneumatic, electromagnetic and structural twinning environments in real time on a shared three-dimensional grid through physical constraint diffusion, and outputs a registration result by adopting entropy regularization optimal transmission; the decision execution module maps discrete control variables quantified by a flight control computer into an Isinus model, obtains control sequence seeds through quantum annealing, generates a decision instruction through pulse sinus strategy network optimization, issues the decision instruction to a control execution unit to generate control execution data, and updates a hypergraph in combination with execution feedback for next cycle use. According to the method, the attitude estimation precision and the control robustness under the conditions of high maneuverability and strong interference are improved.

The invention discloses an electric linear servo system control method based on cascade LESO-DISMC. The method comprises the following steps: firstly, establishing a complete mechanism model of a permanent magnet synchronous motor servo loading system; then designing DOB interference observer real-time compensation by adopting a LuGre friction model and a gap dead zone model; a feedforward control quantity is constructed by utilizing a measurable displacement signal, and redundant torque is inhibited; designing a dynamic integral sliding mode controller to ensure global stability; a high-low bandwidth LESO is constructed, and both fast tracking and smooth filtering are considered; and finally, trajectory optimization is carried out, a smooth trajectory is generated by adopting polynomial interpolation for the step and triangular wave signals, and mechanical vibration is inhibited. According to the method, a LuGre friction model is adopted to compensate nonlinear friction, cascade linear LESO and sliding mode control are combined, a track is optimized through an S-shaped curve, and the problem of'differential explosion 'is solved; according to the method, instruction smoothing, core control and friction compensation are fused, and the anti-interference capability and the loading precision of the system are improved.

The invention discloses a Tube MPC-based hybrid electric vehicle layered energy management method. The method comprises the following steps of performing longitudinal dynamic modeling on a hybrid electric vehicle, determining a vehicle demand power formula, constructing an evaluation function of equivalent fuel consumption, and constructing an output power model of a vehicle engine and a battery; carrying out uncertainty analysis on the energy management system, determining uncertain parameters in the vehicle model, quantifying the uncertainty of the uncertain parameters based on probability distribution, and constructing an uncertainty set of the uncertain parameters; on the basis of a dynamic programming algorithm, under the global working condition, the minimum amount of equivalent fuel consumption serves as a target function, an optimal control sequence of the engine is output, and an SOC reference trajectory is generated; and performing tracking solution on the control variables and the state variables in the uncertainty set by applying a Tube model predictive control (Tube MPC) method to obtain an optimal control sequence under uncertain factors, and outputting a first control variable.