159 results about "Optimum control" patented technology

The invention discloses a cable inspection robot attitude control method based on MPC and ADRC fusion, and the method comprises the steps: firstly, building a model prediction control (MPC) model, carrying out the prediction of the future state of a robot, solving an optimal control problem, and obtaining initial control input for achieving the internal dynamic compensation; secondly, an extended state observer (ESO) in an auto-disturbance rejection control (ADRC) model is adopted, and external disturbance is estimated in real time and used for compensating control input; thirdly, a fuzzy self-adaptive module based on fuzzy PID is introduced, the attitude error, the attitude error change rate and regeneration disturbance intensity generated based on estimation disturbance are collected in real time, the MPC weight and the ESO gain are dynamically adjusted, and the response speed and robustness of the system under variable working conditions are improved. According to the hierarchical control scheme, the defect that an existing single control method is difficult to keep the optimal control effect under variable working conditions is effectively overcome, and technical guarantee is provided for stable operation of the cable inspection robot in a high-risk and high-complexity environment.

A system for controlling motion of an articulated vehicle with one or multiple trailers is described. The system is configured to infer the state of a vehicle, determine a motion path with forward motions and backward motions for the vehicle based on the state, and formulate an optimal control problem for optimizing the motion path. The optimal control problem comprises an integral tracking error function indicating integral tracking error based on motion cusps relating to the motion path. The motion cusps indicate switching between forward motion and backward motion. The motion path is optimized over a prediction horizon based on solving the optimal control problem. A control command is generated for the vehicle based on the optimized motion path and a vehicle model, and the motion of the articulated vehicle is controlled based on the control command thereby changing its state.

The invention discloses a multi-parameter cooperative temperature control system of an energy-saving industrial oven, and relates to the technical field of temperature control of industrial ovens, load data in the oven are collected in real time through a high-precision sensor, and a Kalman filter is used for cleaning, calibrating and filtering environmental noise and drift to obtain smooth and stable data; analyzing a real-time load based on a support vector regression model, capturing a nonlinear relation in combination with a radial basis function kernel, and predicting temperature recovery time and expected energy consumption; a reinforcement learning algorithm is adopted, a high-dimensional state space is analyzed through a deep Q network, the heating power and the airflow speed are dynamically optimized, and optimal control parameters are output; and the control module executes the adjusted setting, monitors the temperature and the energy consumption in real time, feeds back data to a prediction model and a learning strategy, and periodically updates parameters to adapt to load and environment changes. The adaptive capacity and long-term stability are remarkably improved, and the method is suitable for industrial scenes with load change, equipment aging or environment fluctuation.

The invention discloses a wind power plant unit load reduction optimization method and system based on cabin type laser radar real-time wind measurement, and relates to the technical field of wind power generation. Constructing a load estimation model; performing real-time detection on a blade root bending moment predicted value sequence and a tower bottom vibration acceleration predicted value sequence, constructing a multi-objective optimization function including blade root bending moment, tower bottom vibration acceleration and power generation power, and setting a dynamic weight coefficient of the multi-objective optimization function according to three-dimensional wind field prediction sequence data; obtaining an optimal control variable through a multi-objective genetic algorithm based on the multi-objective optimization function; and according to the optimal control variable and vertical wind shearing in the three-dimensional area, differential adjustment is conducted on the pitch angle of each blade, and a personalized variable pitch angle sequence of each blade is obtained. According to the method, by establishing the optimization objective function fusing the load and the power, multi-variable collaborative optimization is achieved, and the safety and the economical efficiency of wind power plant unit operation are remarkably improved.

The invention discloses an intelligent control method and system for a deep water cooling system of a reservoir in a data center, and relates to the technical field of cooling control, and the method comprises the steps: analyzing the cooling demand distribution of the data center, constructing a demand database, constructing a water source monitoring database in combination with the temperature, the water layer depth and the water quality monitoring data of the reservoir, and storing the water source monitoring database; the method comprises the following steps: decomposing a water taking treatment process and a cooling treatment process, extracting and fitting a relationship between control parameters and a treatment effect and energy consumption, searching optimal control parameters according to cooling demand data and a process flow, and generating cooling system control information. The technical problems that an existing data center cooling system cannot be dynamically adjusted according to real-time environment changes and water source conditions, so that the cooling effect is unbalanced, and energy is wasted are solved, the control parameters are dynamically optimized through an intelligent control method in combination with the real-time cooling requirement and water source monitoring data, and the cooling efficiency is improved. The technical effects of precise cooling and energy consumption reduction are achieved.

The invention discloses a gelatin solution temperature control optimization method for donkey-hide gelatin processing, and belongs to the technical field of swarm intelligence optimization. Comprising the steps that a gelatin solution temperature closed-loop PID control system in the donkey-hide gelatin decocting process is constructed, an improved side hand spider turning optimization algorithm is configured to serve as a parameter setting core, the self-adaptive setting process of PID control parameters is executed, and a global optimal control parameter vector obtained through optimization is mapped to a donkey-hide gelatin solution temperature PID controller module. The method focuses on the PID parameter setting problem of donkey-hide gelatin liquid temperature control, introduces the improved side hand turning spider optimization algorithm as a swarm intelligence algorithm to set the donkey-hide gelatin liquid temperature PID control parameters, and can effectively overcome the defect that the side hand turning spider optimization algorithm is easy to fall into local optimum. And the temperature of the donkey-hide gelatin liquid can be controlled more excellently.

The invention relates to the technical field of automobile suspension control, in particular to a control compensation method, medium and equipment based on the high-frequency hysteresis characteristic of an active suspension, and the method comprises the steps: collecting the dynamic state data of a vehicle in real time, carrying out the working condition recognition of the dynamic state data, and determining the control demand of the active suspension of the vehicle under the current working condition; establishing an active suspension model, and determining an execution boundary of an active suspension; based on the control requirement, a control strategy of the active suspension is obtained in combination with the active suspension model and hysteresis characteristics thereof, and it is ensured that the active suspension meets the execution boundary; based on the control strategy, performing multiple experiments on the active suspension model to obtain corresponding performance indexes; and based on the performance indexes, optimizing the control strategy until the active suspension reaches the optimal control performance, and obtaining the optimal control strategy. According to the method, the response hysteresis and the amplitude-frequency attenuation of the active suspension are considered in the control requirement, the active suspension is compensated, and the execution potential of an actuator of the active suspension is fully exerted.

The invention discloses a vehicle queue control method based on an improved gap strategy and a depth deterministic strategy gradient algorithm, and the method comprises the following steps: building a vehicle-to-vehicle gap strategy considering the difference between the speed of a front vehicle and the speed of a vehicle, constructing an MPC frame through the strategy, and adding the minimum safety distance between vehicles into a constraint condition of the MPC frame; adjusting parameters in the MPC framework in real time by using a trained depth deterministic strategy gradient algorithm model to obtain an optimal parameter combination in each state, and then substituting the optimal parameter combination into the MPC framework to calculate and obtain an optimal control sequence at each moment; and finally, longitudinally controlling the vehicles in the vehicle queue according to the specified control quantity in the optimal control sequence of each control period. Compared with a traditional fixed parameter MPC method, the method has the advantages that the motorcade gap error can be remarkably reduced, the stability and safety of motorcade longitudinal control are improved, and efficient and safe automatic driving motorcade management is achieved.

The invention discloses an intelligent deep peak regulation system for a coal-fired power generation unit, and particularly relates to the technical field of power generation control, and the system comprises a multi-source data collection module which carries out the real-time parallel collection of all-dimensional operation parameters through a sensing network and unit decentralized control system interface disposed at a key node, and builds a multi-modal time sequence data flow; according to the method, a foresight control strategy is formed by constructing a fusion prediction model, sensing the running state of a unit in real time and predicting the change trend of key parameters, the defect of response lag of a traditional system is effectively overcome, and the purposes of accurate load tracking and advanced regulation and control under the working condition of deep peak regulation are achieved; a global optimization decision-making mechanism is established by adopting a deep reinforcement learning algorithm, optimal control parameters are calculated in real time through the intelligent peak regulation module, a boiler, a steam turbine and an environmental protection system are dynamically coordinated, the problem of insufficient stability caused by subsystem independent control is solved, and the optimal balance of safety, stability and economic benefits is achieved.

Optimal control of multiple heating, ventilation, and air-conditioning units in an open-plan space demands fast and accurate thermodynamic modeling. Prior methods lack scalability required for effective control in large open-plan offices primarily due to air-mixing interactions. The present disclosure describes a physics-informed graph neural network (PI-GNN) to overcome these challenges. Specifically, thermodynamic interactions are modeled as edges between nodes that represent cells. Further, a modeling approach is used that allows explicit modeling of wall and window surface temperatures which are commonly ignored. The method of present disclosure utilizes PI-GNN as a state-estimator that employs a receding-horizon approach for optimal HVAC control. PI-GNNs are adapted for building HVAC control by incorporating a time-resetting strategy to handle time-dependent ambient conditions and therefore set-points. The method of the present disclosure outperforms a regular PINN model and other baseline control strategies on thermal model accuracy, computation time, energy consumption, and user comfort.

The invention relates to the technical field of power generation system control, and discloses an Agent-driven big data analysis and thermal power generation system operation control method and system, and the method comprises the steps: obtaining the operation data of a thermal power generation system; inputting the operation data into a power system intelligent agent, analyzing the operation state of the thermal power generation system, and calculating optimal control parameters for adjusting the thermal power generation system to the optimal operation state; controlling the operation of the thermal power generation system according to the optimal control parameter so as to adjust the operation state of the thermal power generation system in real time; based on a large amount of thermal power generation system operation data, the high-quality sample is rapidly extracted from the mass data through the power system intelligent agent, deep analysis of the operation state of the thermal power generation system and accurate calculation of the optimal control parameters are achieved, the control efficiency and the adjustment precision of the thermal power generation system are improved, and the practicability is high. The system is always adjusted towards the optimal operation state, and the stability and reliability of system operation are improved.

The invention relates to the technical field of car tipplers, in particular to a remote control method of an intelligent cleaning robot for a cargo compartment. Comprising the steps of generating various cleaning scenes according to historical cargo compartment parameters, and sequentially generating control models of the cleaning scenes; generating an initial cleaning plan according to the equipment parameters of the cargo compartment to be cleaned, and generating a plurality of feedback time nodes according to the initial cleaning plan; monitoring data of the cargo compartment to be cleaned are obtained according to the preset feedback time node, and whether a correction instruction is generated or not is judged according to the monitoring data; a plurality of cleaning scenes are set according to cargo compartment parameters and parameters for carrying coal, optimal control strategies under various cleaning scenes are set according to historical parameters, in the cleaning process of a to-be-cleaned cargo compartment, the to-be-cleaned cargo compartment is monitored by setting a plurality of feedback time nodes, and an initial cleaning plan is dynamically corrected according to a correction evaluation value, so that the cleaning efficiency of the to-be-cleaned cargo compartment is improved. Therefore, the cleaning quality of the to-be-cleaned carriage is ensured.

A vehicle thermal management system includes: a fluid transfer device that consumes power to perform a thermal management function, and a control unit that determines an operation mode of the fluid transfer device. In particular, the fluid transfer device operates in the determined operation mode based on an optimal control value derived using a control model for a predicted state value.

A thermal management system for a vehicle includes a fluid transfer device including a power consumption unit configured to consume power for performing the heat pump function and includes a control unit configured to control the fluid transfer device based on an optimal control value.

The invention provides a dimethyl sulfoxide production parameter control method and system based on reinforcement learning, and relates to the technical field of production parameter control, and the method comprises the steps: obtaining historical process operation data in a dimethyl sulfoxide production process; performing correlation analysis on the historical process operation data, and screening key control variables influencing the yield and the energy consumption; based on the historical process operation data and the key control variables, constructing a digital twinborn simulation model for providing an interaction environment for reinforcement learning; defining a state space, an action space and a reward function, and constructing a dimethyl sulfoxide production parameter control model based on reinforcement learning; based on the reward function and the digital twinborn simulation model, training and optimizing the dimethyl sulfoxide production parameter control model through a PPO algorithm, and outputting an optimal control strategy; and controlling the production parameters of dimethyl sulfoxide according to the optimal control strategy.

The present disclosure relates to an integrated control system for heat pump to optimize energy of solar power generation building, Including a computer comprising a processor and a memory within which a code for execution by the processor is stored, and the computer is configured to collect real-time data measured from at least any one of the heat pump of the solar power generation building, an external environment, a room and a photovoltaic power generation system, where interactions occur in a dynamic behavior of the heat pump; input the real-time data of before pre-set time into multiple dynamic behavior prediction models to derive an indoor air temperature prediction value, heat pump electricity consumption prediction value, and photovoltaic power generation prediction value, of after pre-set time; and search for an optimal control variable of the heat pump using the derived indoor air temperature prediction value, heat pump electricity consumption prediction value, and photovoltaic power generation prediction value.

The invention discloses an energy-saving operation control method for a desulfurization system, and relates to the field of wet flue gas desulfurization of a power plant. The method comprises the following steps: (1) establishing a clean flue gas SO2 concentration prediction model; (2) optimization control of energy-saving operation of the desulfurization system: selecting an optimal feature subset from the selected feature subset, and searching an optimal control value of the optimal feature subset under each working condition; and establishing an energy-saving operation optimization target and constraint conditions of the desulfurization system, and then adjusting operation parameters according to outlet data to ensure energy-saving operation. The method comprises the following steps: carrying out feature selection on desulfurization online operation parameters, and selecting an optimal feature subset to establish a clean flue gas SO2 concentration prediction model; by expanding the data acquisition range, introducing the deep learning algorithm and dynamically updating the data, the SO2 concentration of the purified flue gas can be predicted more accurately, and a reliable basis is provided for optimization control.

The invention discloses a closed-loop automobile crosswind stability dynamic evaluation system based on an MPC control algorithm, and belongs to the technical field of intelligent driving. According to the system, a CFD model unit obtains pneumatic six-component parameters according to a first motion state parameter and a lateral wind environment parameter of a tested automobile at a first moment; the MPC active control unit predicts the state quantity and the control quantity of the tested automobile in a plurality of time steps according to the first motion state parameter on the basis of an MPC algorithm by taking the minimum control cost as a target so as to determine a front wheel steering angle parameter corresponding to the optimal control quantity; and the MBD model unit updates the dynamic parameters of the tested automobile according to the pneumatic six-component parameters and the front wheel steering angle parameters to obtain the dynamic response parameters of the tested automobile at the second moment, and evaluates the crosswind stability of the tested automobile according to the dynamic response parameters. The system is high in reduction degree of actual conditions, and the reliability of test results and the success rate of calculation are improved.

Method for adapting the lateral acceleration of a remotely operated land vehicle to a curved trajectory. One aspect of the invention relates to a computer-implemented method (2) for adapting the lateral acceleration of a land vehicle (V) to a curved trajectory (C) on a surface, the vehicle being remotely controlled. Method (2) comprises a set of iterations for which, in each iteration, optimal longitudinal speed and optimal steering angle setpoints are obtained by solving a constrained finite-horizon optimal control problem. Figure to be published with the abstract: Figure 4.

The invention relates to the technical field of cable production, in particular to a safety early warning method and system for cable production, and the method comprises the steps: responding to the change of environmental parameters at any moment in a cable production process, taking control parameters as particles, taking the sum of production quality and production efficiency as a fitness function, and obtaining a fitness function; obtaining a globally optimal solution by using a particle swarm algorithm; and generating an early warning signal in response to the situation that the variation between the globally optimal solution and the moment control parameter is greater than an allowable variation or the stability of the globally optimal solution is less than a stability threshold. Through the technical scheme of the invention, the optimal control parameter can be quickly found, early warning is realized from two aspects of the variable quantity of the control parameter and the anti-interference capability of the optimal control parameter, and the safety and controllability of the cable production process are ensured.

The invention discloses an oil temperature monitoring and regulating method for an oil-immersed transformer, which belongs to the technical field of control optimization, and comprises the following steps: constructing an oil temperature PID control system of the oil-immersed transformer, introducing an improved candle flame optimization algorithm, and setting oil temperature PID control parameters by using the improved candle flame optimization algorithm. And setting three optimal control parameters obtained through optimization by using an improved candle flame optimization algorithm as parameters of an oil temperature PID controller in the PID control system of the oil-immersed transformer, and optimizing the oil temperature control effect of the oil-immersed transformer. According to the method, the oil temperature PID control system of the oil-immersed transformer is constructed, and the improved candle flame optimization algorithm is introduced, so that the oil temperature of the oil-immersed transformer can be recovered to be stable more quickly when working condition disturbance such as load sudden change or environment temperature change occurs, the insulation aging risk caused by long-time high-temperature operation can be reduced, and the service life of the oil-immersed transformer is prolonged. And the operation safety and reliability of the oil-immersed transformer are enhanced.

The invention discloses a fishery water body environment dissolved oxygen control optimization method, and belongs to the technical field of control optimization, and the method comprises the steps: S1, constructing a fishery water body management dissolved oxygen PID control system; s2, introducing an improved barber optimization algorithm; s3, setting a dissolved oxygen PID control parameter in the fishery water body management dissolved oxygen PID control system by using an improved barber optimization algorithm, and obtaining an optimal PID control parameter through optimization; and S4, setting the three optimal control parameters obtained through optimization by using the improved barber optimization algorithm as parameters of a dissolved oxygen PID controller in the fishery water body management dissolved oxygen PID control system, and optimizing a dissolved oxygen regulation control effect. By introducing the improved barber optimization algorithm, higher optimization precision is realized, and a PID controller parameter solution with better performance is obtained, so that the dissolved oxygen concentration of the water body can be more accurately and stably maintained, and a reliable technical guarantee is provided for fishery water body dissolved oxygen management.

The invention discloses an equipment rotating speed control method and system based on load calculation, and relates to the technical field of combustion engine control, and the method comprises the steps: connecting a load monitoring platform to obtain a load amount, and calculating a load rotating speed demand; the method comprises the following steps: analyzing a single-cylinder methanol engine rotating speed relation network, establishing a network diagram structure, training an adaptive search space based on the structure, extracting relation parameters, and constructing an optimization space; adjusting and calculating according to the load rotating speed demand and the actual rotating speed to obtain an adjusted rotating speed difference; the current control parameters are obtained through the control unit, the control parameters and the adjustment rotating speed difference are used as constraints, fuel consumption and rotating speed stability are optimized, and an optimal control strategy is obtained. The technical problems that in the prior art, a traditional single-cylinder methanol engine is insufficient in rotating speed control precision, large in rotating speed fluctuation and high in fuel consumption are solved, and the technical effects that the rotating speed control precision and stability are improved and the fuel consumption is reduced through self-adaptive control optimization are achieved.

The invention relates to the field of vehicle control, and discloses a range extender control method and device, a vehicle and a storage medium, the method comprises the following steps: obtaining a closed scene category, a closed value and operation information of a target vehicle, the operation information comprising gear information, vehicle speed information, battery remaining capacity and remaining travel; the closed scene category, the closed value and the operation information are input into a preset strategy control model, a target control strategy corresponding to the range extender in the target vehicle in the current closed scene is determined, and the preset strategy control model is trained through a genetic algorithm; the optimal control strategy of the range extender under the closed value and the operation information of each closed scene category is determined; and controlling the range extender in response to the target control strategy. The linkage ecology is constructed according to the closed scene category, the closed value, the operation information and the range extender, multiple control strategies are formed for intelligent switching, and the driving comfort and the user experience are improved.

This application discloses a method, device, medium, and product for active control of aerodynamic noise in the target area during tiltrotor transition flight, relating to the field of noise control. The method includes: during tilt transition conditions, constructing a target noise control area within a set region based on flight mission and environmental requirements, discretizing it into multiple spatial observation points and assigning weights; parameterizing the controllable aerodynamic loads on the blades to obtain control load variables, establishing the relationship between the control load variables and the acoustic response of the target noise control area, determining unknowns, and establishing a global acoustic optimization objective function; using the DDQN algorithm as a solver, optimizing the global acoustic optimization objective function under the premise of satisfying actuator physical constraints to obtain the optimal control law, performing feedforward execution and feedback correction to achieve active control of aerodynamic noise in the target area. This application can solve the main shortcomings and derived problems caused by focusing on single-point noise control.

The invention discloses a washing control method of a pulsator washing machine based on a high-power variable frequency motor, and relates to the technical field of washing machine control, and the method comprises the following steps: setting a plurality of groups of control parameters and generating a plurality of washing schemes according to the type of clothes to be washed, each type of clothes to be washed corresponds to the proportion of clothes of various materials contained in one piece of clothes to be washed; obtaining scheme optimal control parameter data and scheme optimal water quality turbidity change rate data of each cleaning scheme, and scheme-to-clothes type cleaning water quality turbidity change rate data when the scheme cleans clothes types of other cleaning schemes; according to the washing control method of the pulsator washing machine based on the high-power variable frequency motor, control parameters of the pulsator washing machine with the best washing effect, the lowest clothes wear degree and the lowest energy consumption can be analyzed when clothes with corresponding clothes material mixing proportions are washed by different washing schemes; therefore, the clothes washing effect is guaranteed, and meanwhile the clothes abrasion degree and the energy consumption are balanced.

The invention relates to a method and an apparatus for the process-optimized production of manufactured wood boards, in particular oriented strand boards. In order to provide a method and an apparatus for the process-optimized production of manufactured wood products which make it possible to optimize quality while providing continuous quality assurance, which allow the best possible control variables to be determined particularly easily and quickly, and which also guarantee continuous production that results in the lowest possible wastage while maintaining the highest possible level of process stability, the invention provides the following: at least one process parameter, in particular the water content and / or the quantity of adhesive applied to the raw chip material, is measured in the course of the method by means of near-infrared spectroscopy; the measured values of the at least one process parameter are then sent to a control unit for process optimization and / or process-optimized control of the method, wherein the measurement of the at least one process parameter is taken at one or more positions, selected from: a position immediately upstream of the gluing, in particular between a dry raw chip hopper and the gluing station; a position immediately downstream of the gluing, in particular between the gluing station and a chip material distributor and / or a dispersing station; a position in the provision phase immediately downstream of a timber yard, in particular between the timber yard and a subsequent dryer; and / or a position in the provision phase immediately downstream of a dryer, in particular between the dryer and a sieving station and / or a dry raw chip hopper.

A vehicle thermal management system includes a fluid transfer device including an electric power consumption unit, and a control unit for controlling indoor heating through the fluid transfer device on the basis of an optimal control value for the indoor heating.

The invention provides a high-voltage ride-through optimization control method and system for a doubly-fed induction wind generator, and relates to the field of doubly-fed induction wind generators, and the method comprises the steps: respectively obtaining a mathematical model of a rotor side converter and a mathematical model of a power grid side converter; according to the mathematical model of the rotor side converter and the mathematical model of the power grid side converter, constructing an objective function of high voltage ride through optimization control; and obtaining an improved growth optimization algorithm, solving the target function according to the improved growth optimization algorithm to obtain an optimal control parameter, and optimizing the high voltage ride through of the doubly-fed induction wind generator according to the optimal control parameter. According to the method, the high voltage ride through control parameters of the doubly-fed induction generator are optimized, so that a better effect on determining the controller parameters and other variables of the high voltage ride through of the DFIG is achieved.

The invention discloses a resonant fiber-optic gyroscope frequency locking method based on active-disturbance-rejection control, which comprises the following steps: firstly, establishing a mathematical model of output signals of a resonant fiber-optic gyroscope according to the working principle of the resonant fiber-optic gyroscope, and then establishing an active-disturbance-rejection controller structure model suitable for the resonant fiber-optic gyroscope; and finally, building a complete simulation system model in Simulink, and setting partial parameters in the active disturbance rejection controller, thereby obtaining the optimal control effect of the system. Compared with an existing PI control frequency locking method, the active-disturbance-rejection control method has the advantages that the same frequency locking time and gyroscope dynamic range can be obtained, the contradiction between overtonality and rapidity existing in traditional PI control can be balanced, a better tracking effect and angular velocity detection precision can be achieved, and a further thought can be provided for engineering application.