134 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.

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 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.

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 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.

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 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.

The invention discloses a multi-effect rectification variable working condition intelligent card edge optimization system, which comprises a card edge optimization module used for card edge optimization control, and comprises the following steps: obtaining a stability judgment result of a current multi-effect multi-stage rectification tower system at the beginning of production; if the system is judged to be stable, acquiring a control variable and an observation variable; performing parameter search on the control variables by using a particle swarm optimization algorithm, acquiring predicted product quality by using a quality inspection module, and iteratively updating the control parameters through an optimization function in the particle swarm optimization algorithm to finally obtain the optimal control variable of the current system; the stability monitoring module is used for dynamically calculating the stability value of each monitoring point position in real time and obtaining the stability judgment result of the multi-effect multi-stage rectifying tower system; the time sequence analysis module is used for obtaining variables influencing the product quality and the influence time of the variables; and the quality inspection module comprises a soft measurement model and is used for inputting a control variable and an observation variable and outputting predicted product quality.

The invention specifically discloses a compliance control method and device for a power transmission tower operation and maintenance robot and a medium, and the method comprises the steps: determining an impedance control model of a robot impedance controller, carrying out the adaptive optimization of impedance control parameters based on an improved firefly algorithm, and enabling the improved firefly algorithm to be based on a standard firefly algorithm, and a self-adaptive step length factor is adopted for fusion, meanwhile, an improved algorithm of an annealing mechanism is introduced, and finally, the robot is controlled based on optimized parameters. The dynamic response characteristic of the mechanical arm is continuously tracked, the improved firefly algorithm is used for updating impedance parameter configuration in real time, the improved firefly algorithm improves the global search capability and the convergence speed, the optimal control effect is achieved, the adaptive capacity of the system to diversified operation scenes is improved, and the system performance is improved. A parameter optimization strategy based on real-time data analysis successfully overcomes the adaptability problem caused by parameter solidification in conventional impedance control, and the stability and safety of the power transmission tower operation and maintenance robot in the movement process are improved.

A system for managing chassis and driveline actuators of a motorized transportation vehicle includes a control module executing program code portions that cause a sensor to obtain transportation vehicle state information, receive driver input and generate a desired dynamic output based on the driver input and the transportation vehicle state information, then estimate actuator actions based on the transportation vehicle state information, generate one or more control action constraints based on the transportation vehicle state information and the estimated actuator actions, generate a reference control action based on the transportation vehicle state information, the estimated actions of the one or more actuators, and the control action constraints, and integrate the transportation vehicle state information, the estimated actuator actions, the desired dynamic output, the reference control action, and the control action constraints to generate an optimal control action that falls within a predefined actuator capacity range and ensures driver control of the transportation vehicle.

The present application provides a method for automatic adjustment of the blade angle of a dual-axis dual-duct compressor tester, which belongs to aviation engine testing. The method includes: obtaining a speed-angle relationship curve of the compressor blade speed and the blade angle, and obtaining a target blade angle according to the current speed of the compressor blade and the relationship curve; constructing a blade angle automatic adjustment system model based on the target blade angle as output and the given blade angle as input, and constructing a blade angle given angle transfer function in the blade angle automatic adjustment system model based on PID control; constructing a multi-objective optimization function of the blade angle given angle transfer function; optimizing the multi-objective optimization function based on a genetic algorithm to obtain the PID control optimal control parameters of the blade angle given angle transfer function, and automatically adjusting the blade angle of the compressor tester based on the PID control optimal control parameters.

To provide a vehicular control apparatus capable of optimally performing lockup clutch connect / disconnect control during an automatic operation travel.SOLUTION: An electronic control apparatus 80 includes: an automatic operation control part 88 for controlling an automatic operation travel; a reaching prediction period calculation part 94 for calculating an inter-vehicle distance reaching prediction period Td or a vehicle speed reaching prediction period Tv that is required to reach a target inter-vehicle distance Dt or a target vehicle speed Vt with an inter-vehicle distance D with a preceding vehicle or a vehicle speed V in a case where the vehicle is in an automatic operation travel with an LU clutch 36 engaged; and a first disengage-engage control part 92 for disengaging the LU clutch 36 in a case where the inter-vehicle distance reaching prediction period Td or vehicle speed reaching prediction period Tv is larger than a preset target reaching period Tg, and continuing the LU clutch 36 engaged in a case where the inter-vehicle distance reaching prediction period Td or vehicle speed reaching prediction period Tv is equal or smaller than the target reaching period Tg.SELECTED DRAWING: Figure 3

The invention belongs to the technical field of structure deviation detection, and particularly discloses a steel box girder pushing deviation real-time detection method based on image recognition, and the method comprises the steps: employing an optical target network and a multi-view camera array which are arranged on a top plate and a web plate of a steel box girder; according to the method, synchronous measurement of transverse deviation, vertical deflection and a torsional deformation angle and three-dimensional shape dynamic reconstruction are achieved, interference of complex environments such as vibration and shielding is effectively overcome by constructing a vision-inertia tight coupling fusion model, and then high-precision three-dimensional space pose real-time monitoring is achieved. And meanwhile, a time sequence prediction algorithm is introduced to predict the future deformation trend of the beam body and the occurrence time point of critical deformation, the technical span from passive monitoring to active prediction is achieved, finally, the pushing offset is calculated by integrating the current deviation, the prediction increment and the safety margin, and an early warning signal is triggered. And therefore, the optimal control opportunity can be mastered accurately.

This invention discloses a dynamic vibration reduction control method. The method involves acquiring parameter combination data of the vibration reduction equipment and inputting it into computational fluid dynamics software for calculation, outputting combined data of the results. Using the combined data of the results as independent variables and the expected loss as the dependent variable, a deep neural network is used for training to obtain a vibration loss function. An optimization algorithm is used to obtain parameter combination data with the minimum expected loss, and then an optimal vibration reduction control strategy function is constructed. The difference between the expected loss and the minimum expected loss is calculated to obtain the expected benefit. The work required to overcome external forces when the appendage switches from normal operating conditions to optimal control conditions is obtained to obtain the control cost function. An exhaustive search or swarm intelligence method is used to obtain the combination that maximizes the comprehensive evaluation function. The optimal comprehensive control strategy function under different flow velocities is obtained. The optimal comprehensive control strategy function is deployed to the controller of the vibration reduction equipment, and the relative position of the appendage is dynamically adjusted according to the measured different inflow velocities.

The invention discloses an adaptive control optimization method based on deep learning and reinforcement learning, and relates to the technical field of industrial intelligent control, and the method comprises the steps: constructing a mask matrix according to collected data, combining a multi-scale time sequence convolution layer and a graph convolution network with the mask matrix of the data, generating a predicted value of a future step number, and carrying out the prediction of the step number. According to the method, a space-time fusion deep learning model is constructed, a state vector is constructed according to a predicted value of the space-time fusion deep learning model, and an optimal control strategy is generated according to the state vector. Through embedded cooperation of space-time fusion deep learning and reinforcement learning, a prediction-decision-execution full-closed-loop industrial control architecture is constructed; for a complex industrial scene of sensor data partial missing and multivariable nonlinear coupling, reasoning-control integration is realized in an edge controller, and adaptive control optimization with high precision and strong robustness is realized.

The disclosure relates to the optimization of a movement control of a device, wherein the device is suitable and configured for picking up objects and putting them back down. The present solution proposes to use mixed-integer linear programming methods to assign objects to target positions and to assign shipments to order pickers.

The invention relates to a crystallizer vibration control method, and belongs to the technical field of continuous casting methods in the metallurgical industry. According to the technical scheme, the method comprises the steps that parameter combinations of different steel types, casting machine pulling speeds, crystallizer amplitude and vibration frequencies are divided into different environments, and interval division is conducted on all the parameters; setting a coupling adjustment control scheme of amplitude and vibration frequency and selecting an optimal control scheme or a random scheme; calculating the income of the control scheme in the environment and updating the expected income; and the control process is repeated until the optimal control state is achieved. The method has the beneficial effects that the vibration parameters of the crystallizer in the continuous casting production process are optimized by dynamically adjusting the coupling control scheme of the amplitude and the vibration frequency of the crystallizer, meanwhile, normal demolding of a casting blank is guaranteed, and the surface quality of the casting blank is improved; automatic optimization of the vibration parameters of the crystallizer is effectively achieved, and dynamic optimization adaptation of the model can be achieved according to new production data.

A prediction control device according to an embodiment disclosed herein may include a data acquisition unit acquiring data related to a base material onto which slurry is loaded by using a coating die; and a processor analyzing slurry loading characteristics based on the data, deriving a candidate control value including slurry temperature on the basis of the slurry loading characteristics, predicting a quality of the candidate control value by using a prediction model, and deriving an optimal control value based on the quality.