80 results about "Optimum control" patented technology

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 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.

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.

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 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.

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.

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 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 the technical field of intelligent power grid operation control, and discloses a photovoltaic power station intelligent control method and system based on big data, and the method comprises the steps: obtaining temperature distribution characteristics; determining a performance change trend, and performing dynamic compensation according to the performance change trend to obtain a preliminary adjustment signal; self-adaptive optimization judgment is carried out, and a self-adaptive optimization instruction is generated; the optimal control parameters are predicted, real-time updating is carried out according to the optimal control parameters, and the updated power generation efficiency is obtained; performing expected judgment on the power generation efficiency, and extracting residual data in a system log to obtain a refined performance association rule; and S12, updating the performance mapping model, and returning to the step S12 until the power generation efficiency exceeds a preset efficiency value, thereby obtaining the normally operating system. According to the method, the performance change of the photovoltaic module can be accurately sensed and the power factor control parameters can be adaptively adjusted under the dynamic temperature change of the photovoltaic power station.

A road surface painting device for painting a road surface by spraying paint is comprised of a plurality of frames, a trailer having a pair of driving wheels at the rear end and a front end connected to a towing vehicle for towing, a moving part provided on the trailer to be movable in the forward / backward and left / right directions, a spraying part provided on the moving part and spraying the paint in a straight pattern through a paint spraying nozzle, an inspection part provided on the trailer and inspecting the painting state of characters or symbols painted on the road surface by the spraying part, and a road surface sensing part provided on the moving part and detecting the condition of the road surface including the distance to the road surface, wherein the paint spraying nozzle may be provided to be movable along the up / down direction so that the distance between the paint spraying nozzle and the road surface is constant according to the distance to the road surface. This project (result) is the result of the Local Government-University Cooperation-based Regional Innovation Project conducted in 2024 with funding from the Ministry of Education and support from the National Research Foundation of Korea. (Daegu Gyeongbuk Regional Innovation Platform 2022RIS-006)

The application discloses a kind of multi-effect rectification variable condition intelligent card edge optimization system, comprising: card edge optimization module, for card edge optimization control, including: when starting production, the stability judgment result of current multi-effect multi-stage rectification tower system is obtained;If system is judged to be stable, control variable and observation variable are obtained;Using particle swarm optimization algorithm, control variable is searched, and predicted product quality is obtained using quality inspection module, in particle swarm optimization algorithm, control parameters are iteratively updated by optimization function, and finally the best control variable of current system is obtained;Stability monitoring module is used for real-time dynamic calculation of the stability value of each monitoring point, and the stability judgment result of multi-effect multi-stage rectification tower system is obtained;Time series analysis module is used for obtaining variable and its influence time affecting product quality;Quality inspection module includes soft measurement model, for inputting control variable and observation variable, and outputting predicted product quality.

The invention provides a vehicle suspension open-loop control method, system and equipment based on road preview, which are applied to the technical field of vehicle suspension control, and the method comprises the following steps: obtaining road elevation data in a vehicle longitudinal preview range, and determining a target road type and target road related information according to the road elevation data; the target road type comprises a continuous random road and / or a mixed road, the continuous random road and the mixed road have different control requirements on the suspension, and the target road related information is used for representing the road grade and / or the road amplitude corresponding to the road in the longitudinal preview range; determining a matched target optimal control current in a suspension control law according to the target road related information, and transmitting the matched target optimal control current to a suspension system module, so that the suspension system module performs open-loop control on a suspension based on the target optimal control current; the suspension control law comprises various kinds of different road related information and the optimal control current corresponding to each kind of road related information. By adopting the technical scheme, the calculation load can be reduced.

The application relates to a predictive control method and device based on an unknown parameter model, wherein the method comprises the following steps: parameter setting is performed on an unknown parameter model used for simulating a controlled real system to obtain model parameters; the model parameters of the unknown parameter model are updated through an adaptive observer, and a terminal domain of the unknown parameter model is calculated; based on the model parameters of the unknown parameter model, the adaptive observer and the terminal domain of the unknown parameter model, iterative calculation of a control quantity of the controlled real system is repeatedly performed, and when the number of times of the iterative calculation reaches a preset total iteration number, an optimal control quantity of the controlled real system is obtained. Through the application, the problem of how to perform predictive control based on an unknown parameter model is solved, the model parameters are updated based on the adaptive observer and are input into the unknown parameter model, the best control quantity is calculated by the model using the terminal domain, the robustness of the system is improved, and good control performance is achieved under the condition that the model parameters are unknown.

An adaptive traffic light-vehicle coordinated rolling control method based on deep reinforcement learning is disclosed, belonging to the field of intelligent transportation technology. This invention addresses the problems of low intersection traffic efficiency and high vehicle energy consumption in existing control methods. By using a single agent to control one traffic flow and multiple agents to control the intersection, this invention achieves more precise and flexible signal control, eliminating the need for two traffic flows to terminate simultaneously. Both signal timing and vehicle trajectory adjustment employ multi-agent deep reinforcement learning. In the signal timing phase, a long short-term memory neural network is used to fully exploit the temporal features of the traffic light agent's state information. In the vehicle trajectory adjustment phase, a multi-head attention mechanism is used as a feature extractor to extract key features from the state information, enhancing the agent's understanding and utilization of these features. The optimal control scheme is obtained through the interaction between the signal timing module and the vehicle trajectory adjustment module. This method can be applied to intelligent transportation systems.

The invention is suitable for the technical field of skin treatment devices, and provides a control circuit and a control method of a skin treatment device, the skin treatment device and a medium. According to the ultrasonic transduction drive circuit of the skin treatment device, the ultrasonic transduction drive circuit is controlled to sequentially output N sets of alternating currents by sequentially outputting N sets of initial control signals, and an ultrasonic transducer can sequentially output N sets of ultrasonic energy according to the N sets of alternating currents. A detection unit is used for sampling in the process that the ultrasonic transducer outputs N groups of ultrasonic energy in sequence, and N groups of sampling information are obtained. According to the N groups of sampling information, the corresponding sampling information and the corresponding optimal control signal when the ultrasonic transducer works at the optimal frequency point can be determined through the control unit. The optimal control signal is used for controlling the ultrasonic transducer to work at the optimal frequency point, so that the target ultrasonic energy is output, and the compatibility of the skin treatment device is effectively improved.

An air-conditioning system includes a temperature adjuster to adjust temperature in an air-conditioned target area, a humidity adjuster to adjust humidity in an air-conditioned target area, a sensor to obtain environment information including the temperature or the humidity in the air-conditioned target area, and a controller The controller includes an estimator to estimate time variations in the temperature and the humidity in the air-conditioned target area based on the environment information, target values of the temperature and the humidity, and a control schedule indicating time variations in outputs of the temperature adjuster and the humidity adjuster, a control determiner to optimally calculate the control schedule and determine an optimal control schedule based on the time variations in the temperature and the humidity and the target values, an adjuster controller to control the temperature adjuster and the humidity adjuster based on an optimal control schedule determined by the control determiner.

To provide optimal control of the piston trajectory in a free-piston combustion engine. [Solution] Various embodiments of this disclosure relate to free-piston combustion engines. As described herein, methods and systems are provided to achieve desired engine performance by displacing a free-piston assembly, repeatedly determining the position-force trajectory over the propagation path, and causing the displacement of the free-piston assembly based at least partially on the position-force trajectory. In a dual-piston assembly free-piston engine, synchronization of two piston assemblies is provided.

The invention discloses a follow-up helmet high-precision driving control system and method based on artificial intelligence, and relates to the technical field of driving control. A target tracking sensor is used for collecting original data of a target, and a body state sensor arranged in a helmet is used for collecting motion state data of the helmet; generating a transformation matrix from a sensor coordinate system to a stable world coordinate system, and processing the target original data by using the transformation matrix to obtain target real data; inputting historical real data into the neural network, training to obtain a prediction model, and calculating by using the real data to obtain a prediction target instruction; fusing the feedforward control quantity and the feedback control quantity to obtain a final control instruction; the compensator compensates the final control instruction to obtain an optimal control instruction; and feeding the prediction error back to the prediction model for optimization training.

A control system of a work machine includes a hydraulic pump that is rotationally driven by a prime mover and supplies hydraulic operating oil to a hydraulic actuator, a displacement control unit that controls the displacement of the hydraulic pump according to a control input, a displacement sensor that senses a displacement actual measured value of the hydraulic pump, and a controller that acquires a displacement target value of the hydraulic pump and outputs the control input according to the displacement target value to the displacement control unit. A mathematical model for predicting the displacement of the hydraulic pump is stored in the controller. The controller computes a displacement predicted value of the hydraulic pump by using the mathematical model and computes the optimum control input on the basis of the displacement predicted value and a constraint on the control input. The controller computes an evaluation value on the basis of the displacement target value and the displacement actual measured value and changes the constraint on the control input depending on the evaluation value.

The application discloses a gain scheduling intelligent closed-loop control method based on a permanent magnet synchronous motor, wherein under different working conditions, the gain and error accumulation can be intelligently controlled. The control structure can guarantee the independence of the speed instruction input to the output and the interference input to the output, so that the system has better reference trajectory tracking and interference suppression capability. In order to obtain the best control performance when the parameters are uncertain, a gain scheduling closed-loop control system is used in the advanced pre-input system. Compared with the controller without the controller, the steady-state error is smaller and the dynamic response is better under the condition of external load torque interference and permanent magnet synchronous motor parameter uncertainty.

The application discloses a heuristic cluster prediction control method for a multi-unmanned aerial vehicle system, which utilizes an initial solution to screen a control space, and solves the calculation amount problem of the prediction control. The initial solution is determined by a correction gradient of the system at present, and indicates a rough direction for the next motion of the unmanned system. The method can greatly reduce the calculation amount and minimally affect the optimal control input of the system. A new obstacle avoidance mechanism based on a relative speed direction is proposed, which can ensure that the unmanned system safely and smoothly avoids the obstacles. The mechanism is applicable to a simple static obstacle scene, and can effectively deal with the obstacle avoidance problem of dense dynamic obstacles.

The invention discloses a waterproof coiled material processing roller surface temperature control optimization method based on swarm intelligence, and belongs to the technical field of control optimization, and the method mainly comprises the steps: S1, constructing a waterproof coiled material processing roller surface temperature PID control system; s2, introducing an improved cloud drift optimization algorithm to construct an improved cloud drift optimization algorithm module; s3, performing iterative optimization by using an improved cloud drift optimization algorithm to obtain an optimal control parameter; and S4, the optimal control parameters obtained through off-line optimization serve as static operation parameters to be solidified into a temperature PID controller module, and steady-state precision control over the roller surface temperature is achieved. According to the method, the improved cloud drift optimization algorithm serving as a swarm intelligence algorithm is introduced, off-line global setting is carried out on the roll surface temperature control PID parameters in the waterproof roll machining process, and the parameter matching precision and the system evolution stability of a control system facing a large-lag and strong-nonlinearity heat conduction model are effectively improved.

An electronic device of various embodiments of the present disclosure can include a communication circuit communicably connected to a solder paste printing device configured to print a solder paste on a plurality of substrates based on a plurality of control parameters, and a detection device configured to detect a state of the solder paste printed on the plurality of substrates, one or more memories, and one or more processors. The one or more processors according to various embodiments can obtain a first set of control parameters of the solder paste printing device for printing the solder paste on a first substrate, transmit information indicating the first set of control parameters, obtain first solder paste detection information indicating a state of the solder paste printed on the first substrate, determine a first yield for the first substrate based on the first solder paste detection information, and generate a model for exploring an optimal set of control parameters based on a first pair of data including the first set of control parameters and the first yield.

A relative motion control planning method considering terminal velocity constraint comprises the following steps: step one, determining the feasible interval range of control planning transfer time; step two, according to the feasible interval range of control planning transfer time, determining the traversal time interval to obtain the trial point of iterative predictive control planning calculation; step three, according to the obtained trial point, using the method of iterative predictive control planning to traverse all results in the feasible interval range to obtain the terminal state after relative motion control at each trial point; step four, in the series of terminal states obtained in step three, according to the terminal optimization condition, determining one group as the optimal solution. The present application optimizes the optimal control planning position meeting the terminal velocity constraint condition through the iterative predictive comparison in a certain feasible control planning time interval, and calculates the corresponding control planning quantity, which can be applied to various spacecraft on-orbit space rendezvous and docking and other task scenarios.

The application 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, which comprises the following steps: obtaining operation data of a thermal power generation system; inputting the operation data into a power system agent to analyze the operation state of the thermal power generation system and calculate optimal control parameters for adjusting the thermal power generation system to an optimal operation state; and controlling the operation of the thermal power generation system according to the optimal control parameters to adjust the operation state of the thermal power generation system in real time. Based on a large amount of operation data of the thermal power generation system, the power system agent can quickly extract high-quality samples from the massive data, realize in-depth analysis of the operation state of the thermal power generation system and accurate calculation of the optimal control parameters, improve the control efficiency and adjustment accuracy of the thermal power generation system, ensure that the system is always adjusted towards the optimal operation state, and improve the stability and reliability of system operation.

The invention provides a left-turn planning method and system for a T-junction without a signal lamp, and the method comprises the steps: obtaining traffic scene information, and constructing a traffic simulation scene according to the traffic scene information; constructing a planning control network, wherein the planning control network comprises a state space, an action space and a reward function; and setting a state space, an action space and a reward function according to the traffic simulation scene, then generating an optimal control action by using the planning control network, and sending the optimal control action to the corresponding vehicle. According to the invention, the problem that an automatic driving system is difficult to make an accurate and safe passing decision in a complex traffic environment of left turning at a T-shaped intersection in the prior art is solved.

The invention discloses a low-resistance butterfly valve design method based on multi-control-point optimization. The problem that in the prior art, butterfly valves are large in pressure loss and energy loss is solved. The low-resistance butterfly valve design method comprises the steps that S1, control point area division is conducted, specifically, an axisymmetric valve plate is selected, the front end face or the rear end face of the axisymmetric valve plate is divided into a left control point area and a right control point area with the vertical plane where the axis of a valve shaft is located as the boundary during division; s2, control point design: selecting a left control point area or a right control point area, and arranging control points at equal intervals from inside to outside in the diameter direction of the axis of the vertical valve shaft; s4, drag reduction rate analysis is carried out, and an optimal control point combination is selected; and S5, optimizing the valve plate. According to the low-resistance butterfly valve, multiple control points on the valve plate and the valve shaft are combined and optimized, the low-resistance butterfly valve is obtained, and the resistance reduction effectiveness of the new butterfly valve is verified under different import Reynolds numbers and different nominal diameters; the problem that in the prior art, butterfly valves are large in pressure loss and energy loss is solved.