2503 results about "Model predictive control" patented technology

The invention discloses a power distribution management system of an intelligent charging pile, and belongs to the technical field of electric vehicle charging facilities and intelligent power grids. The load prediction module carries out space-time alignment fusion on historical data and real-time monitoring data to generate a power distribution demand prediction value, and the edge calculation controller executes a model prediction control algorithm based on multi-source data to generate a real-time control strategy containing relay time sequence parameters and a capacitance compensation scheme. And the dynamic power distribution adjustment module executes strategy parameters through the solid-state relay array and the parallel compensation capacitor bank. Self-adaptive adjustment of the power distribution network is achieved through a real-time monitoring-prediction-optimization closed-loop control mechanism, the harmonic content of the power grid is effectively reduced, the energy distribution efficiency of the charging pile group is improved, and the method is suitable for intelligent electric energy management of public charging stations and other scenes.

The invention discloses an indoor temperature real-time regulation and control method of a heat distribution pipeline and a control system of the indoor temperature real-time regulation and control method, relates to the technical field of dynamic control of a heat distribution pipe network, and solves the problems of hydraulic oscillation and temperature control hysteresis caused by the fact that local valve regulation neglects whole-network coupling and a first-order linear model is difficult to describe multi-order thermal inertia and large heat capacity in the prior art. According to the scheme, on the basis of pipe network distributed PDE / lumped parameter hybrid modeling and in combination with extended Kalman filtering and unscented Kalman filtering on-line identification, a feedforward decoupling compensation item is generated through spectral decomposition, a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed, a control instruction is issued according to a pump-first and valve-second serialization strategy, and a self-adaptive multi-model predictive control and iterative learning compensation closed-loop structure is constructed. Meanwhile, the model weight and the prediction time domain are dynamically adjusted; according to the method, the global balance capability and the temperature tracking precision of heat distribution pipeline regulation and control are remarkably improved.

The invention discloses a water flow intelligent monitoring system and method, and relates to the technical field of water flow intelligent monitoring, in combination with an actuator health degree evaluation and multi-target prediction control method, the characteristics of a valve and a water pump are finely described by arranging sensors and constructing a digital twinborn model; model predictive control or multi-agent reinforcement learning is adopted to dynamically generate a reliable and efficient control instruction; furthermore, through real-time closed-loop deviation detection and self-adaptive correction, delay, faults or communication abnormity of the actuator can be accurately dealt with; 4, quickly completing self-healing and fault-tolerant control by using a fault prediction and standby scheduling strategy to prevent failure diffusion; and 5, scenes such as rainfall flood, fire fighting and reclaimed water are incorporated into the same framework, and multi-scene coupled comprehensive water resource management is realized, so that water hammer impact and water leakage risks can be effectively reduced, and the safety of the water supply system under complex working conditions or equipment aging is remarkably improved.

The invention relates to a tower crane operation control system based on complex scene three-dimensional real-time modeling. According to the system, a lifting hook is coarsely positioned through a lifting hook positioning and state sensing unit, a real-time position is positioned by combining a laser radar point cloud clustering algorithm with historical pose data, and visual tracking is synchronously performed by means of a tower top camera AI; converting the real-time point cloud data into a 3D voxel grid map, generating a global path by using a 3DA algorithm, and outputting a hoisting track after smooth processing and track optimization; establishing a sling-lifting hook double-pendulum dynamic model, predicting a state sequence based on a model prediction control algorithm, and adjusting a control signal through a feedforward compensation item and a feedback correction item; and the man-machine interaction and monitoring unit is used for displaying the cantilever angle, the lifting hook height and the three-dimensional map of the tower crane in real time and remotely intervening the operation state of the tower crane. According to the system, multi-source data are fused to construct a high-precision three-dimensional map, lifting hook positioning and full-view tracking are achieved, and lifting safety and trajectory tracking precision are improved through path planning and dynamics control.

The invention provides a sewage treatment carbon source intelligent adding method, which comprises the following steps: collecting multi-parameter feed-forward and feedback signals of water inlet and an anoxic tank, constructing a dynamic model containing feed-forward compensation, model prediction control and feedback compensation, calculating the theoretical adding amount of a carbon source, inputting a predicted value and feedback parameters into an LSTM network for correction, and optimizing a network structure by a genetic algorithm. The adding amount is controlled in a closed-loop mode through a variable frequency pump, the LSTM weight is updated on the basis that the error is larger than 5%, and finally a control strategy is optimized by using an NSGA-II algorithm and integrating carbon source consumption, effluent total nitrogen and energy consumption. The dynamic self-adaptive carbon source adding method is constructed by fusing feedforward perception, LSTM prediction, feedback regulation and multi-objective optimization, so that quick response and accurate control on water quality fluctuation are realized, the denitrification efficiency and the carbon source utilization rate are improved, and the method has excellent engineering adaptability and popularization value.

The invention relates to a self-adaptive control rewinding machine tension and coiled material deviation collaborative optimization method in the field of intelligent manufacturing, and the method comprises the steps: deploying a distributed tension sensor network at a key position of a rewinding machine coiled material path, collecting the tension value of each measurement point in real time, and generating a multi-point tension distribution data matrix arranged according to a time sequence; processing the multi-point tension distribution data matrix by adopting a sliding window time sequence analysis algorithm, detecting tension fluctuation abnormity, and if a tension value exceeds a preset threshold range, recording a tension abrupt change timestamp and a change amplitude, and generating tension abrupt change data; based on the working condition state description, the rolling diameter real-time change data sequence and the tension sudden change data, a prediction model reflecting rolling diameter change and tension fluctuation is constructed in real time, and a predicted tension trend is obtained; and comparing the predicted tension trend with a preset ideal tension range through a model prediction control algorithm, and generating a multi-target optimization instruction which comprises a dynamic torque regulation and control quantity and a floating roller position set value.

The invention discloses an intelligent self-monitoring temperature management system for a box-type substation, and relates to the technical field of intelligent power grid equipment monitoring. The problems that an existing system is large in temperature measurement deviation, low in reliability, delayed in early warning, inaccurate in hot spot positioning and extensive in heat dissipation control are solved. According to the scheme, multi-source signals are acquired in parallel through a data acquisition module, and a temperature time sequence is extracted; a boundary calibration module is adopted to fuse data to generate a three-dimensional boundary condition; the multi-physical field solving module obtains an internal temperature / stress field; the physical information prediction module is fused with a heat transfer physical constraint training graph neural network to predict a hotspot migration trend; the hierarchical scheduling module is used for solving a fan and oil pump collaborative optimization instruction in real time based on model predictive control; according to the invention, the accuracy of internal temperature monitoring of the box transformer substation, the reliability of hot spot prediction and the accuracy of heat dissipation control are remarkably improved, the insulation life of equipment is effectively prolonged, and the operation safety and reliability of the system are improved.

The invention relates to the technical field of autonomous navigation and robot environment perception, and discloses a multi-source sensor fused adaptive navigation system, which comprises a multi-source sensor space-time synchronization module, a quantum particle filtering positioning estimation module, a space-time element learning controller module, a cross-modal quantum fusion module and an adaptive navigation control module. Multi-source data space-time alignment is realized through Lie group SE (3) calibration and dynamic time warping; the positioning robustness of particle filtering is improved based on quantum state coding and annealing optimization; dynamically distributing a fusion weight and injecting a physical constraint by utilizing a meta-learning network; feature level fusion of laser radar, vision and inertial data is realized by means of a quantum entanglement mechanism; and constructing closed-loop adaptive navigation by combining model predictive control and quantum purity trigger feedback. According to the method, the navigation reliability problem caused by misalignment of multi-modal sensor data fusion, divergence of state estimation and insufficient cross-modal relevance in a dynamic environment is solved.

The invention provides an unmanned aerial vehicle high-precision autonomous navigation method and system based on GNSS and IMU deep fusion, and aims to solve the problems of insufficient navigation precision and poor robustness in a complex electromagnetic environment. Through a tight coupling architecture, GNSS original observed quantity and IMU pre-integration results are jointly modeled in an observation layer, and multi-source constraints are introduced in combination with factor graph optimization, so that the positioning precision and consistency in weak signal and shielding scenes are remarkably improved. For abnormal observation, a robust kernel function is adopted to dynamically adjust the weight, and the influence of electromagnetic interference and a multipath effect is effectively inhibited. Meanwhile, navigation calculation and model prediction control MPC are combined, and sub-meter hovering and high-precision trajectory tracking are achieved. According to the method, in high-voltage transmission line inspection, dependence on a high-cost sensor is reduced, the engineering application value is high, the method can be widely applied to the fields of electric power inspection, disaster emergency, infrastructure monitoring and the like, and reliable technical support is provided for high-precision autonomous navigation of the unmanned aerial vehicle in a complex environment.

The invention relates to the technical field of control, and particularly discloses a multivariable energy efficiency optimization control system for a heating furnace, which is used for solving the problems of local overheating, non-uniform temperature and difficulty in accurate positioning and compensation of heat loss in the operation of the existing cracking heating furnace. Comprising a parameter detection module, a multivariable coupling modeling and simulation module, an optimization control module and an execution and feedback module. According to the method, dynamic digital twinning is constructed through multi-modal online sensing and data assimilation, a Pareto frontier solution is generated based on model prediction control and improved NSGA-II parallel optimization, and the weight is adaptively adjusted; and when the hot spot / cold spot is triggered, a quadric surface fitting compensation strategy is implemented and issued for execution, so that high-precision simulation prediction, precise closed-loop control and real-time online energy efficiency optimization are realized.

The invention relates to a model predictive control charging optimization method based on a dynamic power state, which initiates a'dynamic power state collaborative optimization 'mechanism, takes a real-time power upper limit as an active optimization target instead of a fixed constraint condition, and breaks through the technical bottleneck of power limitation passive response in a traditional charging strategy. The method specifically comprises the following steps: constructing an electric-thermal-aging multi-physics field coupling model of the lithium ion battery, updating electric-thermal characteristic parameters in real time through an online parameter identification algorithm, and synchronously estimating a core temperature and an aging state in combination with a double-Kalman filtering state observer; innovatively establishing a four-dimensional objective function optimization model containing a dynamic power state, and performing multi-objective collaborative optimization on a power upper limit, a charging speed, a capacity fading rate and a current fluctuation rate; and designing a dynamic rolling optimization algorithm based on a model prediction control framework, and solving the optimal charging current meeting the dynamic power distribution requirement of the power grid in real time under the hard constraint of ensuring the maximum core temperature and terminal voltage.

The invention provides an electric instrument table intelligent control method based on multi-modal perception and model prediction, and relates to the technical field of electric instrument tables, and the method comprises the steps: obtaining high-precision environment perception data through a multi-modal sensor fusion technology, and constructing a dynamic three-dimensional map to recognize an instrument and an obstacle; the system drives the multi-degree-of-freedom mechanical arm to move efficiently through optimal path planning based on model prediction control, the operation period is remarkably shortened, the overall operation efficiency and throughput capacity are improved, meanwhile, potential collision and abnormal stress are monitored in real time in the grabbing and placing process, an intelligent obstacle avoidance and safe shutdown mechanism is started, and the safety of the robot is improved. According to the method, the robustness and safety of system operation are greatly improved, a flexible grabbing strategy is integrated for precious fragile instruments, lossless operation is achieved through real-time force feedback, high-value samples are effectively protected, finally, through machine vision verification and online optimization, the system can continuously conduct self-learning, the operation precision is continuously improved, and the success rate is continuously increased. And the self-adaptive performance is improved.

The embodiment of the invention provides an energy-saving optimization system for heating, ventilating and air conditioning of an indoor ski field. The energy-saving optimization system comprises a multi-source sensing layer, an edge computing layer, a cloud decision-making layer and an equipment execution layer. The multi-source sensing layer is used for collecting multi-source data such as weather, passenger flow, temperature and humidity and equipment state; the edge calculation layer carries out fusion processing on the data and generates a load prediction result through a load prediction mechanism; the cloud decision-making layer generates an optimization control instruction based on a multi-agent deep reinforcement learning and model prediction control optimization strategy; and the equipment execution layer receives and executes the instruction and feeds back the equipment state. Through a multi-layer collaborative optimization architecture, accurate load prediction and equipment intelligent collaborative control are realized, five-stage stepped optimization and a dynamic priority mechanism are adopted, the energy efficiency of the system is remarkably improved, the energy consumption is reduced while the environmental comfort is ensured, and the economical efficiency and the stability of system operation are effectively improved.

The invention relates to the technical field of industrial flue gas purification, and discloses a flue gas denitration pollution reduction and carbon reduction method and system based on model prediction control. The method comprises the following steps: collecting flue gas data through a sensor to obtain a flue gas distribution state; the state is processed through a preset model, and a nitrogen oxide concentration predicted value is determined; judging a regulation and control demand based on the predicted value and generating an adjustment coefficient sequence; calculating an opening value of an ammonia spraying distributor by adopting a particle swarm optimization algorithm, and determining ammonia spraying amount distribution; a control instruction is sent according to ammonia spraying amount distribution, and a temperature uniformity index is evaluated based on temperature feedback data; adjusting optimization algorithm parameters according to the indexes, and generating an optimized ammonia spraying strategy; updating the predicted value and determining the stable range of the denitration efficiency; verifying the ammonia escape concentration in the stable range to obtain a system performance index; and forming a continuous regulation and control sequence according to cyclic feedback of the indexes. According to the invention, accurate dynamic regulation and control of ammonia spraying amount are realized, denitration efficiency and system stability are effectively improved, and the risk of ammonia escape is significantly reduced.

The invention relates to the technical field of equalization control of a battery management system, and discloses a lithium battery hierarchical equalization control method based on a hierarchical model predictive control algorithm, comprising the following steps: constructing a hierarchical control architecture which comprises a state monitoring layer, an equalization decision layer and an execution control layer, each layer realizes cooperative control through closed-loop data interaction; the state monitoring layer collects multi-dimensional state parameters of the lithium battery system, pre-processes the collected data and then transmits the data to the equalization decision-making layer, the equalization decision-making layer constructs a multi-target optimization model based on a hierarchical MPC algorithm, and the model takes SOC consistency, equalization energy consumption minimization and cycle life maximization of the lithium battery system as optimization targets. The abnormal state of the sensor or the balancing module can be identified in time through a fault diagnosis mechanism, and when a fault occurs, a standby model is automatically switched or a balancing task is shared through an adjacent module, so that the system is ensured not to generate abrupt reduction of balancing performance due to the fault of a single component.

The invention discloses a valve actuator self-adaptive control method and system based on artificial intelligence, and relates to the technical field of industrial automation control, the valve actuator self-adaptive control system comprises multiple modules, a data acquisition module uses a multi-modal sensor fusion technology to acquire multi-dimensional data, the acquisition frequency is dynamically adjusted according to the operation state, and the data acquisition module is used for acquiring the multi-dimensional data; the data preprocessing module cleans data by means of a deep convolution auto-encoder and normalizes by using an improved method, the model training module constructs a space-time diagram convolution network model fused with an attention mechanism, adversarial training optimization is adopted, the control strategy generation module is combined with reinforcement learning and model prediction control technologies, stability and energy consumption generation strategies are considered, and the time-space diagram convolution network model is optimized. The instruction execution module executes instructions in parallel through multiple threads, and stable action is guaranteed. The system has remarkable advantages, can accurately collect and process data, can cope with various working condition changes due to the strong self-adaptive capability, has efficient fault diagnosis and processing capability and dynamic evaluation and optimization performance, improves the production efficiency, and brings much convenience to industrial production.

The invention provides a greenhouse environment adaptive regulation and control method and system, and relates to the technical field of environment control, and the method comprises the steps: collecting multi-dimensional environment parameters in a greenhouse; based on the environmental parameters, a preset crop growth period database and weather prediction data, taking minimization of a preset cost function as a target, and adopting a model prediction control algorithm to generate an equipment linkage instruction set in a future preset time period, the preset cost function fusing environmental regulation and control deviation and operation cost; issuing the equipment linkage instruction set to each execution equipment, and executing linkage regulation and control; wherein when the equipment linkage instruction set is generated, a conflict resolution mechanism based on a dynamic priority is adopted to determine an execution sequence of a plurality of equipment instructions; the adaptive regulation and control method integrating multi-source data verification, multi-scale prediction, dynamic priority conflict resolution and multi-target cost optimization improves the accuracy, economy and crop suitability of greenhouse environment regulation and control.

The invention relates to a photovoltaic power grid energy storage optimization regulation and control method based on multi-scale prediction. The method comprises the following steps: A1, obtaining historical power generation data, real-time meteorological data and numerical weather forecast of a photovoltaic power station; a2, generating a multi-time-scale photovoltaic output prediction sequence; a3, establishing an energy storage dynamic model of charge and discharge efficiency, capacity attenuation and operation constraint; a4, generating an energy storage charging and discharging demand curve under different time scales; a5, constructing a multi-time scale coupled optimization model by taking power grid operation cost minimization and renewable energy consumption maximization as targets; a6, updating an energy storage scheduling instruction in a rolling manner based on latest prediction data by adopting a model prediction control framework; a7, monitoring the deviation between the actual photovoltaic output and the power grid load, and dynamically adjusting the energy storage charging and discharging power; and A8, correcting a prediction error through Kalman filtering and a closed-loop feedback mechanism. According to the invention, high-efficiency operation can be realized, and power grid cost minimization and renewable energy consumption maximization can be realized.

The invention provides a virtual power plant energy storage system co-scheduling and control method and system, and relates to the technical field of power management, and the method comprises the steps: obtaining power grid scheduling data, calculating the charge and discharge income, collecting the real-time parameters of a battery, analyzing the performance attenuation law through deep reinforcement learning, determining the initial working parameters, predicting the system state based on a rolling time domain, and obtaining the real-time parameters of the battery. An optimal scheduling scheme is calculated through mixed integer linear programming and model prediction control in combination with a power distribution strategy and is corrected in real time, operation is executed according to a time-phased scheduling instruction and is monitored in real time, and an emergency strategy is started when necessary, so that the economic benefit and the safety of an energy storage system are improved, and the service life of a battery is prolonged.

The invention relates to the technical field of bolt tightening, and provides an intelligent bolt tightening control method and system based on axial force measurement. Comprising the steps of collecting bolt identification and process parameter data, collecting temperature measurement data, and obtaining sound velocity compensation parameters and elastic modulus compensation parameters. And zero-load preloading data are collected and processed to generate zero-load baseline data, and a self-calibration model is obtained. Axial force data and ultrasonic echo data are collected, and an axial force fusion estimation result is obtained. And comparing an axial force fusion estimation result with target axial force data. And when in the target interval, fine twisting control is carried out based on model predictive control and a micro-stepping strategy, and the controllable damping unit is driven to carry out energy absorption and stopping. And acquiring an axial force fusion estimation result when the target axial force is reached, processing the data in the holding stage to obtain an axial force rebound check result, and if the axial force rebound check result exceeds the limit, performing secondary twisting control. According to the scheme, refinement and traceability of the bolt tightening process are achieved.

The invention discloses a lithium battery thermal runaway control and safety response strategy system, which realizes the real-time monitoring and prediction of the internal temperature and voltage state of a battery through thermal-electric coupling modeling and multi-sensing data fusion, and realizes the real-time monitoring and prediction of the internal temperature and voltage state of the battery based on a model prediction control and threshold enhancement strategy. And triggering graded safety response in the early stage of thermal runaway. The system adopts a multi-stage response mechanism including measures of primary early warning, active cooling intervention, emergency power-off fire extinguishing and the like, a gas suppression path and cooling mode switching function is designed, and heat diffusion and spreading and toxic gas harm are effectively suppressed. Compared with a method depending on complex digital twinning or deep learning, the method is simple in structure, rapid in response and low in data dependence, has good system stability and engineering applicability, and can be widely applied to the field of lithium battery safety management of electric vehicle battery packs, energy storage power stations and the like.

The invention relates to the field of optical storage, and particularly discloses an optical storage system cooperative control method and device, a terminal and a medium, and the method comprises the steps: collecting multi-dimensional data in real time, including photovoltaic array data, user side load data, power grid side information data, meteorological data and weather forecast information; generating a photovoltaic power generation power prediction curve, a user load demand prediction curve and a power grid electricity price prediction curve by using the multi-dimensional data through a machine learning model; and taking the current state of the optical storage system and each prediction curve as input parameters, carrying out optimization problem solving based on a multi-objective optimization function and constraint conditions through a model prediction control algorithm, generating an optimal control sequence in a period of time in the future, and controlling corresponding equipment through the optimal control sequence. According to the method, the response speed to uncertainties such as illumination abrupt change and load fluctuation is increased, source-storage-load-network coordination is realized, the sub-optimal problem of independent control of each unit is avoided, the operation cost is reduced, and renewable energy consumption is improved.

The invention discloses a fixed-wing unmanned aerial vehicle trajectory tracking control system and method based on a disturbance observer. The system acquires various state data of the unmanned aerial vehicle through the data acquisition module, the interference observer module estimates external interference in real time based on a dynamic model, the trajectory planning module generates an optimized trajectory according to tasks and environments, and the controller module realizes accurate control by adopting a model prediction control and sliding mode control composite strategy. And the execution mechanism driving module executes the control instruction. The method comprises the steps of data acquisition, interference estimation, trajectory planning, controller design and calculation, actuating mechanism driving and the like. The method can effectively improve the trajectory tracking accuracy and stability of the fixed-wing unmanned aerial vehicle in a complex environment, and has a wide application prospect.

The invention provides a data center hybrid energy storage and renewable energy intelligent scheduling control method and system, and relates to the technical field of data energy storage and resource scheduling, and the method comprises the steps: carrying out the load prediction through a double-layer time sequence memory network in combination with a multi-head attention mechanism, building a hybrid energy storage optimization scheduling model based on a renewable energy generation power prediction result, and carrying out the optimization scheduling of the hybrid energy storage. And a double-layer model predictive control framework is adopted to output a corrected charging and discharging power instruction, so that optimal control of the hybrid energy storage system is realized, the energy utilization efficiency is improved, and the operation cost of a data center is reduced.

The invention discloses a high-precision linear vibration feedback motor system, and relates to the technical field of precise electromagnetic driving, and the system comprises a core driving module which is a moving magnet type linear motor of a Halbach array magnetic circuit structure, and is integrated with a multi-physics field sensing unit; the dynamic parameter tracking module is used for acquiring a reed rigidity coefficient, a damping coefficient and a magnetic constant in real time when the motor runs, and establishing a dynamic displacement model based on a recursive least square method; a double-closed-loop control framework is adopted, an inner loop adopts field-oriented control, and an outer loop is based on model prediction control; the aging prediction unit activates a temperature rise compensation algorithm when the accumulated number of vibration times is greater than a set threshold value; the self-adaptive calibration engine is used for injecting a sweep frequency excitation signal when the system is started, and automatically compensating individual difference parameters according to the harmonic peak offset; and the digital twin mapping unit is used for establishing a real-time mapping relationship between the physical parameters of the motor and the virtual model, and dynamically correcting the model parameters by comparing the actual displacement with the model displacement.

The invention belongs to the technical field of highway tunnel ventilation control, and provides a highway tunnel ventilation control method and system. The method comprises the following steps of multi-source heterogeneous data acquisition, multi-source data fusion, state estimation, traffic flow prediction, dynamic pollution index calculation and multi-target weight generation, rolling optimization with constraints, control execution, model updating and feedback correction. The state sensing precision is improved through multi-source data fusion, real-time accurate estimation and error reduction of a pollutant concentration field and a traffic flow state are realized through a heterogeneous sensor and a Rayleigh fusion technology, the problem of sensing deviation caused by single data in a traditional method is solved, future traffic flow is predicted based on a graph neural network, and the traffic flow sensing accuracy is improved. The fan power is optimized through model prediction control, the ventilation intensity is adjusted in advance, pollutants are prevented from exceeding the standard, the response speed is increased compared with threshold value control, and energy consumption is reduced.

The invention discloses a three-level inverter dual-tracking model prediction control method and an inverter, and the method comprises the steps: constructing an integral sliding mode observer, and obtaining a current prediction value and a lumped disturbance estimation value through the integral sliding mode observer; calculating a voltage error of the inverter, and substituting the voltage error into the PI controller to obtain a voltage tracking control item; based on the voltage tracking control term, a switching state that minimizes the cost function is selected to control the inverter. The integral sliding mode observer is configured to calculate a current prediction error between a current prediction value of a current control period and a system output current, introduce an integrator to track the current prediction error, and generate an error function based on an output result of the integrator and the current prediction error. And feeding back a current predicted value and a lumped disturbance estimated value of the next control period through an error function. According to the invention, the method can achieve the quick estimation of the operation state of the inverter and the robust compensation of disturbance, reduces the steady-state tracking error, and reduces the total harmonic distortion of the inverter.

The invention provides a wind power plant AGC optimization control method and system based on model prediction control, and relates to the technical field of model control, and the method comprises the steps: building an adaptive prediction model, carrying out the online identification of parameters through a recursive least square method, constructing an active power optimization objective function, solving a control instruction sequence, designing a feedback compensator, and correcting a control instruction. And finally, issuing to each fan to execute and realize closed-loop control. According to the method, the AGC control precision of the wind power plant can be improved, the power fluctuation is reduced, and the power grid friendliness and the adaptability of an AGC control system are enhanced.

The invention relates to the technical field of robot control, particularly discloses a path planning and dynamic obstacle avoidance control method for multi-task collaborative operation of an industrial robot, and aims to solve the problems of task scheduling conflict, dynamic obstacle response lag and low collaborative efficiency in a multi-robot system. The method comprises the following steps: constructing a task-resource joint scheduling model and generating initial task allocation; planning a conflict-free collaborative path based on an improved space-time A star algorithm; predicting a dynamic obstacle trajectory by using an LSTM network and generating a space-time envelope; constructing a second-order safety barrier function fusing task priorities; local obstacle avoidance re-planning is realized through rolling horizon model predictive control; and the global rescheduling is triggered when the task delay exceeds the limit or the deadlock risk occurs. According to the technical scheme, closed-loop linkage of global task collaboration and local dynamic obstacle avoidance is realized, and the system collaboration efficiency, the obstacle avoidance success rate and the operation robustness are remarkably improved.

The invention discloses an automatic spraying device for inkjet printing equipment, belongs to the technical field of spraying, solves the problem that existing equipment can only process planes and cannot process curved surfaces and irregular surfaces, and comprises a workbench with a rotary clamping piece, a multi-dimensional adjustable spraying assembly and a processing unit. During working, the clamping piece fixes a workpiece and is driven by the motor to rotate, the laser scanner scans the workpiece to generate a three-dimensional model, and dynamic digital twin bodies are constructed in combination with equipment parameters; and the processing unit drives the nozzle to dynamically adjust the position and attitude under the cooperation of the motor through surface parameterization, trajectory planning and model prediction control, so that the optimal inkjet distance is ensured, and a multi-sensor fusion and error monitoring mechanism can correct the deviation in real time and automatically update the model and the trajectory when exceeding the limit. According to the invention, accurate spray painting of special-shaped workpieces with curved surfaces, concave surfaces and the like is realized, the spray painting precision and stability are improved, the application scene is widened, and the manual intervention requirement is reduced.