633 results about "Predictive controller" patented technology

The invention relates to the technical field of clean environment control, in particular to a dynamic pipe network and model predictive control clean room energy-saving pressure control method and system. Obtaining current pipe network impedance according to the pipe network impedance curve; calculating the current valve impedance according to the valve opening; calculating the total pressure drop of the current pipe network; constructing a fan dynamic model based on a fan similarity law to calculate the air volume at the next moment; a clean room pressure difference dynamic model is constructed, and the clean room pressure difference at the next moment is calculated; constructing a state space model and a target function of a model prediction controller; and when the pressure difference of the clean room is unstable, predicting the change trend of the pressure difference of the clean room in the next time period, and reversely solving the optimal fan frequency and valve opening of the discretization state space model by using the model prediction controller so as to achieve the lowest pipe network impedance and obtain the optimal fan frequency. The control precision and robustness of the pressure difference of the clean room are remarkably improved, and meanwhile energy consumption is reduced.

The invention discloses a biped robot reinforcement learning control method which comprises the following steps: after a model prediction controller receives a walking instruction, outputting expected angle data of each joint of a robot to a reinforcement learning neural network, and meanwhile, returning joint angle data of an actual strategy of the reinforcement learning neural network to the neural network by a sensing system at a bottom layer; a joint angle taking time as a sequence and planned by model prediction is compared with a joint angle actually generated by a neural network, and model prediction control is fused into a reinforcement learning training process by setting a reward function for punishment, so that the reinforcement learning training efficiency is improved, and a stable gait is more quickly achieved. Excellent gaits planned by the MPC are transplanted into reinforcement learning control, and control robustness can be improved under the condition that the excellent gaits of the MPC are reserved; and meanwhile, the joint angle data which is obtained by taking time as a sequence and is obtained by taking MPC as a planner can accelerate the reinforcement learning training process, so that the training speed and the control effect of reinforcement learning are greatly improved.

The invention discloses an aircraft attitude control method and device based on a semiconductor microcomputer system, and the method comprises the steps: collecting the multi-source sensor data of an aircraft, calculating an attitude error vector, and constructing a three-dimensional error space coordinate system; taking the three-dimensional error space coordinate system as a reference, adjusting the model in real time, and solving the optimal attitude adjustment amount in a prediction window period; the optimal attitude adjustment amount is input into an execution mechanism distribution module, and a thrust adjustment instruction sequence and a control surface deflection angle instruction queue of each vector propeller are generated through calculation in combination with a propeller response matrix and a control surface efficiency coefficient; dynamically correcting and predicting weight parameters of the controller through closed-loop verification; and updating the multi-objective optimization model based on the corrected parameters, adjusting the instruction distribution proportion through a dynamic reconfiguration interface, and writing the updated parameters into the attitude database to form a complete closed-loop period. According to the invention, high-precision real-time control of the attitude of the aircraft is realized, and the flight stability and the energy efficiency ratio in a complex environment are improved.

The invention relates to the crossing field of mechanical engineering and automatic control, in particular to a global path planning and anti-swing control method for a ship unloader, and aims to solve the problems of rigid path planning and poor synergism of out-of-control swing of a lifting appliance in traditional ship unloading operation. The method comprises the following steps: constructing a dynamic three-dimensional operation space model with fusion of a laser radar and binocular vision, and updating obstacle information in real time; an improved fast extended random tree algorithm is adopted to generate a high-smoothness initial path; establishing a six-degree-of-freedom lifting appliance swinging dynamic model and identifying parameters on line; path tracking and anti-swing torque are synchronously optimized through a model prediction controller, and the control period is smaller than or equal to 20 milliseconds; and closed-loop feedback correction is implemented by combining an encoder and an inertial measurement unit. According to the scheme, environment dynamic sensing, path-anti-swing depth cooperation and multi-disturbance self-adaptive compensation are achieved, the operation rhythm is improved by 30% or above, the system still operates stably under the 8-level wind condition, and high precision, high robustness and engineering implementability are achieved.

The invention discloses a new energy commercial vehicle fast charging working condition thermal management control method and system, and relates to the technical field of new energy vehicle thermal management and fast charging control, and the method comprises the following steps: collecting the temperature, voltage and current data of each subarea of a battery in a fast charging process, forming a time synchronization sequence, and based on the sequence, obtaining a new energy commercial vehicle fast charging condition; calculating internal thermal resistance and thermal capacity parameters of the battery in real time by using an online identification algorithm, updating a basic thermal model to obtain a corrected thermal dynamic model reflecting actual thermal characteristics of the current battery, importing the corrected model into a model prediction controller, constructing a multi-objective optimization function, and performing rolling solution in a prediction time domain to obtain a prediction model; and outputting an optimal target charging current instruction and a cooling total demand, adjusting the charging power according to the target current, and combining the temperature difference distribution of each partition. Accurate prediction and partition cooperative control of the thermal state of the battery in the fast charging process are achieved, the temperature rise and the temperature difference are effectively restrained while the charging efficiency is guaranteed, the service life of the battery is prolonged, and the system safety is improved.

The invention provides a high-speed magnetic levitation suspension system control method and system based on edge calculation and Transform prediction, and the method comprises the steps: constructing a Transform prediction model with a space-time attention mechanism and an autoregression mechanism based on obtained local low-delay calculation resources and train real-time sensing data, and deploying the Transform prediction model in a vehicle-mounted edge calculation unit; performing short-term high-precision prediction on the gap, the acceleration and the disturbance trend at a plurality of sampling moments in the future through a Transform prediction model to obtain a prediction result; processing actuator current saturation and gap safety threshold hard constraints in a limited prediction domain by using a model prediction controller, and solving an optimization control sequence in real time in combination with a prediction result; and overlapping a control barrier function as a safety filter of the model prediction controller, correcting the optimized control sequence to obtain an optimal control sequence, and controlling the high-speed magnetic suspension system. According to the method, the cloud communication delay and jitter are reduced, and the robustness and security of the system under uncertain disturbance are improved.

The invention provides an overhead and portal crane lifting appliance control method based on multi-stage model predictive control, and relates to the field of data processing. The method comprises the following steps: firstly, acquiring a control parameter and an initial state variable so as to define an operation boundary and an initial state, and establishing a dynamic model containing a coupling relationship among a trolley acceleration, a sling rope length acceleration and a sling swing angle; acquiring obstacle distribution by using space sensing equipment, and calculating the dynamic safety height of the lifting appliance by combining obstacle parameters and control parameters; judging whether the lifting appliance is in an obstacle avoidance stage or a target tracking stage according to the initial state variable and the dynamic safety height; constructing a model prediction controller and dynamically adjusting a state weight matrix and a control input weight matrix; finally, a control input sequence is generated through an optimization algorithm, and track safety and attitude stability control of the lifting appliance is achieved. According to the technical scheme, the swing angle of the lifting appliance is restrained, meanwhile, the obstacles are effectively avoided, and the safety of the lifting appliance in the whole operation process is guaranteed.

The invention is suitable for the technical field of vehicle intelligent control, and provides a model prediction controller parameter self-calibration method based on deep reinforcement learning. According to the method, modeling expression is carried out on the delay characteristic of a vehicle steering system by utilizing a first-order inertial link, the delay characteristic is used as a state variable to be augmented to an error tracking control architecture, and a model prediction controller considering the steering delay characteristic is designed according to the state variable; the problem that the transverse motion control performance of the automatic driving vehicle is deteriorated due to hysteresis between the target wheel rotation angle and the actual vehicle rotation angle is solved. Meanwhile, a deep Q network (DQN) of a multi-target reward mechanism is designed, and an optimal strategy is trained to quickly calibrate multiple parameters of model predictive control (MPC). The generalization performance of reinforcement learning enables the vehicle to adapt to most complex working conditions after sufficient training, the problems that in a traditional method, the parameter calibration time of a control system is long, and robustness is insufficient are solved, and high-precision and stable transverse control over the full-drive-by-wire four-wheel steering vehicle under the complex working conditions is achieved.

The invention belongs to the technical field of robot control, and particularly relates to a self-adaptive whole-body control method suitable for a quadruped robot in an uncertain dynamic environment, and the method integrates an extended state observer (ESO), a model prediction controller (MPC) and a whole-body controller (WBC) based on hierarchical optimization. And a closed-loop control architecture is constructed to improve the robustness and motion coordination ability of the quadruped robot in a complex environment. Firstly, external disturbance is estimated and compensated in real time through ESO; then, the MPC optimizes future system behaviors in a rolling manner under the condition of considering interference terms, and an expected foot end counter-acting force is output; and finally, the WBC is combined with the priorities of the multiple tasks to generate all joint control instructions in a constraint optimization form, and task collaboration and dynamic adaptation are achieved. The method has the advantages of being high in disturbance self-adaptive capacity, high in control precision, suitable for a multi-task environment and the like, and is particularly suitable for quadruped robot control tasks in complex dynamic scenes such as transportation and search and rescue.

The invention relates to the field of aircraft intelligent control, and discloses a sensor fault resistant low-altitude aircraft multi-mode propulsion control method, which comprises the following steps: constructing a nonlinear model of a short-vertical aircraft hybrid propulsion system, taking deep exploration optimization reinforcement learning as a main control, and combining a fuzzy controller and a model prediction controller to form a strategy fusion group; the method comprises the following steps: introducing a residual perception mechanism and a GIRLS-EKF health estimation module, extracting residual signal features by using CNN and Transform, and carrying out real-time diagnosis and dynamic weight adjustment on an abnormal measurement state of a sensor in combination with generalized residual least square filtering and an extended Kalman structure; the controller fusion module outputs the optimal control quantity in a self-adaptive manner according to the health state and the flight mode, and the fault-tolerant performance and the energy efficiency scheduling capability of the multi-propulsion system under the complex working condition are improved; the method is suitable for low-altitude economy and urban air traffic task scenes, and has good engineering integration and intelligent control application prospects.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses a wind disturbance compensation control method for a quad-rotor unmanned aerial vehicle, which comprises the following steps: tracking flight paths of the quad-rotor unmanned aerial vehicle under different wind conditions through a model prediction controller to obtain flight state data under different wind conditions; according to the flight state data and the kinetic equation under different wind conditions, a kinetic model of the four-rotor unmanned aerial vehicle is constructed, and a flight data set is obtained based on the kinetic model; determining the attitude correction of the unmanned aerial vehicle based on the optimized deep network learning model; according to the attitude correction amount of the unmanned aerial vehicle and the flight state data under different wind conditions, the motor basic thrust of the four-rotor unmanned aerial vehicle is obtained; and according to the basic thrust of the motor and the wind disturbance compensation force output by the optimized deep network learning model, obtaining the target thrust of the motor after wind disturbance compensation. The invention further discloses a wind disturbance compensation control device and system for the quad-rotor unmanned aerial vehicle.

The invention discloses a response control method for a charging station participating in a low-voltage treatment scene, and the method comprises the following steps: S1, collecting the voltage data of a charging pile, calculating a deviation, and judging a low-voltage grade; s2, when the deviation is in a light interval, outputting reactive power support voltage in proportion; s3, when the deviation exceeds a heavy threshold value, reducing active power and starting V2G feedback; s4, constructing a graph structure model, and extracting inter-node power coupling and user behavior factors; s5, inputting the model into an improved prediction controller, predicting a future voltage state, and optimizing and outputting a current period power control value; s6, power adjustment is executed, and feedback power is slowly output in the V2G stage; and S7, comparing the control deviation with the behavior factor change, updating the node control priority, and feeding back for rolling optimization of the next period. According to the invention, while the charging regulation and control precision is improved, the user experience and the voltage stability are effectively considered, and the method is suitable for various low-voltage treatment scenes.

The invention belongs to the technical field of fine chemical reaction safety testing and automatic chemistry, and particularly discloses a nonlinear model predictive temperature control method of a semiconductor temperature control type synthesis reactor. The method comprises the following steps: constructing a dynamic heat transfer model of the system based on the semiconductor temperature control type synthesis reactor; designing a nonlinear model prediction controller according to the heat transfer model of the synthesis reactor; constructing a multi-constraint objective function for nonlinear predictive control according to the characteristics of the controlled object; and solving an optimal control sequence for the nonlinear model prediction controller by adopting a projection gradient method. Compared with a traditional PID temperature control algorithm, the temperature of the reactor can be accurately predicted and controlled through the designed nonlinear model prediction temperature control algorithm, it is ensured that the chemical reaction is conducted at the optimal temperature, and the robustness and adaptability of a control system are improved.

The invention provides a brushless motor multi-axis cooperative control method and system for an industrial robot, and relates to the technical field of control, and the method comprises the steps: processing the real-time parameters of a brushless motor of each joint axis through constructing an extended dynamic model, and obtaining a temperature drift coefficient and a load inertia parameter; a self-adaptive pre-compensation model, a neural network compensator and a recurrent neural network prediction model are combined, a torque control instruction is output, a three-phase current prediction value is calculated, a distributed model prediction controller is adopted to generate an optimal switching sequence, multi-axis cooperative control is achieved, and the system stability and the response speed can be effectively improved.

The invention provides a multivariable decoupling control method and system for an air inlet system of a high-altitude simulation cabin, and the method comprises the steps: firstly building a precombustion chamber cavity thermodynamic differential equation, and building a state equation in combination with a regulating valve first-order inertia model and an engine model; constructing a decoupling matrix by using a differential flatness theory, and converting a pressure and temperature coupling system into an independent control channel; monitoring a system state in real time by using an extended state observer, uniformly estimating internal and external disturbances as total disturbances, and generating a compensation signal; the model prediction controller performs rolling optimization on future time domain control quantity according to deviation between a set value and an actual value, generates an initial instruction in combination with the compensation signal, and converts the initial instruction into an independent valve control signal through a decoupling matrix; the high / low-temperature airflow mass flow is controlled through the opening degree of the adjusting valve, the engine model outputs actual parameters to form closed-loop feedback, and the controller updates the control quantity in each cycle. The multi-variable strong coupling, the dynamic disturbance and the model uncertainty are effectively overcome, the control precision and the parameter robustness are both achieved, and the complex working condition requirements are met.

The invention relates to an automatic guided vehicle (AGV) adaptive path tracking method based on MPC. The method comprises the following steps: S1, establishing a kinematic discretization error model based on the kinematic characteristics of the two-wheel differential AGV, processing a continuous kinematic equation by adopting an Euler discretization method, expressing a dynamic change relationship between a transverse deviation distance and an angle deviation in a state-space equation form, and generating a kinematic discrete state-space model; the method has the advantages that the continuous equation is processed by establishing the kinematics discretization error model and adopting the Euler discretization method, the deviation dynamic relation is expressed through the state space, the calculation process is simplified, the precision is kept, sensor data are fused, the wheel type odometer, IMU and laser data are integrated through the extended Kalman filtering algorithm, and the precision is improved. The real-time position and angle deviation are calculated, the positioning accuracy is improved, a model prediction controller objective function is designed, a weight matrix and boundary constraint are introduced according to constraint conditions, and the effect of obstacle avoidance constraint is combined.

The invention is suitable for the technical field of permanent magnet synchronous motors, and provides a permanent magnet synchronous motor current prediction control method based on parameter identification, which comprises the following steps: constructing a permanent magnet synchronous motor current prediction control model; performing parameter sensitivity analysis based on the constructed current prediction control model; and according to a parameter sensitivity analysis result of the current prediction control model, carrying out online identification on parameters of the permanent magnet synchronous motor by adopting improved extended Kalman filtering, sending the identified parameters to the current prediction controller in real time, and updating the parameters in the current prediction controller. And the current error of the current prediction control of the permanent magnet synchronous motor during parameter mismatch is reduced. According to the permanent magnet synchronous motor current prediction control method based on improved extended Kalman filtering parameter identification provided by the invention, the identification precision of extended Kalman filtering on motor parameters is improved, so that the current prediction control performance of the permanent magnet synchronous motor is effectively improved under the condition that the motor parameters are mismatched.

The invention discloses a shape adaptive planning and control method for a deformable unmanned aerial vehicle, and the method comprises the steps: carrying out the searching based on an occupied grid map of a scene through employing a variable-size kinematics A * path planning algorithm, and obtaining an initial path; by taking the initial path as an initial condition, shape-adaptive trajectory optimization in a continuous space-time space is carried out to obtain an optimized trajectory, and in the trajectory optimization process, the mass center position and deformation parameters of the unmanned aerial vehicle are optimized at the same time; and based on the optimized trajectory, on the basis of a nonlinear model predictive controller and by combining an incremental nonlinear dynamic inverse algorithm, calculating and compensating external force disturbance and external torque disturbance caused by deformation of the unmanned aerial vehicle and a load, so as to realize shape adaptive control of the deformable unmanned aerial vehicle.

The invention discloses a commercial vehicle team cruise control method and system, computer equipment and a storage medium, and relates to the technical field of automatic driving and vehicle infrastructure cooperative control, and the method comprises the steps: carrying out the vehicle role interaction of a team vehicle according to a cooperative relation and a vehicle role interaction logic; establishing a surrounding environment potential field model, and describing an interaction relationship between vehicles inside and outside the motorcade; calculating the longitudinal and transverse potential fields of the vehicles of the fleet, and controlling the distance between the vehicles and the safe distance; a model prediction controller for team cruise is constructed by considering the target speed and the environmental influence; and the safety, speed and control constraints of vehicle movement are set, and the driving performance is optimized. The stability of formation control is enhanced, the formation requirements of complex scenes are met, the real-time dynamic control capability is improved, and the formation energy efficiency is optimized.

The invention relates to a tractor clutch test bench cooperative control method based on MPC-BP neural network PID, and belongs to the technical field of tractor clutch performance testing. The method specifically comprises the steps of establishing an enhanced mathematical model of a tractor clutch test bed system; an MPC prediction controller is designed, and an optimal control sequence is generated through rolling optimization of an objective function; a BP neural network PID controller is improved; a multi-unit cooperative control strategy is designed, MPC prediction and BP neural network PID control are combined, and cooperative adjustment of the torque of the loading unit, the rotating speed of the driving unit and the displacement of the clutch separation mechanism unit is achieved; a self-adaptive adjustment and load compensation mechanism is introduced, and the adaptability of the system to different working conditions and clutch models is improved through online model correction, heat fading modeling and fault diagnosis. The control precision, the dynamic response speed and the robustness of the test bed are remarkably improved, and the test bed can be widely applied to research, development and performance testing of the tractor clutch.

The invention relates to a coaxial multi-rotor unmanned aerial vehicle control method based on a Koopman operator, and the method comprises the steps: constructing a deep neural network based on the Koopman operator, and mapping a state variable to a high-dimensional observable space through an encoder; in a high-dimensional observable space, state variables are predicted through a linear connection layer based on dimension rising state variables and rotor rotating speeds; the decoder restores the prediction result to an original state space; training a deep neural network by using historical flight data to obtain codec parameters and a weight of a linear connection layer, and constructing a linear dynamic model of the observation function and the unmanned aerial vehicle; and the prediction controller predicts state variables of future multiple steps of the unmanned aerial vehicle by using a linear dynamics model, and enables the unmanned aerial vehicle to track a given trajectory by minimizing a cost function to obtain optimal control input. Compared with the prior art, the method has the advantages that complete data-driven modeling is realized, and the method can adapt to unmanned aerial vehicles with different sizes, loads and flight conditions.

The invention belongs to the technical field of temperature control, and discloses a temperature control method and device of a hot runner system for an inverted mold. The method comprises the following steps: defining a critical melt area in a physical sprue which directly influences the product quality in a sprue of a hot runner system as a virtual sprue; dynamically generating a melt viscosity target track of the virtual gate region according to the type of an injection molding material and an injection molding process stage; based on measurable injection molding process parameters and sprue area temperature, estimating a current melt viscosity value of a virtual sprue area in real time through a state observer; calculating to obtain an optimal power control sequence by adopting a multivariable model prediction controller; and driving a sprue area heater according to the optimal power control sequence, so that the actual change of the melt viscosity of the virtual sprue area tracks a melt viscosity target track. According to the invention, the defects such as salivation and wire drawing can be effectively eliminated, so that the sprue quality stability and the production efficiency are improved.

The invention discloses a ground wire adaptive visual trajectory tracking control method for unmanned aerial vehicle inspection, relates to the technical field of unmanned aerial vehicle inspection, and solves the problem that it is difficult to effectively fuse geometric, texture and multispectral features to construct a ground wire lightweight model. And a three-dimensional coupling constraint system is difficult to be combined to adapt to a dynamic scene of ground wire inspection. Comprising the following steps: collecting an initial visual image and multispectral data of a ground wire, extracting and splicing geometric, texture and state features, and constructing a ground wire lightweight model based on knowledge distillation to output a state evaluation result; the flight controller combines the model and geographic information to construct a three-dimensional coupling constraint system, calls a dynamic programming algorithm to generate an initial trajectory and sets a constraint deviation real-time monitoring mechanism; multi-dimensional deviation is calculated, and comprehensive deviation is obtained through weighting of the incidence matrix; and if the deviation exceeds a threshold value, starting a lightweight prediction controller to solve an optimal control problem and adjust flight parameters to generate a new trajectory, otherwise, flying along an initial trajectory.

The invention relates to the technical field of industrial process control, and discloses a digital twinning-based fermentation process temperature adaptive control system, which comprises a data acquisition and state representation module, a probability generation type digital twinning module, an adaptive risk management module, an opportunity constraint model prediction control module and a model iteration trigger module, according to the method, probability distribution of future states is predicted by constructing probability generation type digital twinning, and the uncertainty of the model is quantified in real time; the system can dynamically adjust the risk tolerance, drives the chance constraint model prediction controller, seeks an optimal control solution on the premise of ensuring the process safety probability, triggers the model adaptive update by means of an online evaluation mechanism, and realizes the optimal control solution on the basis of effectively managing the process uncertainty. The fermentation temperature is subjected to high-performance self-adaptive control considering safety and economy, and the problem that a traditional deterministic model is insufficient in control robustness is solved.

The invention discloses a commercial vehicle driving control method considering a load non-uniform distribution characteristic. The method comprises the following steps: acquiring and preprocessing sensor data of a commercial vehicle; estimating yaw disturbance torque caused by uneven load distribution in real time; a robust model prediction controller based on yaw disturbance torque compensation is constructed, and multi-axis coordinated yaw torque meeting the driving stability requirement under the non-uniform load working condition is output; driving force optimization distribution is carried out, and target driving force of each electric wheel is determined; and designing a driving force synchronous tracking controller based on the tire relaxation characteristic model, and outputting the torque of each electric wheel to complete the driving stability control of the commercial vehicle. According to the method, the yaw disturbance torque observer and robust model predictive control are fused, and the disturbance yaw torque is estimated in real time and compensated in a feedforward mode, so that pertinence and fineness are better in the aspect of disturbance suppression; the auxiliary sliding mode control can further improve the robustness of the system in an unknown or rapidly changing external environment.

The invention is suitable for the technical field of automatic driving vehicle control, and provides an automatic driving track rapid tracking control method based on online neural network learning, which comprises the following steps: firstly, designing an adaptive model prediction controller, then making a data set and training a fuzzy radial basis function neural network; then, online learning of the fuzzy radial basis function neural network is carried out; and finally, accelerated optimization of the training process of the fuzzy radial basis function neural network is carried out. The automatic driving track rapid tracking controller based on online neural network learning provided by the invention has the remarkable advantages that the neural network model is trained by using control data of MPC (model predictive control), and a nonlinear relation is fitted to approximate the MPC, so that the calculation efficiency is effectively improved, and the rapidity of the controller is improved; the controller can collect real-time data, update a data set and learn new neural network weight parameters online in the process of tracking a reference trajectory, and shows better adaptability and precision compared with a traditional model prediction controller.

The invention discloses a visual servo method for a transformer substation drainage wire disconnecting and connecting robot, and the method comprises the following steps: building a mapping relation between a mechanical arm tail end camera and a mechanical arm kinematic model, and building an image-based visual servo system IBVS; acquiring a drainage wire clamp image in real time, and extracting image feature points of a drainage wire clamp bolt by using the recognition model; the model prediction controller carries out rolling optimization based on the deviation between the extracted current image features and the expected image features, estimates and compensates external disturbance and unmodeled dynamics borne by the system in real time, and calculates an optimal, smooth and safe mechanical arm motion instruction; and the mechanical arm movement instruction is sent to a mechanical arm movement control unit to drive the mechanical arm to move. According to the method, the pose control of the drainage wire disconnecting robot can be realized, the error convergence in the visual servo process is rapid, the steady-state error is small, the dynamic response is timely, and the requirements of the drainage wire disconnecting operation on the precision, the stability and the real-time performance are fully met.

The invention relates to the field of metal plastic forming control, and discloses a self-adaptive control system and method for forging and pressing aviation precision forgings, and the system comprises an online dynamic model identification module which identifies a process fingerprint representing the characteristics of a current blank at the initial stage of machining; the target curve self-adaptive regulator is used for regulating a reference energy dissipation rate curve according to the fingerprint and generating a personalized reference curve; the model prediction controller performs rolling optimization by using continuously updated model parameters and taking the reference curve as a tracking target, and outputs an optimal speed instruction; and the event triggering and constraint dynamic modulation module is used for actively tightening the operation constraint applied to the controller before a forging pressure sudden increase event is pre-judged through a parallel monitoring model prediction error. According to the method, through combination of target self-adaption and constraint dynamic modulation, control over the forging and pressing process is achieved, forging pressure overshoot can be effectively restrained, and the forming precision and batch-to-batch quality consistency of forge pieces are remarkably improved.

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

The invention discloses an operation control method for a wind power generation system, and relates to the technical field of operation control, and the method comprises the steps: obtaining the historical operation data of a wind power generation system in a target sea area, taking an offshore environment factor as an interference factor, and coupling the time-space parameters of ocean turbulence intensity, wave load and salt spray corrosion rate; establishing a fan-marine environment digital twinborn body; operation data is acquired in real time, synchronous mapping and dynamic updating of the digital twin are realized, and a state prediction model is established in combination with historical operation characteristics; taking the maximum power generation efficiency and the minimum mechanical load as a multi-objective optimization function, and introducing a model prediction controller to obtain an optimal control sequence of the wind power generation system in a future time domain of each control period; and selecting a first control instruction in the optimal control sequence, issuing the first control instruction to a physical fan torque controller and a variable pitch system in real time, realizing rolling optimization of the wind power generation system, and obtaining a self-adaptive operation control scheme of the wind power generation system. The power generation benefit of the whole life cycle is improved.