3672 results about "Kinetic model" patented technology

A human-guided vision-force fused impedance iterative learning control method for a robotic arm, comprising: analyzing a robot-environment interaction dynamics equation, solving a visual servo acceleration model, and making use of the equation to establish a human-robotic arm-environment interaction dynamics model in an image feature space; acquiring an image feature position and speed curve of a human-guided robot completing an assembly task, and using dynamic movement primitives for coding and generalization; and designing an impedance iterative learning controller which uses image feature tracking errors as control input, learning impedance characteristics when the human-guided robot performs a contact operation, identifying unknown contact dynamics under the interaction between the robot and the environment, and counteracting identified contact interference in the feature space, so as to implement a flexible assembly operation. The control method solves the problems in existing assembly operations that human-robotic arm-environment coupling nonlinear dynamics, unknown contact dynamics of intensive contact assembly tasks and poor generalization of assembly scenarios require relearning for different scenarios, etc.

The invention discloses an unmanned aerial vehicle-mechanical arm system cooperative control method for precise spraying. According to the method, through a depth camera and a point cloud reconstruction algorithm, three-dimensional modeling of a target surface and semantic recognition of a spraying area are achieved, and a spraying path and spraying posture data are generated. Based on spraying task requirements, a spraying film thickness experience estimation model is established, and path point-level spraying parameters are obtained. Load mass change, mechanical arm mass center change and spraying reaction force of each path point are calculated, and a coupling dynamic model is constructed in combination with the system state. And dynamic compensation control input is generated by using an adaptive sliding mode control algorithm. After spraying is completed, the actual spraying effect is collected through image and point cloud detection, color, film thickness and texture features are extracted and compared with task requirements, error feedback is generated, and control parameters are adjusted online. According to the method, the problems of dynamic change and error compensation in the spraying process are effectively solved, and the method is suitable for a high-precision spraying task in a complex environment.

The invention relates to the technical field of parameter adjustment, and discloses a low-temperature evaporative crystallization machine parameter adjustment method, device and equipment and a storage medium, and the method comprises the steps: collecting M groups of temperature sensor data and N groups of pressure sensor data of a low-temperature evaporative crystallization machine, calculating a first uncertainty representation of temperature parameter measurement and a second uncertainty representation of pressure parameter measurement; performing data fusion to obtain an optimal estimated value of a temperature state and an optimal estimated value of a pressure state; parallel prediction is carried out through the thermodynamic equilibrium model, the crystallization kinetic model and the system response model, and a crystallization process prediction result is obtained; according to the technical scheme of the invention, the interference of the feeding fluctuation, the environmental change and the like can be effectively handled, and the real-time updating of the control strategy and the accurate adjustment of the temperature and pressure parameters can be realized.

The invention discloses a covariance self-adaptive control method for a quadruped robot for spine joint adjustment. The method comprises the following steps that 1, a spine quadruped robot kinematics model with spine joints, a robot trunk dynamics model with the spine joints and a robot leg multi-rigid-body dynamics model are established; 2, based on the energy stability margin theorem and the spine joint coupling effect, a posture control method of the spine joint quadruped robot is provided by combining a covariance self-adaptive evolutionary strategy, and the angle, rigidity and damping coefficient of spine joints are estimated and adjusted according to the terrain gradient; 3, extracting the spatial relationship between the trunk mass center and the foot ends of the robot as an expected reference, updating the expected mass center position in real time, and constructing a model prediction control method to control the posture of the spinal joint quadruped robot; 4, the position of the foot falling point of the swing leg is designed in advance in combination with the additional freedom degree provided by the spine joint and MPC prediction state information. The motion stability of the robot in the complex terrain is improved.

The invention relates to an autonomous navigation method for lunar satellite formation and a second-order filtering navigation architecture, and the autonomous navigation method comprises the steps: obtaining orbit prediction information based on a kinetic model of the lunar satellite formation, and obtaining collaborative theory observation information based on a collaborative observation model, carrying out fusion processing on orbit prediction information, collaborative theory observation information and multi-source observation data of in-orbit observation by using an extended Kalman filtering algorithm, and estimating an absolute orbit state of the lunar satellite formation; establishing a relative motion model of the slave satellite under the local coordinate system of the master satellite, and establishing a relative measurement model; and taking the absolute orbit state of the master satellite as a reference datum, obtaining a preliminary prediction relative orbit state based on the relative motion model of the slave satellite, and obtaining relative measurement data including distance measurement and angle measurement based on the relative measurement model, and fusing the relative measurement data and the preliminarily predicted relative orbit state through an extended Kalman filtering algorithm to estimate the relative orbit state of the slave satellite. Navigation may be independent of ground support.

The invention discloses a saline-alkali soil dynamic precise irrigation method based on a water-salt-carbon flux coupling model, and relates to the technical field of saline-alkali soil improvement and precise irrigation, and the method comprises the following steps: dividing soil into a surface layer, a middle layer and a deep layer, building the coupling model based on a Darcy law, a convection-dispersion equation and a first-order dynamic model, and simulating a water-salt-carbon flux dynamic state; collecting data such as soil water content, conductivity and organic carbon content through a layered sensor network, and inputting the data into the model after cleaning and fusing; the irrigation time, the irrigation amount and the water quality are optimized through a genetic algorithm in combination with the crop growth period requirements; irrigation is executed through an intelligent valve and a variable frequency water pump, and model parameters are adjusted based on real-time monitoring feedback. According to the method, multi-element cooperative regulation and control of saline-alkali soil irrigation are achieved, the soil ecological process can be accurately simulated, the water resource utilization efficiency is improved, the soil environment is improved, and crop yield increase is promoted.

The invention discloses a forklift operation control optimization method and system based on a disturbance observation technology, and relates to the technical field of electric forklift control, and the method comprises the steps: collecting the operation data of a forklift operation process in real time, sensing and fusing through multi-source sensing information, analyzing the state variable of forklift movement, and building a dynamic parameter mapping relation; taking the measured value and the predicted value of the forklift all-condition dynamic model as input, constructing a high-order sliding-mode observer, evaluating disturbance source interference, and generating a motion compensation correction amount; a feed-forward-feedback fused composite controller is constructed, a yawing moment pre-compensation instruction is generated based on a disturbance source disturbance evaluation result, and a forklift trajectory tracking error is dynamically corrected; and monitoring the distance between the operation point of the forklift and the instability boundary in real time, and triggering a torque redistribution strategy when the distance is close to the instability boundary. Through deep fusion of real-time sensing data, dynamic modeling and an intelligent control algorithm, the working performance of the forklift under complex working conditions is remarkably improved.

The invention discloses a finite time tracking adaptive control method for a mechanical arm based on a neural network, and relates to the technical field of industrial robot control. The method comprises the following steps: constructing a kinetic model of the mechanical arm, obtaining an existence form of an unknown nonlinear term in the model, and defining a joint position tracking error and an error change rate of the mechanical arm; constructing a sliding mode dynamic equation based on the tracking error and the error change rate; a BP neural network is adopted to approach the unknown nonlinear dynamic state of the mechanical arm, and the mapping relation between a network input vector and an output vector is determined; combining a sliding mode dynamic equation with BP neural network output, and designing a finite time control method including adaptive gain; and a self-adaptive updating method of BP network weight and sliding mode gain is deduced, so that the tracking error of the mechanical arm is converged to a zero neighborhood within preset time, and self-adaptive control of the mechanical arm is completed. According to the method, high-precision trajectory tracking within the preset time can be realized, and the anti-interference capability is high.

The invention relates to a bridge crane operation dangerous area personnel intrusion detection method and system, and the method comprises the steps: obtaining the real-time position and speed data of a hoisted object through a sensor, analyzing the motion trail of the hoisted object through a deep learning algorithm, and obtaining the prediction result of the motion trail of the hoisted object; calculating a dangerous area boundary by adopting a dynamic model according to a hoisting object motion trail prediction result in combination with load mass and rope length data, and determining a dynamic dangerous area range; acquiring image data of an operation area through an image acquisition device, and identifying personnel positions in the image to obtain personnel position data; and when it is judged that the personnel enter the dynamic dangerous area range according to the personnel position data, an early warning signal is generated, a three-dimensional visualization technology is adopted to superpose the early warning signal to the field real-time image, visual early warning information is obtained, and the visual early warning information is displayed through a display device. According to the invention, dynamic and accurate prevention and control of the dangerous area are realized through multi-dimensional technology fusion.

According to the new energy automobile brake pressure control method based on dynamic environment perception, a multi-window environment perception dynamic time warping algorithm is constructed, so that the similarity of different driver brake sequences is analyzed more meticulously; according to the method, a pavement adhesion coefficient observer for optimizing variable structure Kalman filtering based on a deep reinforcement learning algorithm is provided to calculate a pavement adhesion coefficient; secondly, a slip rate dynamic adjustment factor is obtained through calculation based on a wheel dynamics model, a vehicle density correction coefficient is obtained through calculation based on vehicle density, a road adhesion correction coefficient is obtained based on a road adhesion correction parameter constraint expression, and influences of different external environments on the road adhesion coefficient can be fully considered; the brake pressure calculation is more reasonable, and the driving safety is improved. According to the method, classification results of different drivers are combined, a comprehensive self-adaptive brake pressure calculation model is constructed to cope with different types of drivers, and corresponding final brake pressure is obtained.

The invention discloses a force / position hybrid control method for a dexterous arm-hand robot, and aims to improve the tracking precision and flexibility of a high-degree-of-freedom robot in a complex interaction task. The method comprises the following steps: constructing a complete kinematics and dynamics model; designing a self-adaptive sliding mode impedance controller, converting an impedance equation into a system taking interaction force as a state, and introducing a sliding mode variable to realize stable tracking of expected force; a preset performance boundary function and a synchronization error are introduced to realize cooperative control and performance constraint; and finally, a force / position hybrid control structure is constructed, the outer ring adjusts the expected trajectory, the inner ring achieves accurate tracking, and therefore smooth interaction between the robot and the complex environment is achieved. The method has good robustness and adaptability, and is suitable for tasks such as grabbing and operation.

The invention provides a quadrotor unmanned aerial vehicle suspension swing suppression flight control method based on a DDPG. The method comprises the steps that a kinetic model of a quadrotor unmanned aerial vehicle suspension system is established; performing input shaping processing on an expected reference track signal by using a ZVD input shaper; the unmanned aerial vehicle suspension system is decoupled into position control of a position loop and attitude control of an attitude loop by adopting a cascade trajectory tracking control strategy, and a fuzzy PID controller of the position loop is designed based on a fuzzy theory; the shaped track signal is input into a fuzzy PID controller, and parameters of the PID controller are adjusted on line; performing attitude settlement on the virtual control quantity output by the fuzzy PID controller, and inputting the virtual control quantity into an active disturbance rejection controller; roll, pitch, and yaw torque control quantities are generated based on the virtual control quantity of the attitude loop and the control quantity generated by the DDPG-based tilt angle controller. Under a complex external interference environment, a preset track is tracked with high precision, load swing is effectively suppressed, reliable execution of a transportation task is successfully guaranteed, and meanwhile, the control precision and the anti-interference capability of the system are remarkably 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 discloses a cloud control self-driving automobile control method considering automobile cloud communication time delay. By constructing a time delay prediction model based on historical communication delay data, the back-and-forth communication time delay at the current moment is accurately predicted, and forward compensation is carried out on the vehicle state by adopting an iterative prediction formula in combination with a vehicle transverse dynamic model. According to the method, a process disturbance correction item is introduced, system modeling errors caused by environment disturbance are effectively corrected, and the state prediction precision is improved. And deducing a future vehicle state through the predicted time delay step number in combination with a historical vehicle state and control input so as to realize dynamic compensation of the time delay influence. The method effectively solves the problems of control lag and state information lag caused by communication delay in vehicle-cloud cooperative control, improves the control real-time performance and precision of a cloud control automatic driving system, enhances the robustness and safety of the system in a complex environment, and is suitable for automatic driving scenes with high dynamic and high safety requirements.

The invention relates to the technical field of trajectory tracking control of piezoelectric motion tables, and discloses a trajectory tracking control method and device for a piezoelectric console, and the method comprises the steps: constructing an initial dynamic model of a piezoelectric motion table based on an asymmetric Bouc-Wen hysteresis model, and obtaining a to-be-identified parameter set; based on the root-mean-square error between the actual output displacement of the piezoelectric motion platform at the plurality of sampling time points and the predicted output displacement of the initial dynamical model, constructing a self-adaptation function of the parameter set to be identified; identifying and acquiring a target model parameter set by using a multi-modal Bayesian gradient optimization algorithm and taking the value convergence of the self-adaption function as an optimization target to obtain a target dynamic model; based on the target dynamic model, constructing a hysteresis state observer, and obtaining feed-forward compensation voltage; constructing a self-adaptive fuzzy PID feedback controller, and obtaining a feedback control voltage; and summing the feedforward compensation voltage and the feedback control voltage to generate a driving voltage so as to drive the piezoelectric motion table to perform trajectory tracking.

The invention relates to the technical field of control or regulation systems, and discloses a cooperative control method for a photovoltaic intelligent manufacturing equipment production line, and the method comprises the steps: a control unit collects the stock and change rate of materials in a physical cache region in real time; establishing and mapping the physical cache region into a virtual viscoelastic dynamic model with non-Newtonian fluid characteristics; calculating a virtual elastic restoring force enabling the stock to return to a balance point and a virtual viscous damping force preventing the stock state from changing based on the model; wherein an asymmetric anisotropic damping generation strategy is executed, and a damping coefficient is dynamically split according to a material flowing trend; and finally, superposing the virtual adjustment correction after vector synthesis to the basic transmission speed to generate a dynamic speed instruction, and by constructing a virtual dynamic field with rheological characteristics and an asymmetric damping mechanism, the problem of nonlinear cascade oscillation in discrete logistics transmission is solved, and differential self-adaptive suppression of accumulation and evacuation risks is realized.

The invention discloses a physical and data hybrid driven mechanical arm dynamic model identification method which is characterized by comprising the following steps: S1, acquiring parameters of each axis of a robot, establishing a dynamic model of a mechanical arm, and linearizing the dynamic model according to a minimum inertia principle to obtain a dynamic minimum parameter set and a corresponding regression matrix; s2, an excitation track of the mechanical arm is designed based on the finite Fourier series; s3, the mechanical arm runs the designed excitation track, angles, angular velocities, angular accelerations and torque data of all joints of the mechanical arm in the movement process are collected and filtered, and an observation torque vector and an observation matrix are calculated; s4, performing preliminary estimation on the minimum parameter set by using an iterative reweighted least square method; and S5, aiming at the dynamic characteristics of LuGre friction force in the movement process of the mechanical arm, establishing a mechanical arm dynamic model under physical and data hybrid driving.

A robot control method, comprising: acquiring the current state parameters of a robot to be controlled at the current moment; calling a state regulator to determine, on the basis of a preset dynamics model and the current state parameters, basic control parameters for each joint of said robot; performing parameter filtering processing on the basic control parameters to obtain safety control parameters for each joint of said robot; on the basis of the safety control parameters for each joint, determining motion control parameters for each joint of said robot; and on the basis of the motion control parameters for each joint, controlling each joint of said robot at the current moment. Further provided is a robot control apparatus, an electronic device, a computer-readable storage medium, and a computer program product.

The invention relates to a distributed trajectory planning method and system for an unmanned aerial vehicle cluster in an obstacle environment. The method comprises the steps of initializing a system, constructing a local Euclidean symbol distance site map, and realizing real-time interaction of state information of the unmanned aerial vehicle. An initial collision-free path is generated using jump point search. And solving an optimal control point through an L-BFGS algorithm through multi-constraint trajectory optimization in combination with a load swing dynamics model, four-rotor dynamics limitation, cluster collision avoidance and environment obstacle avoidance requirements. And a dynamic time redistribution strategy is adopted, and the track time interval is adjusted according to speed and acceleration overrun conditions. The method further comprises an adaptive re-planning mechanism, and local target points are updated in real time and adjacent aircraft collaborative optimization is triggered based on local map boundary detection and quadrotor track safety detection. According to the method, efficient, safe and stable trajectory planning of the hanging load quad-rotor unmanned aerial vehicle cluster in a complex environment is realized, the requirement of autonomously and efficiently completing tasks is met, and the task execution efficiency and safety are improved.

The invention discloses a drinking water disinfection by-product generation prediction method based on a reaction kinetic model, and belongs to the technical field of drinking water treatment and drinking water quality prediction, and the method is realized based on the following steps: 1) constructing a disinfection by-product generation kinetic model of a model compound and determining the reaction rate of each step; 2) constructing and optimizing a disinfection by-product generation kinetic model of the natural organic matter on the basis of the obtained reaction rate to obtain a disinfection by-product generation kinetic model of an actual water body; 3) establishing a prediction method for the concentration of each component in the model; and 4) inputting the measured concentration of each component in the actual water body into the constructed model, and calculating and predicting a disinfection by-product generation kinetic result of the actual water body through the model. The method fully considers the generation kinetics of the disinfection by-products of the actual water body under different space (time) distribution, can quickly predict the generation of various disinfection by-products which are managed and controlled and are not managed and controlled but have high toxicity, provides early warning for the effluent quality of drinking water treatment, and ensures the safety of drinking water.

The invention discloses a method for establishing a high-voltage circuit breaker fault detection model, and the method comprises the following steps: collecting current, voltage, mechanical response, temperature and other multi-dimensional signals of a circuit breaker under different working conditions, and unifying the signals into standardized time sequence data; a nonlinear dynamic sparse identification method is utilized to establish a dynamic model for describing equipment state evolution, and sparse coefficients reflecting physical change rules are extracted from the dynamic model to serve as health features. And the features are fused with current monitoring data to generate a joint feature input vector, and a health prediction model based on a TabPFN architecture is introduced for training and discrimination. And finally, accurate prediction of the current health state or the potential fault of the circuit breaker is realized, and the model self-adaptive updating capability is realized. According to the method, physical modeling and data analysis are combined, so that the accuracy and interpretability of fault prediction are improved.

The invention discloses an intelligent automobile cooperative cruise safety control method under Dos attacks and physical faults, and relates to automobile intelligent safety and automatic driving. A hierarchical control framework is adopted and comprises an observer layer and a tracking layer. The method comprises the following steps: establishing a vehicle longitudinal dynamics model and a DoS attack model for DoS attack in a V2X communication network and the problems of sensor and actuator faults and parameter isomerism of a vehicle; a completely distributed self-adaptive preset time observer based on event triggering is designed for each following vehicle in the observer layer, and rapid and accurate estimation of the state of the pilot vehicle is achieved; the method comprises the following steps: constructing an augmentation system at a tracking layer, designing a distributed intermediate observer, carrying out online estimation and compensation on faults of a sensor and an actuator, designing a distributed active fault-tolerant controller based on a fault estimation value and a pilot vehicle state estimation value, and calculating a wheel driving torque to realize safe cruise control. Multiple threats of coexistence of network attacks and physical faults are effectively handled, and the stability and safety of the cooperative cruise system are ensured.

The invention relates to the technical field of temperature control, in particular to an enzyme reaction kettle self-adaptive temperature control system and method, and the system comprises a temperature detection module, a temperature control module, a heat conduction oil flow control module and an automatic regulation and control module; temperature data of a reaction kettle are collected in real time through a temperature detection module, a temperature control module adjusts parameters based on a PID algorithm and fuzzy logic to generate a control instruction, and a heat conduction oil flow control module adjusts flow by combining double-closed-loop cascade control with a feedforward compensation algorithm, so that thermal inertia lag is shortened. And the automatic regulation and control module generates optimized process parameters based on a reaction kinetic model and a genetic algorithm, and dynamically adjusts a temperature set value. The control method comprises the steps of temperature data acquisition, PID control instruction generation, conduction oil flow coordinated regulation, process parameter optimization and the like. The problems that in the prior art, temperature control precision is insufficient, conduction oil flow control is not accurate enough, and technological parameters cannot be automatically regulated and controlled are solved.

The invention relates to the technical field of mechanical arm control, in particular to a self-adaptive fuzzy fixed time control method based on an output-limited mechanical arm, and the method comprises the following steps: S1, constructing a mechanical arm dynamic model; s2, setting constraint conditions of an output state of the mechanical arm dynamic model; s3, defining a tracking error and a virtual control law of the mechanical arm dynamic model; s4, on the basis of output state limitation and fixed time control, an actual control law is designed to serve as input of the mechanical arm dynamic model, and an adaptive law is constructed; and S5, a three-level logarithmic obstacle Lyapunov function is constructed, the three-level logarithmic obstacle Lyapunov function is combined with the virtual control law, the actual control law and the self-adaptive law step by step and substituted into the virtual control law, the actual control law and the self-adaptive law, and then it is guaranteed that the output state of the mechanical arm dynamic model does not exceed the preset safety boundary according to the actual fixed time control lemma. According to the invention, high-precision trajectory tracking can be realized on the premise of ensuring safe operation of the mechanical arm.

A balance control method for a wheel-legged robot is provided. The robot includes a moving wheel, n links, and n rotating joints, the moving wheel being connected to a first link through a first rotating joint of the n rotating joints, and the n links being connected in series through n−1 rotating joints other than the first rotating joint, n being a positive integer greater than 1. The method includes: acquiring a state quantity of the wheel-legged robot at a first moment; determining dynamics model parameters according to a dynamics equation of the wheel-legged robot and the state quantity at the first moment; establishing a sliding surface according to the state quantity at the first moment; calculating rotation torques of the n rotating joints according to the sliding surface and the dynamics model parameters; and controlling the rotating joints according to the rotation torques of the n rotating joints.

The invention discloses a cable underwater robot trajectory tracking control method for coping with ocean current disturbance, and belongs to the technical field of underwater robot trajectory tracking, and the method comprises the following steps: S1, constructing a kinematics and dynamics model of a cable underwater robot, and simplifying the kinematics model of the cable underwater robot; s2, combining speed information of a Doppler velocimeter sensor, designing a super-spiral expansion state observer, and compensating a cable underwater robot system; designing a self-adaptive super-spiral expansion state observer by combining a self-adaptive law; and S3, designing a self-adaptive super-spiral integral terminal sliding mode controller based on the self-adaptive super-spiral expansion state observer. The real-time dynamic performance and stability of the underwater robot in sliding mode control are improved, the buffeting problem existing in sliding mode control is solved, it is ensured that under the condition of ocean current disturbance, the ROV can still achieve accurate trajectory tracking, and the robustness is very high.

The invention discloses a space manipulator impedance control method based on joint hysteresis dynamic position compensation, and belongs to the field of space robot nonlinear dynamics control. The control method comprises the following steps: constructing a flexible joint space manipulator model based on a standard dynamic model of the space manipulator; according to the flexible joint space manipulator model, a virtual torque sensing strategy is designed based on motor side sensor information, and a joint torsion torque estimated value is obtained; building a joint hysteresis model based on a Bi-LSTM mixed hysteresis modeling method fusing multi-scale memory and an attention mechanism, and training the joint hysteresis model; inputting a joint torsion moment estimation value into the trained joint hysteresis model, and estimating a joint torsion angle; based on the estimated joint torsion angle, the joint angle measured by the motor side encoder is subjected to superposition correction, and track error compensation caused by joint hysteresis is achieved.

The invention relates to a speed planning optimization method and system for industrial robot repeated path operation, and the method comprises the steps: carrying out the constraint iterative learning speed planning based on path arc length coordinates, building a precise rigid-flexible coupling dynamic model of a robot, and obtaining the motion constraint condition of the robot; calculating the speed planning motion performance of the robot through the motion performance evaluation function, and updating the parameters of the motion performance evaluation function based on a dynamic linear scaling mechanism; an intelligent iterative learning mechanism based on arc length coordinate parameterization, dynamic constraint on-line satisfaction and provable monotonic convergence performance is realized through an updating law of an iterative learning motion performance evaluation function; and outputting the optimal speed curve of the robot. Track deviation, vibration data and energy consumption characteristics are automatically recorded when a task is executed each time, the root of a problem is autonomously analyzed, key parameters such as an acceleration curve and joint torque distribution are dynamically adjusted, and dependence on manual parameter adjustment in repeated operation is eliminated.

The invention discloses a bridge crane anti-swing control method based on a neural network algorithm, and relates to the technical field of crane control, and the method comprises the following steps: collecting suspended load swing state variables of a bridge crane and establishing a dynamic model; the dynamic model is used for predicting the suspended load swing trend of the bridge crane, and a swing trend prediction result is obtained; feed-forward compensation control is carried out according to the swing trend prediction result, and a feed-forward control signal is obtained; performing real-time feedback correction on the movement of the bridge crane trolley according to the feedforward control signal to obtain a real-time correction control signal; and performing nonlinear compensation calculation according to the real-time correction control signal to obtain a final anti-swing control result. According to the invention, the method achieves the precise estimation of the future swing state, improves the prediction accuracy of the future swing state, achieves the advanced compensation, improves the response speed of a control system, enables a compensation strategy to adapt to different operation environments, and improves the control precision and robustness.

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.