703 results about "Dynamic models" patented technology

The application discloses a self-adaptive flood forecasting method, a terminal and a storage medium. The method comprises the following steps: generating a high signal-to-noise ratio local hydrological real-time data stream through adaptive sampling and a double-channel differential quantization technology; receiving an upstream input data stream, performing flood evolution calculation based on a viscoelastic dynamics model and fusing a dynamically identified input delay to obtain an original prediction value; for model parameters, a confidence ellipse is constructed based on ridge regression, the residual of real-time data and the prediction value is compared through statistical projection, the measurement noise and the river physical drift are intelligently distinguished, and the parameters are updated smoothly; finally, the original prediction value is corrected by solving a convex optimization problem subject to safety constraints, and a physically credible final prediction value is output. The corresponding terminal comprises corresponding modules for realizing the above steps. The application realizes high-precision, self-adaptive and intrinsically safe flood forecasting.

The application discloses a rubber dam dynamic boundary simulation method and system based on a two-dimensional water-sediment dynamics model, relates to the technical field of water conservancy engineering numerical simulation and scheduling, and comprises the following steps: defining the internal interface and the virtual dam top elevation of the rubber dam; deciding the target state based on multi-element water-sediment data; smoothly adjusting the dam top height; in the flux calculation of the finite volume method, first, rotating the state quantity to the local coordinate system, comparing the water level with the dam top elevation and correcting the state variable: when the water level is lower than the dam top, the normal velocity is forced to be zero to simulate water retaining, and when the water level is higher than the dam top, the effective water depth and the sediment discharge are calculated based on the dam top; after solving the one-dimensional Riemann problem, the global flux is obtained by inverse rotation. The application realizes the continuous and automatic simulation of the rubber dam state without changing the terrain, and solves the problems of mass non-conservation and numerical instability of the traditional method.

This invention provides a method for reconstructing actuator faults in a heterogeneous multi-agent integrated energy system, relating to the field of fault diagnosis. The invention establishes a unified dynamic model for the agents; defines intermediate variables and constructs their dynamic equations; builds a global error dynamic system for distributed intermediate observers and agents; derives the stability criterion for the global error system based on Lyapunov stability theory, obtaining the state observer gain matrix for state estimation; loads the state observer gain matrix and the gain matrix for state estimation into each agent; and during the real-time operation of the agents, obtains the current state vector estimate and intermediate variable estimate, and calculates the reconstructed actuator fault value. This invention utilizes an output space coupling mechanism, enabling distributed collaboration between different energy networks without requiring a unified model dimension, thus breaking down the physical barriers to actuator fault reconstruction in integrated energy systems.

The application discloses a kind of offshore wind farm automatic power generation control flow field dynamic modeling methods, comprising the following steps: data sampling is carried out using CFD simulation, obtains data set;Data is collected and arranged;Eigen-orthogonal decomposition is used for order reduction processing;Offshore wind farm automatic power generation control flow field dynamic model based on yaw, variable pitch, speed regulation process and its disturbance process is constructed.The application can effectively overcome the shortcomings that the data set obtained by high-precision wind farm flow field dynamic simulation is large in structure and difficult to apply directly, and the dynamic reduced-order model of the wind farm flow field obtained can greatly compress the data structure, retain the main characteristics of the flow field, and has high wake prediction accuracy.

This application relates to a predetermined-time tracking control method for a free-floating space manipulator system, and pertains to the field of trajectory tracking control technology for manipulator systems. The aim is to address the poor stability and low robustness of free-floating space manipulator systems when facing model uncertainties and external disturbances. This application establishes a dynamic model of the space manipulator in free-floating mode; designs a nonlinear disturbance observer based on predetermined-time convergence; constructs piecewise continuous sliding mode variables based on the dynamic model; constructs a non-singular predetermined-time sliding mode controller based on the dynamic model, the nonlinear disturbance observer, and the piecewise continuous sliding mode variables; and utilizes the non-singular predetermined-time sliding mode controller to perform predetermined-time trajectory tracking control on the free-floating space manipulator system.

This invention addresses the common failure problems encountered by actuators during the flight of heavy-lift launch vehicles in disturbed environments. It studies a method for achieving rocket attitude stabilization and tracking under complex conditions, proposing an adaptive fault-tolerant backstepping attitude control design method. First, to avoid attitude singularities, a quaternion-based kinematic and dynamic model of the launch vehicle oriented towards actuator failures is established. Based on this model, an adaptive fault-tolerant backstepping attitude controller is designed to adaptively estimate and compensate for uncertainties and external disturbances, handling potential failures and ensuring the stability of the control system. The algorithm proposed in this invention is more effective and superior than traditional adaptive backstepping methods.

This invention discloses a ship dynamic positioning anti-interference control method considering vertical dynamic optimization, comprising: establishing a four-degree-of-freedom nonlinear dynamic model of the ship and obtaining the error vector by combining the desired pose; defining a virtual control law by backstepping and processing the velocity error using a first-order filter; approximating the uncertainties in the model based on an improved Actor-Critic reinforcement learning framework and designing an interference observer to estimate external disturbances; calculating the compensated desired acceleration by combining the approximation output of the uncertainties, the estimated value of the external disturbances, and the velocity error; obtaining the thruster pitch ratio based on the desired acceleration and generating the final control command through a thrust distribution model. This invention can effectively handle model uncertainties and external environmental disturbances in ship dynamic positioning, especially optimizing vertical dynamic performance and improving control accuracy, response speed, and system robustness.

This invention discloses a collaborative game control method for swarm spacecraft based on a CTDE architecture, comprising the following steps: Step S1: Constructing a swarm-based relatively nonlinear dynamic model containing multiple tracking spacecraft; Step S2: Obtaining a configuration evaluation index for guiding a collaborative encirclement configuration; Step S3: Obtaining a Markov game environment framework; Step S4: Obtaining a dense reward signal for policy optimization within the Markov game environment framework; Step S5: Obtaining a convergent collaborative game control policy based on the CTDE architecture; Step S6: Training the collaborative game control policy using a phased learning mechanism. This invention achieves stable convergence in a multi-agent environment, provides dense reward signals to alleviate reward sparsity, forms a swarm encirclement configuration, smoothly integrates hard engineering constraints, and improves the robustness of the policy under high-dynamic adversarial conditions through phased learning.

This invention discloses a method for accurate identification of the moment of an accident in a new energy vehicle based on multimodal data fusion. The method acquires vehicle motion sensor data, dynamic parameters, and visual image data, and then performs the following parallel processing: The physical layer processes the data to obtain motion features and compares them with preset trigger conditions; when a suspected collision is detected, it outputs the physical layer trigger time and acceleration amplitude; the dynamic layer processes the data based on the deviation between the vehicle's dynamic model and its actual motion state, outputting a vehicle trajectory status label; the visual layer processes the data to detect targets and analyze their motion state; when visual evidence of a collision is detected, it outputs the visual layer trigger time and the type of collision object; the fusion layer performs spatiotemporal alignment of the three layers' outputs and makes a comprehensive judgment, outputting the fused accurate collision trigger time and accident scene classification information. This invention effectively filters false alarms and improves the detection rate of low-speed accidents through multimodal fusion, achieving accurate identification of the moment of an accident and scene reconstruction.

A recursive RISE control method and system for maglev train levitation systems is disclosed. The control method includes: constructing the dynamic equations and voltage balance equations of the electromagnets based on a single-point levitation system, and then constructing a dynamic model of the single-point levitation system accordingly; designing a virtual control law, and then designing a recursive RISE controller based on the backstepping method by combining the dynamic model of the single-point levitation system and the virtual control law; performing stability analysis on the RISE controller to obtain its stability conditions; and, based on satisfying the stability conditions, using the RISE controller to perform real-time control of the levitation gap of the maglev train levitation system. This invention has excellent anti-interference capabilities, reducing the impact of external interference or model uncertainty on levitation accuracy. Furthermore, by handling the nonlinear relationship between voltage and current through the backstepping method, this invention avoids the model mismatch problem caused by linearization.

This invention discloses a visual enhancement method and system for underground monorail cranes based on dynamic modeling, specifically relating to the field of image processing technology. It involves constructing exposure window time-series data by acquiring video frames of the underground monorail crane's operation, corresponding timestamp data, and exposure time parameters; detecting track joint impact features and identifying transient impact intervals on the exposure window time-series data; establishing a coupled dynamic model of vehicle body vibration and crane sway; calculating the time-varying pose response within the exposure window; and constructing a corresponding composite fuzzy kernel model; using the composite fuzzy kernel model to perform deconvolution constraint solving on the operating video frames to generate structural consistency enhancement images; and finally, outputting the visually enhanced image of the underground monorail crane after cross-frame consistency verification, thereby significantly improving the clarity and stability of the underground monorail crane's operating images.

This invention relates to a deep learning-based autonomous obstacle avoidance and path planning system and method for unmanned aerial vehicles (UAVs). The system includes: a perception simulation module, which uses multi-sensor joint modeling to achieve dynamic environmental reconstruction; a decision control and dynamics modeling module, which generates a series of control commands after acquiring fused perception data; and a scene interaction module, which realizes dynamic obstacle behavior modeling and multi-scene library access. The perception simulation module, decision control and dynamics modeling module, and scene interaction module are integrated with a ROS-MATLAB joint interface platform to achieve real-time synchronization and visualization analysis of multi-source heterogeneous data. This invention proposes a dynamic obstacle prediction model driven by multimodal data, using attention weights to dynamically allocate sensor confidence, reducing obstacle velocity prediction error to 0.12 m / s; and designs a hierarchical reward function and a priority experience replay strategy, improving the DRL training convergence speed to 40% and achieving a Pareto optimal state for path length and energy consumption.

The application belongs to the field of sub-seasonal climate prediction, and provides a sub-seasonal climate prediction method and system based on the combination of dynamic model downscaling and machine learning downscaling, which comprises the following steps: S1, generating initial field and model boundary field information required for dynamic downscaling based on global climate model output data; S2, driving regional climate model to perform dynamic downscaling by using the initial field and model boundary field information; S3, generating input field required for machine learning downscaling based on the output circulation field information of the regional climate model; S4, performing machine learning downscaling correction optimization on the output circulation field of dynamic downscaling based on a convolution model; S5, performing machine learning super-resolution based on the output data of machine learning downscaling correction; and S6, generating sub-seasonal prediction information of double downscaling of dynamic model and machine learning. The application utilizes the complementary advantages of dynamic downscaling and deep learning downscaling, improves the sub-seasonal prediction skill, and copes with new challenges brought by climate change.

This invention discloses a fault-tolerant and vibration suppression control method for rigid-flexible coupled robots based on synchronous disturbance observation and dynamic event triggering. The method includes: establishing a large-deformation dynamic model of the rigid-flexible coupled robot mechanism and constructing rigid coordinates, flexible modal coordinates, and control targets; establishing an actuator effectiveness decay fault model and transforming the original underactuated system into an equivalent fully actuated model oriented towards trajectory tracking; designing a fixed-time synchronous disturbance observer based on error dynamics to estimate external disturbances and composite uncertainties online; constructing a dynamic event triggering mechanism and a time-synchronous fault-tolerant sliding mode control law to achieve synchronous convergence of the system state to the sliding surface within a fixed time; introducing a hybrid reference trajectory and virtual input to establish an augmented state model and designing a robust linear quadratic state feedback controller. This invention achieves coordinated control of trajectory tracking and flexible vibration suppression under conditions of external disturbances, actuator failures, and limited communication resources.

The application discloses a multi-power unit high-speed train control method, and relates to the technical field of train control.The method comprises the following steps: performing stress analysis on a multi-power unit high-speed train to determine a high-speed train dynamics model; determining a discrete-time nonlinear model based on the high-speed train dynamics model; constructing control force and control force change rate constraints; converting the discrete-time nonlinear model to obtain a dynamic linearization data model; designing a parameter estimation algorithm and an adaptive extended state observer; designing a discrete-time terminal sliding mode function and a hyperbolic curve type reaching law; determining a data-driven terminal sliding mode control scheme based on the dynamic linearization data model, the parameter estimation algorithm, the adaptive extended state observer, the discrete-time terminal sliding mode function and the hyperbolic curve type reaching law; and performing tracking control operation on the multi-power unit high-speed train by using the data-driven terminal sliding mode control scheme.The application improves the control precision of high-speed train operation while reducing the sliding mode chattering.

The present application relates to a kind of based on distributed model predictive control's electric precipitator cluster energy-saving optimization operation control method and control system, based on electric precipitator cluster establishment distributed linear dynamic model, describe the dynamic response relationship between the dust concentration in each electric chamber in electric precipitator cluster and high voltage power supply secondary voltage;According to the outlet concentration information of electric precipitator cluster, design state observer, estimate the dust concentration in each electric chamber;Based on the estimated state of distributed linear dynamic model and state observer, design the controller based on distributed model predictive control, obtain optimal control strategy;Based on method implementation control system.The present application is convenient for control implementation, get rid of the dependence on historical data;Real-time estimation of unobservable electric chamber dust concentration can be implemented in electric precipitator cluster engineering application;Method ensures accurate regulation for each electric chamber, realizes multi-field collaborative dust removal, efficiently and reasonably allocates energy, saves electric precipitator cluster operating cost.

The application provides a tracked vehicle state prediction method and system under limit state, and relates to the tracked vehicle dynamics technical field.The method comprises the following steps: constructing a tracked vehicle control model according to a tracked vehicle vertical force model, a tracked vehicle ground contact model and a tracked vehicle horizontal plane dynamics model; acquiring control vectors, corresponding state vectors and target state vectors of continuous time points before a current time point as parameter optimization data sets; optimizing control model parameter sets in the constructed tracked vehicle control model to obtain a tracked vehicle control model after optimization of the control model parameter sets; and after the optimization of the control model parameter sets is fixed, accurately predicting a state vector of a next time point of the tracked vehicle according to the state vector and the control vector of the current time point of the tracked vehicle.The application fully considers the dynamic performance and the ground adhesion characteristics of the tracked vehicle, and can realize high-precision dynamics description of the unmanned tracked vehicle.

In order to overcome the problem that the current technology is difficult to estimate and continuously correct the bridge twin model by obtaining a series of test and test data, the application provides a bridge twin dynamic model Fourier modeling method based on test data, which is only related to the parameters of the bridge according to the system matrix related to all nodes of the bridge and the observation coefficient matrix related to all nodes, and the first continuous test process of the bridge does not change greatly, and the first comprehensive detection of the bridge twin dynamic mathematical model and the subsequent each time of the bridge twin dynamic mathematical model comprehensive correction process is relatively short, according to the characteristics of the fast and slow change matrix, the slow change matrix is set as a constant matrix in the first continuous test, and the system matrix related to all nodes of the bridge and the observation coefficient matrix related to all nodes in the bridge twin model are established through a series of effective tests and optimal indexes, and the bridge twin model is continuously corrected in the whole life cycle of the bridge, so that the technical problem of estimating and improving the bridge vibration model by obtaining a series of test and test data is solved.

This invention provides a vehicle simulation speed correction method and system, relating to the field of vehicle intelligent dynamics modeling technology. The correction method includes the following steps: S1: Input and process simulation data output from the vehicle dynamics simulation model and corresponding real vehicle test data; S2: Construct a dynamic graph structure representing the interaction between state variables based on a preset physical coupling relationship of the vehicle powertrain; S3: Input the simulation data into a GCN and perform graph convolution operations under the constraints of the dynamic graph structure to extract graph embedding feature sequences representing the spatial dependencies between state variables; S4: Input the graph embedding feature sequences into a TCN and perform temporal convolution operations to learn the dynamic evolution law of state variables in the time dimension and output the correction amount of the vehicle simulation speed; S5: Output the result. Based on this, this invention solves the problem that existing correction methods have various limitations in practical applications.

This invention discloses a high-precision periodic orbit maintenance method based on maintenance point rolling optimization, relating to the field of spacecraft orbit control and orbit maintenance technology. Based on a high-precision ephemeris dynamics model, it constructs a maintenance point rolling prediction mechanism, a navigation and execution error model, and a least-squares orbit correction strategy under multi-path point constraints. This application uses the above method to achieve high-precision, low-energy-consumption, and highly robust long-term maintenance of a spacecraft's periodic orbit in a realistic multi-body perturbation environment.

This invention discloses a distributed optimization and safe formation control method for multiple robotic arms with nonlinear uncertainty. By establishing a dynamic model of the multi-robotic arm system with nonlinear uncertainty, a dynamic event triggering method with internal system information is constructed. Each robotic arm broadcasts its position information to its neighboring nodes only when the triggering condition is met. A predetermined reference trajectory is generated for each robotic arm system, converging to the global optimum and satisfying formation constraints. The position tracking error between the actual output of the robotic arm system and the predetermined reference trajectory at each moment is calculated. The virtual control law designed in the first step of the back-reasoning is used as the target to be optimized. A quadratic programming problem is constructed by combining the safety constraint boundary inequality built with the control obstacle function to obtain the safe virtual control law. In the second step of the back-reasoning, the velocity tracking error between the actual state of the robotic arm and the safe virtual control signal is calculated, constructing a back-reasoning controller based on the obstacle Lyapunov function. This invention overcomes the control instability risk caused by strong nonlinearity and achieves high-precision trajectory tracking.

The application provides a kind of foot type robot control method, device, electronic equipment and storage medium, it is related to robot control technical field, the foot type robot control method includes: obtaining the state vector of foot type robot, form parameter coding and control condition, input training good generative motion prior model, generate multiple candidate action sequences;Again candidate action sequence is substituted into the dynamics model that is constructed in advance, corresponding prediction state is obtained;Then risk assessment is carried out in combination with candidate action sequence and prediction state, obtains each sequence evaluation value, selects the minimum as optimal action sequence;Then the action residual correction quantity of trained residual reinforcement learning model is output, and the optimal action sequence is corrected to obtain the modified action sequence;Finally, the robot executable action sequence is obtained by the preset projection constraint module projection processing.The application can effectively improve the efficiency of foot type robot control.

A system and method are provided for artificial intelligence (AI) model selection and loading. The method includes receiving an input data stream for an AI application and analyzing the stream to determine temperature scores based on topic frequency, importance weightage, and access frequency. The method also includes mapping these temperature scores to relevant expert AI models and predicting the temperature and context of the next prompt using historical data patterns through a sliding window that smooths temporary anomalies. The system dynamically selects AI models based on temperature scores and predicted contexts, then pre-loads these models into a memory hierarchy where higher-temperature models are placed in faster memory. The method processes incoming prompts using these pre-loaded models and outputs responses, improving processing efficiency through predictive model management. The system addresses challenges of managing large-scale AI models by implementing intelligent pre-loading mechanisms that anticipate and prepare for upcoming processing needs.

The application discloses a vehicle drift control method, device and equipment and a storage medium, and belongs to the technical field of vehicle control. The application obtains current state parameters of a vehicle, determines parameter differences between the current state parameters and preset state parameters, calculates target control parameters according to the parameter differences and a vehicle state error model, and controls the vehicle to drift according to the target control parameters. The target control parameters are calculated according to the current state parameters of the vehicle and the preset state parameters to be reached, and then the vehicle is directly controlled to drift according to the target control parameters, so that the vehicle physical parameters do not need to be calibrated, the experience dynamic model does not need to be relied on, the control parameters are directly calibrated, and the precision of the vehicle drift control is improved.

This application provides a method and related equipment for calculating the sampling control period of a self-balancing robot. The method includes: acquiring the dynamic model and motor control input information of the self-balancing robot; calculating the sampling control description information of the self-balancing robot for any time interval within a preset sampling interval based on the dynamic model and motor control input information; acquiring the instantaneous energy functional, cyclic correlated energy functional, and interval cumulative energy functional of the self-balancing robot at any time interval, and constructing cyclic energy constraint information based on the instantaneous energy functional, cyclic correlated energy functional, and interval cumulative energy functional; and performing negative qualitative analysis on the cyclic energy constraint information in conjunction with the sampling control description information to calculate the sampling control period corresponding to the self-balancing robot. In this way, by calculating the maximum permissible sampling control period, communication and computing resources can be saved while ensuring stable system operation.

This application provides a preset performance dynamic surface trajectory tracking control method for a robotic arm system. The method includes the following steps: Step 1, analyzing the total kinetic and potential energy of the robotic arm system using the Lagrange formula, and solving for the control torque using a pneumatic artificial muscle mechanics model and rotational laws to establish a dynamic model of the robotic arm system; Step 2, designing a fixed-time disturbance observer to monitor the total disturbance of the system in real time, addressing the strong nonlinearity and joint coupling and frictional disturbance problems of the robotic arm system; Step 3, designing a preset performance fixed-time dynamic surface controller to compensate for the total disturbance of the system in real time, and designing a control law to enable the angular positions of each joint of the robotic arm system to quickly track the desired target trajectory signal within the allowable error range, thereby achieving the preset transient and steady-state performance constraints within a fixed time and ensuring that all signals of the multi-joint pneumatic robotic arm closed-loop system are bounded.

This application relates to the field of road maintenance engineering technology, and in particular to a method and apparatus for repairing road cracks. The method includes: real-time acquisition of raw data on the attitude of the crack filling vehicle and geometric parameters of the crack ahead; filtering and calculating the pitch angle and roll angle, which characterize the road slope; constructing a dynamic model of the filling material flow based on the above parameters; solving for and executing the optimal pumping pressure sequence through model predictive control; and adaptively adjusting the nozzle angle. The apparatus includes a mobile carrier and integrated sensing, control, execution, and power supply units. This invention achieves precise matching between the filling parameters and real-time working conditions, improves filling uniformity, reduces filling waste, and enhances operational efficiency and quality stability. It is suitable for refined road maintenance and has strong practicality.

This invention discloses an AI-enhanced extended Kalman filter method and system, belonging to the field of Kalman filter technology. The method constructs an extended state including velocity and measurement bias, and performs prediction and linearization based on a dynamic model. The forward innovation and its variance are calculated using the minimum process noise of the fixed bias channel, and used for feature normalization. A sliding window feature sequence is constructed using the normalized innovation and velocity increment, and input into a one-dimensional convolutional neural network for multiple random deactivation inferences to obtain anomaly intensity scores and cognitive uncertainty. Based on this result, the process noise of the bias channel is adaptively adjusted to enhance bias tracking capability when anomalies are significant, and maintain estimation smoothness when normal conditions are normal. Finally, the extended Kalman filter update is completed, achieving robust joint estimation of velocity and bias. This invention improves the estimation stability and accuracy under strong noise and transonic conditions, and is applicable to velocity measurement of aircraft and ultra-high-speed motors.

The application discloses a kind of for ship overturning buckle simulation test and underwater deep tank evacuation evaluation system, comprising: physical simulation experiment platform, for carrying out ship overturning buckle simulation test, and collecting real-time observation data in test process;Digital twin system, for receiving and according to real-time observation data in test process, construct and physical scene synchronous evolution three-dimensional dynamic model, realize the dynamic visualization of ship overturning buckle process and escape path deduction;Evacuation path planning algorithm evaluation system is embedded in digital twin system, for relying on three-dimensional dynamic model provided by digital twin system, realize the access of different evacuation path planning algorithm, multi-scene test and multi-criteria evaluation.The application provides quantifiable technical support for ship capsizing accident mechanism research, evacuation path optimization design and emergency rescue scheme formulation.

A method and apparatus for predicting the configuration and attitude of a towed wire array subjected to current-induced vibrations, relating to the field of marine exploration technology, are disclosed. The method includes: employing a quasi-static model to determine the configuration and attitude and tension of the towed wire array at its equilibrium position by solving a set of nonlinear equations considering boundary constraints; wherein the tail boundary conditions of the quasi-static model include tail rope tension; employing a dynamic model, using the configuration and attitude and tension as initial conditions, discretizing and solving the dynamic model to obtain the spatiotemporal distribution characteristics of the displacement of the towed wire array subjected to current-induced vibrations; wherein the dynamic model includes segment tension and gravity. This application combines a quasi-static model and a dynamic model, adding tail rope tension, segment tension, and gravity to the existing parameters, making the model conform to the actual stress conditions of the towed wire array, thereby achieving accurate prediction of the spatiotemporal distribution of the configuration and attitude of the towed wire array subjected to current-induced vibrations.