23 results about "Dynamic system model" patented technology

The application discloses a method for adaptive secure consensus control of a multi-agent spatio-temporal dynamic system with unknown boundary nonlinearity under hybrid attacks, comprising the following steps: constructing a multi-agent spatio-temporal dynamic system model with unknown boundary nonlinearity and a virtual leader model; establishing a hybrid network attack model containing deception attacks and denial of service (Dos) attacks, and using an adaptive radial basis neural network to approximate the unknown boundary nonlinearity function; defining a consensus error signal to obtain a consensus state error system; designing a composite adaptive neural network secure boundary consensus control scheme, constructing a Lyapunov function for the error system, and obtaining a sufficient condition for the error system to realize mean square secure consensus control. The application effectively solves the problem that the traditional method is difficult to simultaneously cope with complex network attacks and unknown boundary nonlinear disturbances, and significantly improves the security and robustness of the system.

The application provides a response lag compensation method of a urea liquid injection system, which solves the problem of response lag between the urea liquid rate supplied by a metering pump and the urea liquid rate sprayed by a gas-assisted nozzle in the gas-assisted urea injection system, and comprises the following steps: a first-order dynamic system model about the residual urea liquid amount in a urea pipe is constructed to associate the urea liquid rate sprayed by the gas-assisted atomizing nozzle with the urea liquid rate pumped by the metering pump; an imbalance difference between the urea liquid rate pumped by the metering pump and the urea liquid rate sprayed by the gas-assisted atomizing nozzle is obtained, the urea liquid rate sprayed by the gas-assisted atomizing nozzle at t2 is calculated as X2, the required urea liquid injection rate at the next moment t2 is given as Xneed according to the actual working condition of the engine, the urea liquid rate pumped by the metering pump at the next moment t2 is calculated as Y1, the urea liquid rate pumped by the metering pump at the next moment t2 is corrected as Y2 which is calculated to compensate, and in addition, the application also provides a gas-assisted urea liquid injection system applying the method.

The present application relates to the technical field of medical image processing, in particular to a real-time monitoring and early warning system for the circulation state image of a cardiovascular surgery patient, comprising a video acquisition module, an image preprocessing module, a microcirculation evaluation module, a multi-site perfusion fusion module based on differential manifold, a trend prediction and early warning module, and a clinical interaction and data integration module; the video of the patient's fingernail bed and lips is simultaneously acquired by a high-frame-rate camera, and after preprocessing, the local perfusion index and pulsatility stability index are extracted; innovatively, the multi-site microcirculation signals are mapped onto the differential manifold for representation; through the time-frequency dual-channel fusion driven by Riemann geometry and the attention weighting mechanism, the whole-body perfusion comprehensive index is integrated; and based on the dynamic system modeling on the manifold, early warning can be provided before the conventional blood pressure significantly decreases, valuable time is won for clinical intervention, the non-invasive monitoring method avoids invasive complications, and whole-process continuous monitoring can be realized.

This invention belongs to the field of measurement and control, and relates to a fixed-time preset performance control method, device, computer equipment, and storage medium for a macro-micro composite motion platform. The method includes: constructing a dynamic system model of the macro-micro composite motion platform; transforming the dynamic system model into a state-space equation suitable for a backstepping recursive control framework; designing a fixed-time preset performance function to constrain the tracking error signal within a preset range within a fixed time; constructing a nonlinear filter to simplify the control design process and establishing a switching function to resolve coupling terms in the system; designing an adaptive fixed-time preset performance tracking control algorithm and proving the stability of the closed-loop system based on Lyapunov stability theory. By designing the controller using an adaptive backstepping recursive control framework, the control design process is simplified, and the computational load is reduced; the tracking error signal of the closed-loop system converges to the preset range within a fixed time, improving the transient performance and control accuracy of the system.

This invention belongs to the technical field of fuel cell gas supply systems and provides a control method and system for PEMFC injection gas supply systems based on deep reinforcement learning. Addressing the difficulty in establishing accurate control models for PEMFC injection gas supply systems, this invention proposes a control method for PEMFC injection gas supply systems based on deep reinforcement learning. First, a dynamic system model of the PEMFC injection gas supply system is established using a deep neural network. Second, an actor-critic framework is used to interact with the learned dynamic system model of the PEMFC injection gas supply system and maximize the cumulative reward within the prediction interval, thus learning a neural network strategy based on model predictive control. Finally, by fixing the parameters of the actor network model and deploying the actor network in the controller of the PEMFC injection gas supply system, real-time optimal control of the PEMFC injection gas supply system can be achieved.

The application discloses a random complex network state estimation method and system, and belongs to the technical field of estimators. When the measurement signal transmission requirement of a target node obtained in real time meets a preset dynamic event triggering condition, an augmented error system model is constructed based on a constructed dynamic system model, a missing measurement model and the dynamic event triggering condition, and a gain matrix of a constructed state estimator is obtained by solving a linear matrix inequality. The state estimator is updated according to the gain matrix, and the measurement signal of the target node at a triggering moment is input into the updated state estimator to obtain an updated state estimation signal at a current moment. The triggering condition includes a preset waiting time interval and a forced triggering time constraint. The internal dynamic variable of the dynamic event triggering condition is updated according to the updated state estimation signal at the current moment. The method balances the state estimation precision and the communication frequency by introducing a dynamic event triggering mechanism.

Methods, systems, and apparatuses for combining pressure measurement with a dynamic system model to estimate the air mass within an actuator, and then estimate the applied contact force which may be based on a quasi-static deformation model. An example method includes, in response to a request to apply a contact force by an actuator at a first actuator pressure, determining a second actuator pressure for the actuator to apply the contact force. The contact force is generated using one or more components of a haptic assembly, and the second actuator pressure is determined based on one or more of properties of the components of the haptic assembly and pressure data obtained via one or more sensors. The example method further includes causing the haptic assembly to adjust the first actuator pressure to the second actuator pressure such that the actuator applies the contact force.

This invention discloses a robot navigation method suitable for large sparse environments, comprising the following steps: Step 1, establishing a system evolution equation based on the robot's state vector and kinematic equations, incorporating an environmental sparsity factor into the system evolution equations, and then introducing trajectory tracking weights and obstacle avoidance weights to construct a dynamic system model; Step 2, determining whether the dynamic system model is in a critical state between stability and chaos by solving the Jacobian matrix and Lyapunov exponent of the dynamic system model. When the maximum Lyapunov exponent is approximately equal to 0, the critical stability condition is reached; Step 3, based on the critical state, designing a path evaluation function to screen stable path points, and dynamically adjusting the obstacle avoidance weights through bifurcation parameter adjustment control equations to achieve adaptive switching between ordered tracking and chaotic obstacle avoidance, thus completing robot navigation in large sparse environments. This invention can ensure that robots can efficiently and stably complete navigation in large sparse environments.

The invention provides a multi-unmanned aerial vehicle wireless energy transmission and wireless data collection collaborative optimization method based on adaptive hierarchical deep reinforcement learning, comprising: constructing a dynamic system model composed of a plurality of unmanned aerial vehicles, a plurality of information devices and a plurality of energy devices, the information devices uploading data to the unmanned aerial vehicles, and the energy devices uploading data to the unmanned aerial vehicles; the energy equipment receives wireless energy from the unmanned aerial vehicle; minimizing the total information age of all the information devices and minimizing the total energy hunger degree of all the energy devices are set as common optimization objectives, and the double-objective optimization problems are fused into a single-objective optimization function according to weights; and constructing and operating a three-layer multi-agent deep reinforcement learning framework, solving the single-target optimization function, and outputting an optimal cooperative control strategy. According to the method, real-time balance, autonomous decision making and efficient cooperation of the multiple unmanned aerial vehicles for heterogeneous Internet of Things equipment requirements are realized.

The invention belongs to the technical field of parameter identification, and provides a parameter identification method and system for a hybrid linear dynamic system, and the method comprises the steps: obtaining a fitting high-dimensional time sequence track of the hybrid linear dynamic system; constructing a hybrid linear dynamic system model based on the obtained fitting high-dimensional time sequence track to obtain a high-dimensional state transition matrix; disassembling a parameter estimation task based on a hierarchical idea and a row decomposition strategy, and disassembling the obtained high-dimensional state transition matrix into a low-dimensional row vector estimation task; updating parameters of the disassembled low-dimensional row vector by adopting a mobile data window increment stochastic gradient algorithm; a self-adaptive step length mechanism is introduced, the step length is dynamically adjusted and updated based on the parameter estimation residual error, and a prediction residual error is obtained; and estimating a transfer matrix and a noise covariance of the hybrid linear dynamic subsystem according to the obtained prediction residual error, and completing parameter identification of the hybrid linear dynamic system.

The invention discloses a road accident prediction method, system and device under an air-ground network based on RBF-DNN approximation. The method comprises the following steps: step 1, obtaining road data through a mobile sensor and a fixed sensor of the network and processing the road data; step 2, according to the air-ground networking system model and a possible unknown fault type model, in combination with a tracking error function and a robust adaptive fault-tolerant sliding mode function, obtaining a dynamic system model with an internal actuator unknown fault; step 3, according to the obtained dynamic system model, designing an adaptive law with projection and sigma-correction and a non-Zeno event triggering mechanism, and constructing a DNN closed-loop control air-ground networking prediction control model based on RBF; 4, after the obtained prediction control model is trained, road accident prediction is carried out on the obtained road real-time data; the problem that communication link defects still exist in an existing prediction technology in a highway sudden dangerous case scene is solved.

The invention discloses a tumor treatment strategy decision-making method based on threshold guidance and a related device, and aims to solve the problems of drug resistance, lack of adaptability, difficulty in clinical implementation and the like of an existing tumor treatment strategy, and the method comprises the following steps: defining a threshold-guided treatment period and a threshold-guided non-treatment period, and constructing a threshold-guided dynamic system model, carrying out theoretical analysis and numerical simulation on dynamic behaviors, carrying out comparative analysis with other treatment strategies, carrying out theoretical parameter sensitivity analysis, and optimizing an adaptive treatment strategy by utilizing reinforcement learning. According to the method, treatment decisions are dynamically adjusted through a single-threshold strategy, high individuation and precise treatment are achieved, heterogeneity of tumor dynamics among patients can be effectively handled, tumor development time is remarkably prolonged, the dosage is reduced, toxic and side effects are reduced, and the living quality and treatment compliance of the patients are improved; and a novel effective method is provided for tumor treatment.

The invention discloses a distributed multi-trolley optimal hunting control method based on a switching observer, and belongs to the technical field of multi-agent cooperative control. The method comprises the following steps: constructing a trolley dynamic system model; according to the denial of service attack state variable, whether the denial of service attack is eliminated is judged; constructing a surrounding object estimator to obtain a surrounding object estimation position of the neighbor trolley; constructing a distributed adaptive optimal estimator to obtain an optimal position local estimation value of the surrounding object; and constructing a trolley controller. According to the invention, through the switching type neighbor information state observer, the advantage of information compensation is provided in a denial of service attack environment; a distributed self-adaptive optimal estimator of a cost function is introduced, so that each trolley can cooperatively track a global optimal solution changing along with time; when the communication fails, each trolley performs open-loop prediction by depending on the historical state and the own model, and continuously estimates the information state of the neighbor trolley, thereby improving the robustness and stability of the multi-trolley hunting system.

The invention provides a cooperative control system based on digital twinning in spandex fiber production, and the system comprises a global optimization layer which comprises a global digital twinning model, and the global digital twinning model comprises a hybrid dynamic system model and a multi-target optimizer; the edge collaboration layer is deployed at an edge computing node, the edge collaboration layer comprises a function element decomposition engine, a space-time planner and a terminal execution layer, the terminal execution layer is provided with an adaptive controller, and the adaptive controller is used for generating an optimal control parameter according to a function element with a space-time voucher; and the feedback self-evolution module comprises a product compliance verification unit and a targeted model correction unit. The method and the device are used for solving the technical problems of low dynamic task response efficiency and insufficient global optimization capability.

This application discloses an arc fault detection method, apparatus, computer-readable storage medium, and system. The method includes constructing a stochastic dynamic system model of the arc fault in the circuit to be detected, the stochastic dynamic system model including the state equation and observation equation of the arc fault; acquiring sampled data of the circuit to be detected in the current observation period, extracting features from the sampled data to obtain a multidimensional observation vector of the current observation period that matches the observation equation; based on the stochastic dynamic system model and the multidimensional observation vector of the current observation period, using a Bayesian filter as a framework, performing state estimation through a continuous-discrete extended Kalman filter to obtain the posterior state vector of the current observation period; and determining whether a fault arc exists in the current observation period through a preset fault decision scheme. This application provides a novel implementation path for high-precision, high-reliability, and low-cost AC series, parallel, and grounding arc fault detection.

The application discloses a kind of identification methods for high orbit space infrared camera control system, comprising: establishing the equivalent direct current motor model under ideal frictionless condition;Establishing speed position double closed loop control system;Determine the system balance working point;Design balance point sweep input signal;Get boundary sweep signal;Obtain angle output signal;According to angle output signal, the control object balance point nominal model g ture And boundary model g 边界 Is obtained by identification fitting;Design PID control rate C s , evaluation C s The reliability of high orbit space infrared camera control system in orbit control under nominal model and the robustness of high orbit space infrared camera control system in orbit control under boundary model.The application will be applied to space infrared camera spin-scan mechanism system model identification based on the coarse and fine combination of camera working balance point dynamic system model identification method, can improve the identification efficiency and accuracy of space spin-scan mechanism model, improve the stability and reliability of infrared camera overall control.

A device may generate, based at least on time series data of a quantity of one or more markers present in a blood sample of a subject, a dynamical system model representative of an immune system of the subject, the dynamical system model including one or more functions corresponding to a derivative of the time series data, and the generating of the dynamical system model includes performing a regression to identify, within a library of candidate functions, one or more functions for inclusion in the dynamical system model. A device may determine a steady state solution for the one or more functions included in the dynamical system model. A device may determine, based at least on the steady state solution for the one or more functions included in the dynamical system model, a stability of the immune system of the subject. Related systems and computer program products are also provided.

The application discloses a kind of multi-unmanned aerial vehicle safety formation control method based on distributed discard protocol, belong to multi-agent cooperative control technical field;Method is: the dynamic system model of unmanned aerial vehicle is constructed;Design multi-channel communication transmission mechanism based on distributed discard protocol once trying;According to the reference trajectory of the leader and neighbor information, the pre-defined time speed control signal of the combination of sliding mode control strategy is constructed;Using the pre-defined time controller of the combination of sliding mode control strategy, realize the cooperative control of multi-unmanned aerial vehicle formation under denial of service attack.The application can maintain reliable information interaction under denial of service attack environment through multi-channel communication transmission mechanism;And in combination with distributed discard protocol once trying and feedback mechanism, make unmanned aerial vehicle can dynamically adjust the priority of communication channel, switch to high-reliability safety channel autonomously, thereby significantly improve the robustness and information transmission reliability of system in complex environment.

The invention relates to the technical field of system modeling, in particular to a training method and device of a hierarchical dynamic system model.The method comprises the steps that original time sequence data are normalized to generate a long time sequence data track; slicing and sampling at different time positions of the trajectory according to different time length probabilities to generate a training data set; defining local loss for evaluating the prediction precision of the element sub-model, and evaluating network loss and global loss of the network sub-model for the network constraint satisfaction degree, so as to construct a multi-target fusion loss function of the layered dynamic system model; and adjusting the weight of the sub-loss in the multi-target fusion loss function in stages, performing model training by taking minimization of the total loss as a target until a model convergence condition is reached, and constructing a layered dynamic system model. Therefore, the problem that the model training precision and efficiency are not high due to the fact that data values cannot be fully mined, the training process is unstable and convergence is difficult in a training mode in the related technology is solved.

The invention provides an unmanned aerial vehicle formation mixed triggering cooperative control method and system, and relates to the technical field of unmanned aerial vehicle cooperative control. Comprising an unmanned aerial vehicle dynamical sub-model, a controlled output sub-model, a DoS attack sub-model, a hybrid trigger mechanism control sub-model, a state detector sub-model and a controller sub-model. Constructing a closed-loop dynamic system model and a control target thereof based on the cooperative control system model; and converting and solving the control target, and performing cooperative control on the unmanned aerial vehicle formation according to a solving result. According to the method, a closed-loop dynamic system model with a hybrid trigger mechanism and a control target are constructed based on DoS attack and incomplete observation signals, the control target is converted and solved to obtain a controller gain and a trigger matrix, and cooperative control is performed on the unmanned aerial vehicle based on the controller gain and the trigger matrix, so that the unmanned aerial vehicle is controlled more accurately. And stable cooperative control of the unmanned aerial vehicle under DoS attacks and incomplete signals is realized, and the robustness is higher.

The invention provides an optical cable motion state detection method and device, equipment, a medium and a product. The method comprises the following steps: acquiring an initial structure parameter and a current structure parameter of an optical cable cylindrical shell; based on the initial structure parameters and the current structure parameters, utilizing an Euler-Lagrange equation to establish a power system model of the optical cable cylindrical shell; performing qualitative analysis on the power system model to obtain a traveling wave type; calculating a threshold condition of an external excitation amplitude required in the process of converting the power system model into the chaotic motion state by using a Melnikov method; and judging whether the phase diagram of the optical cable cylindrical shell in the current state meets the chaos state determined by the threshold condition or not by using a numerical analysis method. According to the method, the problems of detection time limitation, environment limitation, manual limitation and the like in the prior art are solved, the motion state possibly generated by the optical cable is detected in the data transmission process, whether the optical cable is deformed or broken or not is predicted, and the recognition accuracy and stability are improved.

The invention particularly relates to a diagnosis system combining a real-time liquid rocket engine fault simulation model and an artificial intelligence algorithm, and belongs to the field of liquid rocket engine fault diagnosis. According to the system, models of parts such as a pump and a turbine are established according to physical characteristics of all parts of an engine, a dynamic system model of a three-layer architecture is constructed, normal data containing random noise are generated through a Broyden iteration method, and fault data are obtained through a fault factor injection method; and then an LSTM-Lightgbm fault diagnosis algorithm is established, and the data is divided into a training set and a test set to complete model training and testing. According to the method, the problems of limited feature extraction capability, insufficient generalization capability and the like of a traditional diagnosis technology are solved, the method has diagnosis accuracy, real-time performance and comprehensiveness, the accuracy of a training set and the accuracy of a test set reach 99.58% and 99.31% respectively, the functions of fault early warning, type positioning and the like can be achieved, and the engineering practicability is high.

The patent relates to a kind of trajectory tracking control of mechanical arm based on improved super-helix sliding mode control, belongs to the adjacent of mechanical arm trajectory tracking technology.The method comprises the following steps: S1, constructing the dynamics model of mechanical arm according to the related parameters of mechanical arm, DH parameter method and Lagrange method;S2, based on the dynamics system model of mechanical arm, construct super-helix sliding surface;S3, design improved super-helix sliding mode control approach law;S4, use Cartesian interpolation method to carry out trajectory planning of mechanical arm;S5, based on super-helix sliding surface and its approach law, combined with the dynamics model of mechanical arm, determine trajectory tracking control law;S6, build the control model of improved super-helix sliding mode control and mechanical arm combination;S7, select control parameter, carry out trajectory tracking control.The controller proposed in the application can effectively suppress chattering under the premise that the system can reach state stability, has strong robustness to external disturbance, and ensures the trajectory tracking of mechanical arm end effector.