22 results about "Discrete system" patented technology

The invention relates to a model prediction control method and device based on disturbance prediction, equipment and a medium, and relates to the technical field of system control, and the method comprises the steps: obtaining the disturbance data and actual state quantities (height, speed and attitude angle) of an aircraft at the current moment in real time, determining a disturbance prediction sequence based on the current and historical disturbance data, and obtaining a disturbance prediction result; and constructing a discrete system state prediction model, performing rolling optimization solving by taking minimization of a preset cost function as a target to obtain an optimal control sequence in a future prediction time domain, and outputting a first control quantity in the optimal control sequence as an actual control instruction at the current moment to an execution mechanism. According to the invention, automatic control of a multivariable complex system is realized, high precision and high real-time performance of control are ensured, and the ability of the system to deal with extreme conditions is improved.

The application discloses a short-fiber reinforced ablative material bulk ablation simulation method, and belongs to the technical field of multifunctional composite materials and structure numerical simulation.The application comprises the following steps: constructing a discrete system composed of a series of spherical particle units by using a discrete element method; calculating the contact area, contact force and connecting force of the particle units by using a Hertz-Mindlin contact model and a parallel-bond model at each calculation time step; calculating the temperature of the particle units by using an energy conservation principle and a Fourier heat conduction law; calculating the displacement and angular displacement of the particle units by continuously integrating the momentum conservation equation twice; updating the velocity, displacement and diameter of each particle unit, and updating the porosity and pore pressure of the discrete system; iteratively solving and visualizing until a specific condition is met, and ending the simulation.The application characterizes the internal structure features of the short-fiber reinforced ablative material by using the discrete element method, so that the numerical simulation accuracy of the ablation process is improved, and the possibility of capturing the micro ablation mechanism is provided.

The invention discloses a structural vibration detection method based on an inequality constraint hybrid least square algorithm in the technical field of structural vibration detection. The method comprises the following steps: S1, system model construction: establishing a linear discrete stochastic system state space model; s2, designing an algorithm: proposing a hybrid least square algorithm with input inequality constraints; s3, solving a system model: applying a proposed inequality constraint hybrid least square algorithm to a discrete system to realize simultaneous optimal estimation of a system state and input; and S4, performing comparison simulation: performing simulation by taking a two-layer shear structure as an example, comparing the state and input estimation of the shear structure under the conditions of no constraint and existence of inequality constraint, and verifying the effectiveness of the method. According to the invention, the hybrid least square algorithm of the input inequality constraint is provided, and the filtering effect is optimized by using part of input information, so that the accuracy of structural vibration detection is improved.

This invention provides an online iterative compensation system and method for servo errors, comprising: a disturbance observer module: treating the internal nonlinearity and external disturbances of the system as a lumped disturbance during the compensation of slow-cut servo errors by a fast-cut servo system, and estimating and compensating for them; a dual-loop high-bandwidth control module: designing a positive acceleration-velocity-position feedback damping controller in the inner loop to suppress lightly damped resonant modes; an online iterative compensation module: predicting the tracking error of the robust high-bandwidth controller and iteratively compensating it through online reference trajectory correction; and an overall control scheme implementation module: including a disturbance observer, a high-bandwidth dual-loop controller, and an online iterative compensation module; predicting the system state within the prediction interval based on a linear model of the discrete system difference equations, and modifying the reference trajectory online for error compensation. This invention enables real-time iterative compensation, improving the tracking accuracy and anti-interference capability of the fast-cut servo system during the compensation of slow-cut servo errors.

The invention discloses a piezoelectric ceramic driver control method based on a multi-grid method, and belongs to the technical field of precise driving control. Aiming at the problems of low positioning precision caused by inherent nonlinear characteristics of hysteresis, creep and the like of a piezoelectric ceramic driver, many parameters of a traditional complex model and large calculation amount, the method comprises the following steps: firstly, establishing an electromechanical coupling nonlinear power system model, and accurately converting an original system into a linear system through a feedback linearization technology; then constructing an optimal control problem, deducing a regular equation set by using a Pontryagin minimum principle, and performing time discretization by using an implicit Euler format to ensure numerical stability; and finally, introducing a multi-grid algorithm to efficiently solve the large-scale discrete system. According to the method, the calculation complexity is remarkably reduced while the model precision is reserved, the convergence speed is increased by more than 8 times compared with a traditional iteration method, the robustness to parameter changes is high, a precondition device does not need to be adjusted, the positioning error can be controlled within the range, and the method is suitable for the high-end manufacturing fields such as precise positioning and optical focusing.

This invention discloses a modeling method for grid-type multi-converter based on sub-microsecond simulation, belonging to the field of converter modeling and simulation technology. The method involves discretizing the switching elements to construct a generalized constant admittance switching model based on the LC equivalent circuit. Then, by discretizing the system state matrix, an extended modeling from a single converter to a multi-converter system is achieved. Furthermore, by combining the inertia characteristics of a virtual synchronous generator, a complete grid-type multi-converter model including control elements is established. Finally, a simulation model of the grid-type multi-converter system is built in PSCAD / EMTDC, verifying the correctness and effectiveness of the method. This invention demonstrates good applicability and stability in integrating the generalized constant admittance switching model with the inertia characteristics of a virtual synchronous generator at a sub-microsecond simulation step size. It significantly improves the simulation accuracy and efficiency while achieving rapid convergence of transient processes in the grid-type multi-converter and active support for grid voltage and frequency.

The application relates to a multi-platform mixed power model simulation method, system and computer equipment. The method comprises the following steps: determining a next time synchronization point of a power model and writing a state signal and the time synchronization point into a first shared memory, providing an accurate benchmark for discrete system simulation; triggering the discrete system to advance according to the time synchronization point and updating a second shared memory, guaranteeing simulation timing and data synchronization; reading the updated target simulation signal in the second shared memory to continue power model simulation, ensuring a circulating dynamic updating process of "benchmark setting-simulation advancing-data feedback", realizing collaborative simulation under non-real-time multi-platform mixing, and improving overall simulation synchronization and accuracy.

The application belongs to the field of multi-agent cooperative control, and particularly relates to a multi-agent consensus cooperative control method based on event-triggered impulse; the method comprises the following steps: constructing a communication topology of a nonlinear multi-agent system; constructing a dynamic model of an agent in a second-order discrete system and initializing the system; setting a non-continuous saturation constraint impulse control protocol; each agent communicates with each other according to the communication topology, continuously acquires state information of neighbor agents and saves its own state information in the communication process; all agents are updated by using the non-continuous saturation constraint impulse control protocol at the impulse moment; all agents are updated according to the dynamic model at the non-impulse moment; an error system is constructed according to the state information of the agents, and when the error system is stable, the agents reach the consensus; the application can realize the state consensus at a faster speed and with fewer event triggers.

The invention belongs to the field of solid propellant performance prediction, and discloses a high-precision modeling method and system for a solid propellant, and the method comprises the steps: obtaining parameters of propellant components; the method comprises the following steps: constructing a solid propellant into a discrete system consisting of spherical particle units, and endowing the particle units with real physical parameters according to component thermodynamic parameters, reaction kinetic parameters and mechanical parameters of the propellant; establishing parallel bond bonds among particle units, and endowing the parallel bond bonds with real physical parameters according to the thermodynamic parameters, the reaction kinetic parameters and the mechanical parameters of propellant components; based on the real physical property parameters of the particle units and the parallel keys, a solid propellant high-precision model is established and used for providing a high-precision initial structure model for propellant ignition combustion process simulation and combustion performance prediction. According to the method, the problems that actual formula parameters such as particle size distribution and gradation are insufficiently considered and a microscopic combustion mechanism is difficult to capture in an existing method are solved, and the accuracy of a propellant numerical model is improved.

The invention relates to a zero dynamic attack detection method for a wind power system based on off-phase sampling, and the method comprises the steps: obtaining the dynamic information between main sampling points of a discrete system through introducing an offset phase sampling mechanism, and achieving the detection of zero dynamic attacks. The offset phase sampler is placed at an offset phase compared with the main sampler, and obtains internal system dynamics in a sampling interval; and the data abnormity is judged through residual error accumulation detection. The method is high in engineering feasibility, can effectively detect extremely hidden zero dynamic attacks on the premise of not changing a system structure and not introducing redundant hardware or a high-safety communication link, and breaks through a blind area of a traditional detection method.

This invention relates to a system call analysis method based on multi-dimensional correlation, belonging to the field of data processing technology. It solves the problems of high performance overhead and difficulty in balancing accuracy and real-time performance in existing call analysis methods. The method includes: filtering and capturing system call events generated by a target application according to filtering rules, and dynamically adjusting the filtering rules by real-time monitoring of system load; correlating the captured system call events along process, time, and resource dimensions to obtain multiple system call sequences; precisely matching each system call sequence with atomic behavior rules to obtain the behavior type of successfully matched system call sequences; performing feature matching of unmatched system call sequences with historical behavior data to obtain the corresponding behavior type; and outputting structured behavior data based on each system call sequence and its behavior type. This achieves accurate and real-time analysis of discrete system call events.

The invention discloses an enhanced moving least square meshless method for calculating a steady-state sound propagation problem, and relates to the technical field of numerical simulation calculation and numerical forecasting of the steady-state sound propagation problem, and the method comprises the steps: S1, constructing an interpolation function of a steady-state sound field; s2, constructing a local enhanced interpolation primary function; s3, constructing a global interpolation format; s4, constructing a numerical calculation model for discretization of the steady-state sound propagation problem; s5, constructing a discrete system matrix equation; s6, sound pressure solving is carried out; s7, result verification and analysis; according to the enhanced moving least square meshless method for calculating the steady-state sound propagation problem, the problem that high-frequency errors are large in existing steady-state sound propagation calculation is solved.

The application discloses an anaerobic ammonia oxidation activity prediction method and system based on a TabPFN model, and comprises the following working steps: step S1, collecting anaerobic ammonia oxidation system operation data, and constructing an original data set with anaerobic ammonia oxidation activity SAA as a prediction target, wherein the operation data comprises heterogeneous table data composed of numerical continuous operation parameters and discrete system information of a category; step S2, preprocessing the original data set, and sequentially completing missing value completion, abnormal value identification and cleaning, and category feature encoding conversion to obtain a standardized heterogeneous table data set; and step S3, dividing the standardized heterogeneous table data set into a training set and a test set. According to the heterogeneous table data characteristics of SAA, the application can simultaneously process continuous variables and category variables, and improve the adaptation capability to complex operation data; and the application can learn the nonlinear mapping relationship and interaction between SAA and multiple factors, so that the prediction precision and generalization capability are improved.

This invention proposes a multi-model non-disruptive switching control method and system for steam temperature under all operating conditions in thermal power units, relating to the field of thermal power generation. The method includes: acquiring steam temperature data and load conditions in the steam temperature loop of the thermal power unit; constructing steam temperature models under different load conditions across all operating conditions based on adaptive weight positive and negative feedback using a slime mold algorithm, using the steam temperature data and load conditions as input; constructing a continuous system model set from the steam temperature models under different load conditions across all operating conditions, and then discretizing it into a discrete system model set; using the discrete system model set as input, controlling the steam temperature data under a single operating condition using a step-wise generalized prediction method; non-linearly fitting weight allocation parameters to multiple models across all operating conditions, and obtaining a flexible switching curve through smoothing; and controlling the steam temperature across all operating conditions using the flexible switching curve. This invention achieves non-disruptive switching between different operating conditions, realizing precise steam temperature control across the entire operating range of thermal power units with limited computing resources.

The invention relates to the field of deep neural network control, in particular to an aperiodic sampling deep neural network controller based on closed-loop stability guarantee. The controller design comprises the following steps: step 1, firstly, dividing a discrete system into a known nominal part and an unknown uncertainty part, and constructing a virtual filter based on integral quadratic constraint to obtain an augmented system model; 2, deploying a dynamic event triggering strategy between the sampler and the DNN controller; 3, on the basis of the step 2, performing convex relaxation on the deep neural network controller, converting nonlinear mapping into linear matrix inequality constraint, and establishing a convex optimization model through a slack variable; and 4, carrying out stability analysis on the closed-loop system. Constructing a linear matrix inequality criterion and solving a corresponding convex optimization problem to obtain a closed-loop stability guarantee, and taking ellipsoid approximation of an attraction domain as stability measurement; and step 5, non-periodic sampling deep neural network control applied to the inverted pendulum.

The present invention discloses a neural network-based NARMA-L2 multivariable control method, which includes: deriving a NARMA-L2 multivariable control law of a nonlinear discrete system; using neural networks to offline identify nonlinear functions in the control law; designing the controller with the control law, designing a closed-loop control system for a certain type of turbofan engine, and adding the function of making the online update of neural network errors on the basis of this to correct the controller parameters, and studying the tracking performance of the control system. Aimed to minimize the quadratic performance index for the errors of the engine's high-pressure rotating speed and pressure ratio, by using neural networks in combination with the deduced NARMA-L2 multivariable control law, the present invention has designed a dual-variable controller, calculated the engine's control variables and controlled the fuel flow of the engine and the critical interface area of the exhaust nozzle.

The present application relates to a robust fault-tolerant tracking and anti-interference dynamic output feedback control method for the pressure maintaining system of an injection molding machine, belonging to the field of advanced control of industrial processes, comprising the following steps: Step one: design a dynamic output feedback switching controller with probability; Step two: establish a closed-loop discrete system actuator random failure model containing controller state deviation; Step three: sufficient condition for system asymptotic stability when the system is not affected by external disturbance and variable output set value; Step four: sufficient condition for system asymptotic stability when the system is affected by external disturbance and variable output set value; This method proposes a new scheme for the control of the pressure maintaining stage of injection molding with parameter uncertainty, unknown external disturbance and time-varying set value under the condition that the state variables are not completely measurable; This scheme combines the theories of stochastic control, robust predictive control and dynamic output feedback, which not only guarantees the tracking performance of the system, but also reduces the energy consumption of the equipment.

The application provides a kind of equipment system analysis method and system based on linear constant discrete system, comprising: step 1, the equipment composition / communication relationship / operation flow / equipment performance integrated model of equipment system represented by linear constant discrete system state space equation G is established;Step 2, the operation capability comprehensive analysis of equipment system is carried out: including the multi-variable capability, reaction capability, task completion capability, anti-destroying capability of equipment system, the evaluation index model of calculating the above capabilities is established, and the evaluation index value of each capability is calculated based on the state motion expression of state space equation G.The problems existing in the current system can be found by analyzing the operation capability of the equipment system, which points out the direction for subsequent equipment system optimization, and can support the test and evaluation of equipment system optimization result.

The invention provides a positioning and tracking method based on continuous discrete filtering of a physical information neural network. The method comprises the following steps: establishing a continuous-discrete time system model; a PINN-based time updating method is adopted; the invention discloses continuous-discrete filtering based on physical information neural network time updating. According to the method, the neural network thought is introduced into continuous-discrete system filtering, a continuous-discrete filtering framework is adopted, the error size of target state prediction is effectively controlled, and the tracking precision can be remarkably improved through the strong nonlinear fitting capability of the PINN; a state prediction method independent of step length is realized for a global kinematic model before filtering, so that a continuous-discrete filtering method with large step length and high precision can be realized, and the continuous-discrete filtering speed is further improved.

The invention provides a distributed consistency security state estimation method and system, and the method comprises the steps: setting all initial parameters and boundary conditions needed by a linear time-invariant discrete system, and setting the operation time step of the system as an initial value; designing the gain of the false data injection attack observer, and updating the covariance upper bound of the attack signal observation error by means of the gain, the measurement output of the sensor and the deviation of the system state predicted value and the observation estimation of the physical layer hostile attack signal; state prediction is carried out based on the obtained state estimation result, and the covariance upper bound of a state prediction error is updated; in combination with the number of node neighbors, preset parameters and the upper bound of the updated state prediction error covariance, obtaining Kalman filtering gain used for adjusting measurement residual influence; and combining the obtained attack signal observation value, the Kalman filtering gain and the consistency gain to complete the state estimation updating of the current time step, and then updating the covariance upper bound of the state estimation error.

The application provides a vibration noise suppression method for a permanent magnet synchronous motor of an electric vehicle, comprising the following steps: establishing a mathematical model of the permanent magnet synchronous motor in different coordinate systems; constructing a torque and flux observer to obtain an electromagnetic torque estimation value and a stator flux estimation value; obtaining an electromagnetic torque reference value through speed loop control, and obtaining a stator flux reference value based on the electromagnetic torque reference value and by introducing a cloud drift optimization algorithm; obtaining a discrete system, constructing a cost function, and obtaining a reference voltage vector by solving; performing inverse coordinate conversion on the reference voltage vector, adopting hybrid random space vector pulse width modulation, generating a three-phase pulse width modulation signal, and sending the three-phase pulse width modulation signal into an inverter to drive the permanent magnet synchronous motor, so as to realize vibration noise suppression of the permanent magnet synchronous motor. The application can effectively suppress vibration noise and improve the control performance and operation stability of the permanent magnet synchronous motor.

This invention proposes a Bayesian network-based method for predicting and diagnosing delamination damage in composite materials, belonging to the fields of composite material delamination damage prediction and aircraft structural safety. The method includes: calculating the energy release rate under different delamination damage parameters using a damage parameterized modeling method and an energy release rate reduction model based on energy differential; forming a damage parameter and energy release rate reduction model; defining the delamination damage propagation process as a nonlinear discrete system; constructing a dynamic Bayesian network under composite material delamination damage propagation based on the energy release rate reduction model and the delamination damage propagation model; and defining the nodes and connections within the network; using a particle filtering method based on sequential importance sampling as the inference algorithm for the dynamic Bayesian network to predict, diagnose, and update composite material delamination damage. This invention can ensure the safety of composite structures in aircraft.