24 results about "Discrete time system" patented technology

The invention relates to the technical field of multi-axis servo systems, and discloses a synchronous control method, device and equipment for a multi-axis servo system, and the method comprises the steps: measuring a mechanical coupling relation parameter, a motor dynamic characteristic parameter and a sampling delay time parameter of the multi-axis servo system, and substituting the parameters into a state equation modeling formula, calculating to obtain a discrete time system state matrix; performing Z transformation processing on the discrete time system state matrix and calculating frequency response characteristics to obtain a transfer function matrix and a target coupling axis pair set; executing hierarchical optimization to obtain a preliminary control instruction; a trajectory tracking error and an inter-axis synchronization error are calculated and substituted into the optimization objective function for sampling delay compensation processing, and a compensation control instruction is obtained; the compensation control instruction is processed in real time, synchronous control signals are output to the servo shafts to achieve multi-shaft coordinated movement, and the synchronous control precision in the multi-shaft servo system is improved by accurately recognizing the key coupling shaft pairs and the sensitive frequency points.

The present application relates to the technical field of power system control, in particular to a distributed discrete dynamic power distribution method, system and device for constant current load and a storage medium; local output power and local power generation capacity of each discrete sampling time of renewable energy source RES are acquired; the local capacity utilization rate is calculated according to the local output power and the local power generation capacity; the neighbor capacity utilization rate of the renewable energy source RES is acquired; the utilization rate deviation is calculated according to the local capacity utilization rate and the neighbor capacity utilization rate; the local voltage reference value of the renewable energy source RES is generated according to the utilization rate deviation, and the power distribution is dynamically adjusted according to the local voltage reference value; the output power and the dynamically changing power generation capacity of the local renewable energy source are collected at each discrete sampling time, so that the inherent stability risk of the continuous time CT control algorithm in the discrete time DT system is fundamentally avoided, and the stable operation of the system under the discrete sampling control is ensured.

The invention discloses a centrosymmetric multi-cell-body-based attack detection and security state estimation method and system, and relates to the technical field of network security, and the method comprises the steps: building a system model and an attack model of a nonlinear system based on a nonlinear discrete time system and a false data injection attack; based on the system model and the attack model of the nonlinear system, obtaining a state estimation set of the nonlinear system according to a set membership estimation method; carrying out attack detection through an attack detection strategy based on the state estimation set of the nonlinear system; when the existence of the attack is detected, estimating the security state through a security state estimation algorithm; the method can be applied to attack detection problems of replay attack, denial of service attack and false data injection attack at the same time, and has high application value; the influence of attacks on the estimation performance can be reduced, so that the control system operates within the reasonable estimation performance, and the operation mode is prevented from deviating from an expected mode.

The embodiment of the present invention provides a method and device for linear resonant motor parameter identification. The LRA parameter identification uses the zero-input response discrete-time system model of the second-order linear resonant system to construct a second-order forward prediction error filter, and uses the augmented Wienerhof equation to solve the coefficients of the discrete-time system model to obtain the minimum mean square error estimate, which has the advantages of strong anti-noise ability and good robustness. Through the estimated discrete-time system model, the impulse response invariant method can directly calculate the poles, free oscillation frequency and damping coefficient of the continuous-time system without iterative calculations.

The invention belongs to the technical field of permanent magnet synchronous motor tracking control, and particularly discloses a permanent magnet synchronous motor discrete finite time self-adaptive backstepping control method and a permanent magnet synchronous motor discrete finite time self-adaptive backstepping control system. The method comprises the following steps: firstly, establishing a discrete time system model of the permanent magnet synchronous motor, and designing a discrete finite time adaptive backstepping controller of the permanent magnet synchronous motor based on a minimum operator according to a backstepping method principle and in combination with an uncertain nonlinear function of an adaptive neural network technology processing system; then proving the finite time stability from the last subsystem to the first subsystem by using a Lyapunov stability theory, and estimating the stability time; and finally, enabling the permanent magnet synchronous motor to quickly track an expected signal by utilizing a discrete finite time self-adaptive backstepping controller of the permanent magnet synchronous motor based on a minimum operator. The feasibility of the method is verified through simulation and experimental results, and the designed controller has good tracking effect and robustness.

The present application relates to a kind of discrete multi-motor servo system fuzzy wavelet neural network control method with event triggering mechanism, belong to multi-motor servo system control field, comprising the following steps: S1: the discrete time system model of multi-motor servo system is established;S2: design two type fuzzy wavelet neural network to estimate unknown nonlinear function caused by external interference and internal parameter perturbation;S3: based on the backstepping control framework, introduce event triggering mechanism with dead zone operator, design discrete time fuzzy wavelet neural network controller;S4: using the discrete time fuzzy wavelet neural network controller, control multi-motor servo system is carried out.

The present application relates to a kind of solving method for the design of nonlinear system multiple small fault estimator, first establish the discrete-time system model containing actuator and sensor multiple fault and nonlinear term, then introduce nonsingular transformation and decompose the original system into two subsystems: subsystem 1 only contains external disturbance, subsystem 2 contains both actuator fault and sensor fault. Based on two subsystems, two iterative learning estimators 1 and 2 are designed, and an integrated solution method for designing two estimator parameters is applied using optimization algorithms. Finally, a multiple small fault estimation strategy for nonlinear systems is presented, and accurate estimation is achieved when multiple faults occur concurrently. The present application can accurately estimate the actuator and sensor faults of nonlinear discrete-time systems, while simultaneously counteracting the influence of external disturbances on fault estimation results, effectively estimating small faults in the system, and improving the system's fault handling capability and fault tolerance performance. The solving method of the present application is simple and easy to implement in practical engineering systems.

This invention discloses a power system PI sampling frequency control method and apparatus based on multimodal sampling and continuous packet loss. The method includes: establishing a power system model; establishing a multimodal switching sampling model and a continuous packet loss model for the power system; the multimodal switching sampling model is used to characterize the aperiodic sampling characteristics of the communication network, and the continuous packet loss model is used to describe the continuous loss of data packets during transmission; based on the multimodal switching sampling model and the continuous packet loss model, a PI sampling control strategy is used to dynamically adjust the frequency deviation of the power system, and a boosting technique is used to transform the continuous-time power system into an equivalent discrete-time system; and sufficient conditions for the stochastic stability of the equivalent discrete-time system are established. This invention can achieve frequency control in non-ideal network environments with multimodal switching sampling and continuous packet loss, ensuring the stable operation of the power system.

The invention provides an ACAS X anti-collision decision-making method, system and equipment based on probability prediction, and relates to the technical field of aviation safety, and the method comprises the steps: when a local machine detects a target machine, setting the relative motion state information of the two machines at an initial moment as an observation value at the initial moment, setting the variance at the initial moment as 0, and setting the variance at the initial moment as 0; constructing a state vector at an initial moment; establishing a discrete time system state equation for describing time evolution of the state vector; for each action in the candidate action set, on the basis of the state vector at the initial moment and a system state equation, online predicting future probability distribution of the state vector after the local machine executes the current action; based on the future probability distribution of the state vector, calculating the conflict probability of the local machine and the target machine in the future after the local machine executes the current action; and the action with the minimum conflict probability is determined as the optimal anti-collision decision instruction from the candidate action set, so that the problems of poor flexibility, difficulty in updating and insufficient real-time performance of an existing anti-collision system are solved.

The invention relates to a privacy state estimation method and device based on homomorphic encryption. The method comprises the following steps: constructing a discrete time system model based on a target communication system; determining an initial state set of the cloud and the sensor according to the system model; the system model at least comprises a sensor, a cloud and a user; based on the initial state set, determining a ciphertext of a homomorphic encryption function of the cloud and the sensor through a constrained polynomial holosymmetric polytope method; determining a homomorphic encryption function according to a preset condition and the ciphertext; encrypting a ciphertext based on the homomorphic encryption function, and updating the state of the cloud or the sensor; and sending the encrypted data with the updated state to the user, and decrypting the encrypted data by the user through a preset private key to obtain a state estimation set. According to the method, the constraint polynomial holosymmetric polytope method is combined with homomorphic encryption, so that the eavesdropping attack of an attacker is effectively prevented, the probability that the attacker initiates a secret attack is reduced, and the method is suitable for wide scenes.

The application discloses a missile guidance control integration method based on a neural network mechanism modeling, which comprises the following steps: in a continuous time system, a vertical plane missile guidance control integration model is established; according to the dynamic equation of the vertical plane missile guidance control integration, a state transition equation in a discrete time system is established; a data set for fitting the dynamic model of a neural network is established, and a neural network parameter model obtained through neural network regression calculation is used as a non-mechanism fitting model of a missile control system; and according to the neural network parameter model, a guidance control integration mechanism model used in a control cycle is obtained. The method uses the input and output data of a control process to perform self-modeling of a time-varying control system, meanwhile, the model is equivalent to a mechanism method, and the modeling result can be used for the control of a discrete time system, and the modeling principle has interpretability and reliability.

The invention discloses a semi-homomorphic encryption credible model prediction control method and device explicitly considering quantization error, and the method comprises the steps: constructing a linear discrete time system model with additive disturbance based on a linear discrete system model and a deviation effect caused by the quantization of a measurement state, the control gain quantization error is explicitly introduced into a control law structure, and a prediction state feedback system model explicitly considering the quantization error is obtained; based on the prediction state feedback system model, a linear matrix inequality optimization problem is constructed, and then an offline model prediction control algorithm is designed; based on a predictive control algorithm, quantization and encryption parameter constraint conditions for ensuring bounded stability of the closed-loop system are obtained; under constraint conditions, semi-homomorphic encryption is adopted to realize privacy protection and control performance guarantee of system state information. According to the method, the control gain and the measurement state quantization error are simultaneously and explicitly incorporated into the modeling and optimization process of model prediction control, so that the control performance and the closed-loop stability are ensured.

The invention provides an iterative learning security control method for a discrete time system under composite attack, and belongs to the field of communication information processing. The method comprises the following steps: S1, constructing a discrete time system subjected to random spoofing attack and DoS attack; the system comprises a discrete time system model and a hybrid network attack model in which random spoofing attack and DoS attack coexist. S2, designing an output estimation compensation mechanism, and constructing estimation output to compensate actual output, which cannot be directly obtained, of the discrete time system, so as to obtain the estimation output; and S3, designing an iterative learning control strategy based on the estimated output, so that the tracking error of the discrete time system after the spoofing attack and the DoS attack is converged within a preset security level. According to the method, the boundaries of system trajectories can be guaranteed, the convergence condition is simpler than that in the prior art, the method is easy to check in practical application, and the system safety control target is finally and effectively achieved.

The application discloses an underwater navigation adaptive filtering method based on Cramer-Rao lower bound constraint, belongs to the technical field of underwater navigation and positioning, and is used for underwater navigation and comprising the following steps: for a discrete time system, a non-Gaussian measurement noise is modeled as a Gaussian scale mixture model; a robust innovation sequence and a weighting factor are obtained through variational iteration update, and are used for correcting a measurement information matrix and updating a posterior Cramer-Rao lower bound; and an experience estimation and the Cramer-Rao lower bound constraint are combined to adaptively adjust a process noise covariance matrix, so as to optimize state prediction. Through the closed-loop feedback and decision mechanism taking the Cramer-Rao lower bound as a theoretical performance scale, the adaptive process is broken through from the open-loop estimation depending on experience data to the intelligent optimization anchored and calibrated by the theoretical optimality criterion, so that precise decoupling and cooperation of time-varying noise tracking and abnormal interference suppression are realized, and finally the performance of underwater high-precision and robust navigation and positioning is improved.

This invention provides an intelligent control method and system for an upper limb rehabilitation robot. Addressing the uncertainty of the discrete-time dynamic model of a real robotic arm, this invention designs a tracking error and then constructs an adaptive neural network controller based on discrete deterministic learning theory. This controller can accurately model / learn the internal unknown dynamics along a periodic trajectory, and then utilize the learned knowledge to construct an experience-based learning controller. Furthermore, by combining interpersonal skill transfer methods, the control performance of the rehabilitation robot in uncertain environments is improved. This invention achieves rapid convergence, high precision, and better transient performance, which is of great significance for improving the efficiency of rehabilitation training with upper limb rehabilitation robots.

The invention relates to the technical field of power system control, in particular to a distributed discrete dynamic power distribution method, system and device for constant current load and a storage medium. Acquiring the local output power and the local power generation capacity of the renewable energy RES at each discrete sampling moment; calculating a local capacity utilization rate according to the local output power and the local power generation capacity; obtaining a neighbor capacity utilization rate of the renewable energy RES; calculating a utilization rate deviation according to the local capacity utilization rate and the neighbor capacity utilization rate; generating a local voltage reference value of the renewable energy RES according to the utilization rate deviation, and dynamically adjusting power distribution according to the local voltage reference value; according to the method, the output power of the local renewable energy and the dynamically changing power generation capacity are collected at each discrete sampling moment, so that the inherent stability risk when a continuous time CT control algorithm is implemented in a discrete time DT system is fundamentally avoided, and stable operation of the system under discrete sampling control is ensured.

A circuit and a method are provided for extracting a digitized DC voltage value representing a magnitude of a discrete-time signal received from a discrete-time system. A first full-wave rectifier performs a full-wave rectification of the discrete-time signal to obtain a full-wave rectified signal, and an oversampling analog-to-digital converter digitizes the full-wave rectified signal to obtain a bitstream. The oversampling ADC includes a second-order or higher order delta-sigma modulator and a filter. The filter is configured to filter the bitstream to extract the digitized DC voltage value.

The invention relates to a nonlinear system attack detection method and system based on a constraint polynomial, and the method comprises the steps: converting a polynomial holosymmetric polytope into a constraint polynomial holosymmetric polytope through the order reduction of the constraint polynomial holosymmetric polytope according to the definition of the constraint polynomial holosymmetric polytope; constructing a state observer based on the constrained polynomial holosymmetric polytope; constructing a discrete time system model based on the state variable and the system matrix of the target system; and obtaining an observation result of the state observer in real time, and judging whether the discrete time system model is attacked or not according to the observation result. According to the method, the nonlinear state estimation problem is realized through the state observer constructed by the constraint polynomial holosymmetric polytope, and the nonlinear state estimation problem is used for solving the attack detection problem of a nonlinear system.

The present invention discloses a road congestion degree identification method and early warning system under a discrete time system, the method comprising dividing any time period into two-dimensional time vectors according to the traffic flow characteristics of different time periods; calculating the predicted vehicle arrival rate and predicted vehicle detention probability distribution law under different two-dimensional time vectors; designing a quantitative standard to predict the road congestion degree index under different time vectors; and finally setting the road congestion degree early warning threshold and the road congestion degree level according to the road congestion degree index. The system comprises a traffic condition early warning data receiving module, a signal processing module, a signal light indication module, a buzzer early warning module, a computer storage module and a transmitting antenna module. The present invention predicts the road congestion condition according to the real-time traffic data of different road sections, improves the traffic congestion condition to a great extent, improves the judgment and attendance efficiency of traffic control personnel, reduces the prediction energy efficiency of traffic sections, and alleviates traffic pressure.

The invention discloses an objective function design method in temperature control of rapid heat treatment equipment, and belongs to the technical field of semiconductor heat treatment processes, in the applied rapid heat treatment equipment, a heating lamp group is divided into a plurality of lamp areas, and the control voltage of each lamp area can be independently controlled; the method comprises the following steps: modifying quadratic form performance indexes about a state vector and a control vector to adapt to a discrete time system, and adding a temperature difference item into the quadratic form performance indexes to control the temperature difference to obtain a target function; solving an optimal control sequence of N time points in the future through extreme value necessary conditions; and the control voltage vector of the first time point in the optimal control sequence is applied to the temperature control system so as to adjust the control voltage of the heating lamp group. The method solves the problem that a conventional control algorithm target function lacks consideration of temperature difference factors, can reduce the temperature difference, meets the requirements of customers for temperature difference indexes, and improves the yield and quality of a semiconductor process.

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 present invention relates to the technical field of multi-axis servo systems, and discloses a synchronous control method, device and equipment for a multi-axis servo system. The method includes: measuring the mechanical coupling relationship parameters, motor dynamic characteristic parameters and sampling delay time parameters of the multi-axis servo system and substituting them into the state equation modeling formula to calculate the discrete-time system state matrix; performing Z-transform processing on the discrete-time system state matrix and calculating the frequency response characteristics to obtain the transfer function matrix and the set of target coupling axis pairs; performing hierarchical optimization to obtain a preliminary control instruction; calculating the trajectory tracking error and the inter-axis synchronization error and substituting them into the optimization objective function for sampling delay compensation processing to obtain a compensation control instruction; performing real-time processing on the compensation control instruction and outputting a synchronous control signal to each servo axis to achieve multi-axis coordinated motion. The present invention improves the synchronous control accuracy in the multi-axis servo system by accurately identifying the key coupling axis pairs and sensitive frequency points.

The invention discloses an underwater navigation adaptive filtering method based on Cramer-Rao lower bound constraint, belongs to the technical field of underwater navigation positioning, is used for underwater navigation, and comprises the following steps: modeling non-Gaussian measurement noise into a Gaussian scale mixture model for a discrete time system; a robust information sequence and a weighting factor are obtained through variational iteration updating and are used for correcting the measurement information matrix and updating a posterior Cramer-Rao lower bound; and adaptively adjusting a process noise covariance matrix in combination with empirical estimation and Cramer-Rao lower bound constraint so as to optimize state prediction. According to the method, a closed-loop feedback and decision-making mechanism with the Cramer-Rao lower bound as a theoretical performance scale is introduced, and the adaptive process is broken through from open-loop estimation depending on empirical data to intelligent optimization of closed-loop anchoring and calibration according to a theoretical optimality criterion, so that precise decoupling and cooperation of time-varying noise tracking and abnormal interference suppression are realized; finally, the performance improvement of underwater high-precision robust navigation and positioning is achieved.

The application provides a kind of fast flexible full pure embedded automatic driving lateral motion control method, including establishing vehicle system dynamic model;Vehicle system dynamic model is carried out continuous time system discretization processing, and the discrete time system for model predictive controller is obtained;The cost function and constraint condition of model predictive controller are constructed;The optimal control decision of model predictive controller is calculated according to the cost function and constraint condition.The application can improve the calculation efficiency of automatic driving lateral control MPC to meet the needs of real-time control;The application solves MPC problem through three main steps of series expansion, rational approximation and piecewise continuation, and the calculation speed is faster and the calculation accuracy is higher in the same time, solves the deficiencies such as low calculation efficiency and slow convergence speed of existing method, meets the needs of automatic driving real-time control.