12 results about "Discrete time system" patented technology

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 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 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 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.

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 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 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.