97 results about "Time-variant system" patented technology

A method for the estimation of the state variables of nonlinear systems with exogenous inputs is based on improved extended Kalman filtering (EKF) type techniques. The method uses a discrete-time model, based on a set of nonlinear differential equations describing the system, that is linearized about the current operating point. The time update for the state estimates is performed using integration methods. Integration, which is accomplished through the use of matrix exponential techniques, avoids the inaccuracies of approximate numerical integration techniques. The updated state estimates and corresponding covariance estimates use a common time-varying system model for ensuring stability of both estimates. Other improvements include the use of QR factorization for both time and measurement updating of square-root covariance and Kalman gain matrices and the use of simulated annealing for ensuring that globally optimal estimates are produced.

A limit time cooperative control method of a mechanical arm servo system includes: building the dynamic model of the mechanical arm servo system, and initiating the system state, sampling time and related control parameters; approximating the nonlinear input dead zone linearity in the system into a simple time-varying system according to the differential mean value theorem, and deriving a mechanical arm servo system model with an unknown dead zone; calculating the tracking error, the non-singular terminal sliding mode surface and the first derivative of a control system; selecting a neural network approximation unknown function on the basis of the mechanical arm servo system model with the unknown dead zone, designing a neural network limit time cooperative controller according to the system tracking error and the non-singular terminal sliding mode surface, and updating a neural network weight matrix. By the method, additional inverted compensation of a nonlinear dead zone can be avoided, sliding model high-frequency buffeting is reduced, and limit time fast tracking of the mechanical arm servo system is achieved.

A rotary-table servo system neural network control method comprises (1) building a mechanical dynamic model of a permanent magnet synchronous motor rotary-table servo system, and initializing the system state, sampling time and relative control parameters; (2) according to the differential mean value theorem, enabling a non-linear input dead zone in the system to linearly approximate to a simple time-varying system, avoiding complex calculation of dead-zone inverse compensation, and finally inferring a rotary-table servo system model provided with an unknown dead zone; (3) at each sampling moment, calculating and controlling a system tracking error, a fast terminal sliding mode surface and a first-order derivative of the system; (4) based on the rotary-table servo system model provided with the unknown dead zone, selecting a neural network approaching unknown trend, designing an adaptive robust finite-time neural network controller according to the system tracking error, the fast terminal sliding mode surface and the first-order derivative of the system, and updating a neural network weight matrix; and (5) entering the next sampling moment, and repetitively executing the steps from (3) to (5).

The invention relates to an SOC estimation method of a variable length sliding window by identifying battery parameters. The method comprises the steps that the battery parameters are identified in real time by a variable length sliding window least square method, then the battery parameters are used for conducting self-adaptive extended Kalman filter to estimate the battery SOC, and therefore the influence of the battery parameter change caused by battery ageing on estimation precision of the battery SOC is avoided. According to the method, the time-varying battery parameters can be tracked effectively, and no dependency exists to the selection of initial data; the self-adaptive extended Kalman filter is more suitable for the state estimation of a nonlinear time-varying system; a matrix inversion operation is converted into scalar division operation, and better real-time performance is achieved.

The invention provides a specified performance back-stepping control method of a mechanical arm servo system. The method comprises the steps of constructing a dynamic model of the mechanical arm servo system; initializing the system state, sampling time and control parameters; linearly approximating a nonlinear input dead area of the system as a simple time varying system according to the differential mean value theorem; deviating a mechanical arm servo system model with the unknown dead area; calculating the tracking error of the control system, FC (funnel control) error variation and differential. With the adoption of the method, the problem of control buffeting of a sliding die can be reduced, the system of the dead area input on the system can be effectively avoided, and the specified performance control of the mechanical arm control system can be achieved.

The invention discloses a method for designing a controller of a time-varying aircraft model with uncertainty, which is used for solving the technical problem that the existing robust control theory lacks design steps, so the flight controller is hard to design directly. The technical scheme is as follows: the time-varying system segmentation robust stability and solvability conditions are given, selection of desired closed-loop poles of linear time-varying system state feedback is directly utilized and a constraint condition inequality direct design feedback matrix is given according to the characteristic that all the real parts of all the desired closed-loop poles are negative, so that the engineering technicians in the research field directly design the flight controller for the time-varying aircraft model with uncertainty obtained through wind tunnel or flight tests, thus solving the technical problem that the current researches only give the robust stability inequality but can not directly design the flight controller.

The invention discloses an unknown adaptive Kalman filter method for a system model, which aims at solving the problem that the filter of the Kalman filter method has reduced accuracy even is diffused under the condition that the system status model is unknown or time-variant. Aiming at the unknown or time-variant condition established by the system status model, information is adopted to calculate filter residues based on Kalman filter; a weighting efficient is calculated according to the filter residues and the ratio of measurement noise covariance; the noise covariance matrix of system status is revised in real time through the weighting efficient, so that the problem of accuracy reduction even diffusion of the Kalman filter caused by the unknown or time-variant system model can be solved effectively. Under equal conditions, the X axle filtering error mean is reduced to 0.0056 from 0.1231 in the prior art, and the Y axle filtering error mean is reduced to 0.0039 from 0.3895 in the prior art.

Methods, devices and algorithms for the linearization of nonlinear time variant systems and the synchronization of a plurality of such systems. An example of such a system would be a transmit path, including the power amplifier, as used in wireless transmit systems. Advances made in CMOS technology, digital to analog converter (DAC) technology make it possible to implement a substantial part of such a system in the digital domain. Another aspect is the integration of a substantial part of such a transmit system in a single integrated circuit (IC). A digital implementation that allows for linearization of a broad range of nonlinear and time variant effects. Since this digital implementations operate a high clock frequency a energy efficient implementation is essential to keep the power consumption under control. Another aspects is the reuse of methods, devices and algorithms used for the linearization a transmit system to synchronize multiple transmit systems.

The invention discloses a fault estimation method for a class of event-triggered transmission time-varying systems based on an unknown input observer technology. The method comprises establishing a system state space model, and introducing an event trigger signal transmission strategy therein; respectively augmenting an original system state and each possible fault into new state signals, designing filter parameters, recursively solving the least upper bound of an error covariance and minimizing the least upper bound, and calculating the filter gain; utilizing a filter residue corresponding tothe normal situation and a reasonably set threshold to perform fault detection; utilizing a residual matching method to implement fault separation, and taking a fault corresponding component in the corresponding filtered estimation value as a fault estimation value.

The invention provides a networking time-delay compensation and control method using the active-disturbance-rejection control technology for a permanent magnet synchronous motor. The method comprises the following steps: 1) building a permanent magnet synchronous motor control system model containing time-varying network-induced delay, and describing a permanent magnet synchronous motor control system as a discrete-time linear time-varying system with one-step input delay, so as to describe an undetermined dynamic part of the system caused by the time-varying delay as the additive noise of the system; 2) designing an expanding state observing device, and estimating the undetermined dynamic part caused by the time-varying delay as part of total disturbance through the expanding state observer; 3) compensating the undetermined dynamic state caused by the time-varying delay in the networking permanent magnet synchronous motor control system, wherein all undetermined dynamic parts caused by the time-varying delay and the internal and external disturbances in the system can be counteracted during compensating. The method shows high capability on inhibiting the nondeterminacy caused by time-varying delay, the internal and external disturbances of the system, and the nondeterminacy of a model.

The invention provides a cement combined-grinding prediction control method based on Bang-Bang control. The Bang-Bang control process is employed for mixing operation experiences such as observation, waiting, determination and adjusting of an operator into a control algorithm, the tracking error convergence rate is accelerated, and the system overshoot is reduced, and thus the combined-grinding system production process has good stability and the precise rapid control purpose is reached. Aiming at cement mill load control, LPV (Linear Parameter-Varying) prediction control is applied to solve control problems of variation and nondeterminacy of parameters of a linear time-varying system, and enable the system to have good robustness.

The invention discloses an active noise control system based on a variable step LMS algorithm. According to the system, a filter based on the variable step LMS algorithm is applied to identification of a secondary channel; a filter weight value is updated by adjusting a step value of the filter; in order to obtain a relatively fast convergence rate, the step value of self-adaptive filtering in an initial phase is relatively high; and when the self-adaptive filtering approaches a steady state, the step size is reduced, so a relatively low steady state error can be obtained. The variable step LMS algorithm is applied to the identification of the secondary channel of the active noise control system, and the contradiction among the convergence rate, the steady state error and a tracking capability of a time-varying system can be solved well.

The invention discloses a dynamic state estimation method based on self-adaptive volume Kalman filtering, which is used for realizing accurate estimation of a dynamic state variable of an electric power system generator. According to the method, a fading memory index weighted Sage- Husa noise statistical estimator is introduced into volume Kalman filtering; and the mean value and the variance of the time-varying system noise are dynamically estimated and corrected, the influence of system noise matrix mismatching on the state estimation precision is inhibited, and the accurate estimation of the state variable of the generator is improved. The algorithm considers the actual engineering background, is simple and convenient, and has high engineering application value.

The invention provides an error modeling compensation method and system of an optical remote sensing satellite image time-varying system. The error modeling compensation method comprises the following steps: based on observation data of a plurality of satellite sensors at a plurality of imaging time periods of a ground camera, obtained by an optical remote sensing satellite, resolving a relative mounting parameter change sequence between the satellite sensors to obtain an optimal mounting parameter estimated value; according to the observation data of the plurality of imaging time periods of the plurality of satellite sensors and the mounting parameters obtained by calibration, realizing optimal information fusion of the plurality of satellite sensors, and outputting high-precision attitude data; realizing ground camera precise attitude inversion by adopting a strict geometrical imaging model; constructing a time-varying system error compensation model by adopting Fourier series; and fusing attitude and ground camera precise attitude results according to information of the plurality of satellite sensors, and realizing optimal estimation of parameters of the time-varying system error compensation model based on a least squares principle. With the adoption of the error modeling compensation method and system, on-track compensation of low-frequency errors and non-uniform attitude standard errors of the satellite sensors can be realized, and influences on errors of a high-resolution-ratio optical remote sensing image out-of-control positioning time-varying system are effectively weakened.

The invention discloses a distributed fusion filtering method for simultaneously estimating an unknown input and a state, which is used for solving a problem that an existing distributed filter does not give out a state equation for estimating the unknown input. The distributed fusion filtering method comprises the following steps of: establishing a discrete linear time-varying system with a plurality of sensors; based on an observed quantity of each sensor, designing a three-step recursive filter; and deducing a cross-covariance matrix between any two local estimations, and then according tothe acquired local estimations and the cross-covariance matrix, giving out a distributed scalar weighting fusion filter of each component of the state by utilizing a linear minimum variance componenton the basis of a scalar weighting fusion estimation algorithm. The distributed fusion filtering method disclosed by the invention is superior to an existing distributed fusion filtering method in estimation accuracy on the state, and gives out unbiased estimation on the unknown input by utilizing the filtering algorithm for simultaneously estimating the unknown input and the state when the systemhas the unknown input with unknown statistic characteristics, and yet no estimation on the unknown input is given out in existing literatures.

The invention provides a finite time domain Hinfinite control method of a time-varying system under the influence of a high-speed communication network, belonging to the networked control system field. At first, under the influence of random communication protocol and high-speed communication network, a networked time-varying system model with multiplicative noise, stochastic time delay and quantization error is proposed. Then observer-based state feedback controller is designed. By using Lyapunov stability theory and linear matrix inequality analysis method, the sufficient conditions for theclosed-loop system to satisfy the Hinfinite performance requirement are obtained. Finally, a finite-time-domain Hinfinite controller design algorithm based on cone-complementary linearization is proposed, and the time-varying gain matrices of the observer and the controller are obtained by using Matlab LMI toolbox. The invention considers the influence of the stochastic communication protocol andthe high-speed communication network on the networked time-varying system under the actual situation, and the existence of multiplicative noise, the stochastic time delay and the quantization error ofthe system, and is suitable for the finite time domain Hinfinite control of the general networked time-varying system, and reduces the conservatism.

A mechanical-arm servo-system neural-network full-order sliding mode control method with dead-zone compensation is disclosed. Aiming at a mechanical arm servo system which contains a dynamic execution mechanism and is with unknown dead-zone input, a full-order sliding mode control method is used and a neural network is combined so as to design the mechanical-arm servo-system neural-network full-order sliding mode control method with the dead-zone compensation. A dead zone is converted into a linear time-varying system, and then the neural network is used to approach an unknown function so as to compensate an additional influence of a traditional unknown dead zone and an unknown parameter of the system. In addition, a full-order sliding mode surface is designed so as to guarantee rapid and stable convergence of the system; generation of a differential term is avoided in an actual control system so that buffeting is improved and a singular problem is solved. The invention provides the control method which can improve a buffeting problem of the sliding mode surface, solve the singular problem and can effectively compensate a system unknown dynamic parameter and unknown dead zone input so that rapid and stable control of the system is realized.

A dead zone compensating control method for a mechanical arm servo system with guaranteed transient performance comprises following steps: a dynamic model of a mechanism arm servo system is established, and the system state, sampling time and control parameters are initialized; according to the differential mean value theorem, a non-linear input dead zone in the system is linearly approximated as a simple time-varying system, and a mechanism arm servo system model with unknown dead zones is deduced; a bounded function limiting the tracking error transient performance is introduced; a conversion error variable is defined by use of a error conversion method; a virtual control quantity of the system is designed by use of the Lyapunov method; an unknown virtue control quantity is estimated by use of a neural network; a first-stage wave filter is added to avoid problems such as inversion complexity blast. The invention provides a method for effectively compensating the influence of unknown dead zone input on the system and avoiding the problem of complexity blast brought by the inversion method, and for increasing the system transient tracking performance and guarantying stable tracking control of position signals.

The invention relates to the field of nonlinear time-varying system optimization control, in particular to a control strategy for achieving multivariable PID in a PLS frame on the basis of a Gaussianprocess model. The problems that by means of existing control strategies, interaction among multiple loops cannot be eliminated, a built model is not complete, and the control performance is poor aresolved. The control strategy for achieving multivariable PID in the PLS frame on the basis of the Gaussian process model comprises the steps that 1, parameters of a PID controller are given; 2, the GP(Gaussian process) model is used for achieving the control strategy of multivariable PID, wherein firstly, an MIMO system is subjected to decoupling, secondly, the model uncertainty is improved, thirdly, a covariance function is chosen, and the parameters are optimized; 3, the PID controller is set, wherein a gradient-based optimization algorithm is used for using the GP model for adjusting the PID controller. The control strategy for achieving multivariable PID in the PLS frame on the basis of the Gaussian process model has the advantages that the GP model is used for providing a predictionvariance, and the prediction reliability of a local random area is shown; cross coupling effects brought by a multivariable control process are taken into account, through the PLS frame, the MIMO system is decoupled into single loops, and then based on the GP model, the PID controller parameters are adjusted separately.

The invention discloses a stability analysis system and method of a time-varying power system. The system comprises a data acqusition module, a wind velocity fitting prediction module, a time-varying system establishment and order reduction module, a stability criterion solving module and a result output module, wherein the data acqusition module is used for acquiring data; the wind velocity fitting prediction module is used for performing fitting prediction on conditional wind velocity trend in short time, sectioning the wind velocity and calculating a conditional characteristic wind velocity probability density matrix of each section according to a fitting model; the time-varying system establishment and order reduction module is used for establishing a continuous markov time-varying power system model considering a wind velocity random characteristic and reducing order of the system; the stability criterion solving module is used for solving whether the system is stable by using a feasibility problem solving method in a linear matrix inequality. Through the stability analysis system and method of the time-varying power system, the problem that the stability of the time-varying power system is difficult to distinguish caused by the wind velocity random characteristic in case of fan connection can be effectively solved.

The invention provides a fault estimation method under a sensor network environment and based on the Green space theory. The method includes the following steps that a dynamic model of a time-varying system with actuator and sensor faults is established; various sensors and measurement output adjacent to the sensors are synthesized; distributed fault estimation is conducted on the dynamic model; the existence judgment conditions of fault estimators on nodes are acquired through the linear estimation theory of Green space; if the existence conditions are satisfied, estimator parameter matrixes on the nodes are acquired through the update analysis and projection theorem in the Green space; the estimator parameter matrixes are substituted into a distributed fault estimation formula, and distributed fault estimation of the time-varying system with the actuator and sensor faults is conducted under the sensor network environment. The method can effectively solve the coupling problem between the sensor nodes under the sensor network environment and achieve the purposes of a small conservative property and high estimation accuracy.

The invention discloses a precision current detection apparatus of an NB-IOT terminal under multiple operation modes. The apparatus comprises an overall scheduling unit, a radio frequency unit, a base-band signal generating unit, a base-band signal processing unit, a combiner, a synchronous processing unit, a high speed AD conversion unit, a multi-range galvanometer and a voltage output unit. The radio frequency unit comprises a radio frequency emission unit and a radio frequency receiving unit. The apparatus is integrally designed with a power supply current through a protocol stack and accurate current measurement to an NB-IOT module at any moments is realized so that a current measurement problem of a time-varying system is solved and dynamic current measurement at a microsecond grade can be realized, a measured speed is fast and good synchronization is possessed. Besides, the apparatus possesses a good cost performance, the size is small and weight is light. Compared to a traditional measurement system, usage convenience of the apparatus is greatly increased. Under signaling mode measurement, without any external design, whole measurement can be completed so that measurement efficiency is greatly increased.