318 results about "Nonlinear system" patented technology

A model predictive controller (MPC) for predictive control of nonlinear processes utilizing an EKF (Extended Kalman Filter) and a nominal trajectory generator. The nominal trajectory generator includes another instance of EKF, a linear corrector, and a time-varying deviation model. A nominal control trajectory can be predicted and an optimal solution for the time-varying deviation model can be computed based on an approximation of a system inverse known as signal de-convolution. The EKF can be utilized to estimate a current process state by supplying a measured output and to predict a future nominal trajectory by supplying a reference output. A Kalman smoother can also be utilized for the signal de-convolution in order to obtain enhanced trajectory estimates.

According to some embodiments of the present invention, a nonlinear system may be modeled by obtaining a transfer function for the nonlinear system and generating a Taylor series expansion of the transfer function. The Taylor series expansion includes a plurality of moments respectively corresponding to a plurality of coefficients of the Taylor series terms. At least one Krylov subspace is derived that matches at least one of the plurality of moments. The nonlinear system is modeled using the at least one Krylov subspace.

A model predictive controller (MPC) for predictive control of nonlinear processes utilizing an EKF (Extended Kalman Filter) and a nominal trajectory generator. The nominal trajectory generator includes another instance of EKF, a linear corrector, and a time-varying deviation model. A nominal control trajectory can be predicted and an optimal solution for the time-varying deviation model can be computed based on an approximation of a system inverse known as signal de-convolution. The EKF can be utilized to estimate a current process state by supplying a measured output and to predict a future nominal trajectory by supplying a reference output. A Kalman smoother can also be utilized for the signal de-convolution in order to obtain enhanced trajectory estimates.

The invention relates to a command filtering backstepping control method based on an interference observer. An anti-interference non-linear controller is designed according to a multi-source interference non-linear system. The command filtering backstepping control method comprises the following steps of firstly building a non-linear mathematical model of a multi-source interference system; secondly designing equivalence interference in an estimation system of the non-linear system interference observer; then substituting equivalence interference estimated values obtained from the non-linear system interference observer into a command filtering backstepping controller, building the command filtering backstepping controller; and finally using a pole allocation method to design command filtering backstepping controller gain and non-linear interference observer gain for guaranteeing a closed loop composite error system to asymptotically be stable. The command filtering backstepping control method is strong in anti-interference capability, and can be applied to anti-interference control for systems such as aircrafts, mechanical arms, biochemical processes and ships.

Compressed sensing is used to determine a model of a nonlinear system. In one example, L1-norm minimization is used to fit a generic model function to a set of samples thereby obtaining a fitted model. Convex optimization can be used to determine model coefficients that minimize the L1-norm. In one application, the fitted model is used to calibrate a predistorter. In another application, the fitted model function is used to predict future actions of the system. The generic model is made of up of constituent functions that may or may not be orthogonal to one another. In one example, an initial model function of non-orthogonal constituent functions is orthogonalized to generate a generic model function of constituent orthogonal functions. Although the number of samples to which the generic model is fitted can be less than the number of model coefficients, the fitted model nevertheless accurately models system nonlinearities.

Predicting the remaining life of individual aircraft, fleets of aircraft, aircraft components and subpopulations of these components. This is accomplished through the use of precomputed databases of response that are generated from a model for the nonlinear system behavior prior to the time that decisions need to be made concerning the disposition of the system. The database is calibrated with a few data points, to account for unmodeled system variables, and then used with an input variable to predict future system behavior. These methods also permit identification of the root causes for observed system behavior. The use of the response databases also permits rapid estimations of uncertainty estimates for the system behavior, such as remaining life estimates, particularly, when subsets of an input variable distribution are passed through the database and scaled appropriately to construct the output distribution. A specific example is the prediction of remaining life for an aircraft component where the model calculates damage evolution, input variables are a crack size and the number of cycles, and the predicted parameters are the actual stress on the component and the remaining life.

The invention relates to a neural network deep learning-based cloth defect detection method. The method includes the following steps that: (1) high-speed line scanning imaging is performed; (2) an improved BP neural network cloth defect detection algorithm is adopted to accurately detect cloth defects; and (3) a convolutional neural network deep learning-based cloth defect classification algorithm is adopted to automatically select characteristic information of defect diversity, and non-linear and systematic processing and classification are performed. According to the method of the invention, algorithms such as image correction, splicing and de-noising are realized in an imaging system through a GPU, so that high-speed and high-quality image acquisition can be realized; the improved BP neural network cloth defect detection algorithm is adopted to detect and eliminate interference factors such as dust, dirt, cotton balls and folds; and the convolutional neural network deep learning-based cloth defect classification algorithm is adopted to monitor various kinds of detects in real time, and the classification algorithm can automatically select the characteristic information of defect diversity and carry out non-linear and systematic processing and classification.

Te invention discloses a battery SOC estimation method applying multiple models parallelly. The method includes following steps: 1), acquiring battery state parameters; 2), normalizing the battery state parameters; 3), respectively substituting the battery state parameters after being normalized into a genetic algorithm optimized BP neural network estimation model, a self-adaptive neural-fuzzy reasoning system model and an OS-ELM neural network model to acquire three SOC estimation results; 4), according to a SOC-OCV relation, acquiring initial estimation voltage values respectively corresponding to the three SOC estimation results acquired in the step 3); 5), respectively calculating difference values between the initial estimation voltage values and voltage, and normalizing the difference values to acquire weighting coefficient of the initial estimation voltage values; 6), calculating a final estimated value of SOC according to the weighting coefficient. By utilizing three importantparameters of a battery, using the three models parallelly, using multiple linear system estimation results to describe a nonlinear system and then weighting, summing and estimating the battery SOC value, estimation accuracy is improved effectively.

Methods and apparatus are disclosed to automatically generate posynonomial performance parameter models for linear and non-linear systems such as electronic circuit characteristics, based on numerical simulations, measurements or observation. The resulting models can be used for automated optimisation of the systems, e.g. optimization-based sizing of electronic circuits so that their performance meets the set of specifications imposed by the designer. They can also be used for optimizing the performance of the system such as the performance of an electronic circuit or to adjust or control the operation of the circuit. The methods and apparatus can be applied to any physical (e.g., electrical, chemical, mechanical, biological) or non physical (e.g., economical, financial, banking) system with which the input is performance values of the system rather than an analytical definition of the system, e.g. the input can be performance data obtained by simulation and / or by measurement and / or observation.

The invention discloses a nonlinear systematic distortion correction device and method. The nonlinear systematic distortion correction device comprises a self-adaptor module and a precorrector module, wherein the self-adaptor module comprises a data acquisition unit, a signal processing unit and a correction parameter identification unit, wherein the data acquisition unit is used for acquiring primary link data and feedback link data; the signal processing unit is used for preprocessing the acquired primary link data and feedback link data; the correction parameter identification unit is used for carrying out parameter identification according to the processed primary link data and feedback link data and obtaining nonlinear systematic correction parameters; and the precorrector module is used for carrying out precorrection processing on the primary link data according to the correction parameters. By utilizing the technical scheme provided by the invention, the problem that the existing digital preprocessing method can not meet the high-linearity requirement is solved, and further the effect of improving the identification accuracy of the correction parameters is achieved.

Methods and systems for estimating differential or difference equations that can govern a nonlinear, time-varying and parameter-varying dynamic process or system. The methods and systems for estimating the equations may be based upon estimations of observed outputs and, when desired, input data for the equations. The methods and systems can be utilized with any system or process that may be capable of being described with nonlinear, time-varying and parameter-varying difference equations and can used for automated extraction of the difference equations in describing detailed system or method behavior for use in system control, fault detection, state estimation and prediction and adaptation of the same to changes in a system or method.

The invention discloses a lake and reservoir algal bloom predicating method based on multielement nonstationary time series analysis and neural network and support vector machine compensation, and belongs to the technical field of water quality monitoring. The method comprises the steps of characteristic factor nonstationary time series modeling, error influence factor kernel principal component analysis, neural network error modeling according to the situation of large sample data, support vector machine error modeling according to the situation of small sample data, final error compensation and predicating result obtaining. The problems that existing algal bloom predication precision is not high, and predication is hard to carry out according to the small sample data are solved, the description of the algal bloom forming process corresponds to reality better, and the result of algal bloom modeling predication is more accurate. The advantage compensation of a time series analysis method suitable for linear system modeling and a statistical learning method suitable for nonlinear system modeling is achieved, and the algal bloom predication accuracy is improved.

The invention discloses an amplifier digital pre-distortion device based on a dynamic fuzzy neural network. The amplifier digital pre-distortion device comprises a digital pre-distortion device, a digital analog converter, a broadband quadrature modulator, a power amplifier, an attenuation coupler, a broadband quadrature demodulator, an analog digital converter and a pre-distortion device training module. Dynamic fuzzy neural network pre-distortion processing is conducted on an inputted digital baseband signal through the digital pre-distortion device, coupling, quadrature demodulation and analog digital conversion are conducted on an analog baseband signal outputted by the power amplifier through the attenuation coupler, the broadband quadrature demodulator, the analog digital converter respectively, and then an outputted digital baseband signal is generated. The inputted digital baseband signal and the outputted digital baseband signal are synchronously inputted into the pre-distortion device training module, and the pre-distortion device training module obtains a dynamic fuzzy neural network module parameter after carrying out parameter training through an online self-organized learning algorithm and sends the dynamic fuzzy neural network module parameter to the digital pre-distortion device. The invention further provides an amplifier digital pre-distortion method. By means of the amplifier digital pre-distortion device and method, the advantages of a fuzzy system and the advantages of a neural network are combined, dynamic adjustment can be achieved according to the complexity of a non-linear system, implementation is easy, and the non-linear characteristic and the memory effect of the power amplifier can be well compensated for.

A linearity compensation circuit provides cancellation of harmonic distortion introduced by a non-linear system into an input signal by providing a separate harmonic correction component for each harmonic of a fundamental frequency of the input signal. The harmonic correction components are summed with a delayed version of the input signal to produce a corrected input signal for input to the non-linear system. The separate harmonic correction components are generated by respective harmonic correction units, each having a programmable input filter, a Hilbert Transformer filter and a plurality of phase shifters. The output of each programmable input filter is input to the Hilbert Transformer filter and a compensating delay. The output from the Hilbert Transformer filter and the delayed version of the filtered input signal from the compensating delay are input to parallel phase shifters, and the respective outputs from the phase shifters are multiplied together to produce the separate harmonic correction component for each harmonic correction unit.

The present invention provides a method for real-time online control of hybrid nonlinear system, characterized in that, it comprises the following steps: a. the current observational state of plant in the network is transmitted to first controller, where said first controller is used to provide real-time online control for plant, which guarantees the asymptotic stability of the controlled plant in the network; b. Said first controller obtains the current control output information according to the current observation state information; c. Giving said output control information to said controlled plant in the network as feedback, wherein said controlled plant in the network is nonlinear hybrid system. The present invention realizes the control of nonlinear hybrid system through network by providing control method with quantized controller to guarantee the asymptotic stability of the system. Especially, the load capacity of network will be greatly reduced by transmitting the observation information after being quantized.

The invention relates to an active disturbance control method of a flexible direct current power transmission system. The method adopts the technical scheme that the method comprises the following steps that a rectifier controller is arranged at the head end of a direct current power transmission line of the flexible direct current power transmission system to carry out the constant direct current voltage control on a rectifier at the alternating current system side, an inverter controller is arranged at the tail end of the direct current power transmission line to carry out the constant alternating current voltage control on an inverter, and a double-closed loop type vector control strategy is adopted for the constant alternating current voltage control of the inverter, wherein an outer loop of the voltage is controlled by an active disturbance controller, an inner loop is controlled by current decoupling, and the direction of bus voltage at the inverter alternating current side is fixed to the direction of an axis d. The method has the advantages that the overshoot of the bus voltage at the passive network alternating current side is reduced when the flexible direct current power transmission system starts to respond, the precision and the stability of the alternating current voltage control are improved, the inside and outside total disturbance of the flexible direct current power transmission system can be evaluated in a real-time way, the feedforward compensation can be timely carried out, the disturbance control capability of the flexible direct current power transmission system is improved, and the control requirements of a non-linear system can be met.

The invention provides a nonlinear system filter design method based on a self-adaption event triggering mechanism. The method comprises the steps of firstly building a state equation of a non-linear system; setting self-adaption event triggering conditions; building a filter model and a system error model of the non-linear system and obtaining an optimal filter of the non-linear system. The method employs the self-adaption event triggering mechanism for the non-linear system which is characterized by random nonlinearity, multiplicative noise and state constraints, so that the performance of filters is improved.

The invention discloses a speed-tension coordination control method for jigger fabrics; the method mainly uses a fuzzy control system to compare the sampling speed of a photoelectric coder with the set speed, and compare the tension-detection value of a tension detection device with the set tension, so as to timely adjust the control rules according to the dynamic changes of the errors to subsectionally and alternately control the speed and the tension to respectively obtain a coiling frequency fA and an unwinding frequency fB needed in maintaining the equal linear velocity constant tension of dyeing fabrics; a coiling electromotor and an unwinding electromotor are respectively controlled by a coiling transducer working in speed mode and an unwinding transducer working in torque conversion mode. The method solves the coupling problem between the speed and the tension of the jigger fabrics and makes more accurate speed-tension coordination control of the double-input / double-output, coupled and time-varying non-linear system of jiggers, so as to reduce dyeing color difference caused by instable speed and tension and improve the dyeing quality of fabrics.

An optical control system is described which is capable of maintaining and optimizing a fiber-optic transport system within it's domain of control while interacting with other optical systems which are controlled independently. This allows the optical system to be incorporated as a building block into a larger optical network in a relatively arbitrary fashion. This provides an underlying control system for a non-linear system like optics network that is flexible and extensible.

The invention discloses an SVM (support vector machine) parameter selection method based on multi-kernel function adaptive fusion. Compared with the prior art, the method achieves the specific analysis of the characteristics of a local kernel function, a global kernel function, a mixed kernel function and a multi-kernel function. The method comprises the steps: combining all fusion coefficients, kernel function parameters and regression parameters of the multi-kernel function together to serve as a parameter state vector, thereby enabling a model selection problem to be converted into a state estimation problem of a nonlinear system; carrying out the parameter estimation through fifth-order volume Kalman filtering, and achieving the adaptive fusion of weighted coefficients of the multi-kernel function and the selection of kernel parameters and regression parameters.

The invention discloses a method for monitoring autonomous integrity of a GNSS receiver, relates to a receiver autonomous integrity monitoring technology, and belongs to the technical field of measurement and test. The method adopts a CKF-based and innovation extrapolation method to achieve monitoring of the autonomous integrity of the GNSS receiver, and innovation generated in an innovation extrapolation process and a variance-covariance matrix thereof construct test statistic of each extrapolation moment, micro slowly-changing pseudo-range deviation can be tested; a machine learning algorithm is adopted to judge and classify the test statistic of the each extrapolation moment, and dynamically updates a fault database used for generating a machine learning rule at the same time; on-line learning of judgment rules of unmodeled fault types has the advantages of being less in calculation amount, proper in precision, suitable for nonlinear systems and capable of monitoring the micro slowly-changing pseudo-range deviation with high confidence degree.

The invention discloses a triaxial magnetometer self-error online correction method, and belongs to the electronic instrument measuring calibration and correction field; the method mainly comprises the following steps: obtaining a modular square expression of a geomagnetism vector measuring value containing three error parameters; building a triaxial magnetometer error parameter estimation systemmodel; obtaining an estimated value of the error parameter; obtaining the corrected geomagnetism vector measuring value. The method utilizes an adaptive particle filter algorithm so as to obtain a geomagnetism field vector compensation value in real time; the method proposes the adaptive particle filter so as to fast estimate the parameters, thus improving the estimation efficiency, fast estimating the error parameters, and improving the parameter estimation instantaneity; in addition, the particle filter has a better filtering effect on a non-linear system; therefore, the calibration method is low in cost, and good in applicability.

The invention relates to a particles-impact damper with an elastic support. The inner part of the particles-impact damper is provided with an elastic energy-storage component and a plastic impact energy-dissipation component. The invention is characterized in that the particles-impact damper is connected with a controlled main system through the elastic support and the natural vibration frequency of the particles-impact damper is not greater than that of the controlled main system. The elastic support can be a spiral spring, a plate spring or an elastic cushion; and free movement direction of the elastic support is identical to the vibration direction of the controlled main system. The particles-impact damper can effectively reduce acceleration in impact process and decrease impact on the controlled main system so as to consume vibration energy of the controlled main system to the maximum extent, reduce the energy retransmission to the controlled main system and alleviate the impact of ultraharmonics and subharmonic. The particles-impact damper can be applied to the vibration control of nonlinear systems with low frequency and dense modes.

The invention discloses a pavement peak-value attachment coefficient estimating method on the basis of a UKF (unscented kalman filter) and a corrected Dugoff tire model. The method is characterized by comprising the following steps: collecting each sensor signal of a vehicle in real time, estimating a slip rate and a slip angle of each wheel by utilizing a longitudinal kinetic equation and a geometric coordinate relation of the model; and transmitting the estimated slip rate, vertical force, the slip angle and the like to a UKF coefficient calculation module based on the corrected Dugoff model to obtain a coefficient vector of a nonlinear system, and transmitting the vector and real-time estimated longitudinal force to a UKF pavement peak-value attachment coefficient estimation module to solve a peak-value attachment coefficient. A vehicle state observation system is used for collecting the signals in real time, so that the real-time property of the calculation is guaranteed, and the estimation accuracy on pavement situations which are not fitted is high. By utilizing the corrected Dugoff tire model and a UKF theory, the solving process is simple, the number of operation is small, the rapidness in operation is realized, and the convergence time is short. The method is good in robustness, capable of well recognizing the pavement situations of each wheel and suitable for being used for estimating the pavement peak-value attachment coefficient in real time.

The invention belongs to the technical field of photovoltaic grid-connected inverters, and particularly relates to a T-type three-level inverter finite set model prediction control method and system, which have the advantages that the delay time is shortened when rapid dynamic response is ensured and a nonlinear system is introduced. The prediction control method comprises the following steps: the three-phase output of an inverter is grid-connected with a power grid through inductance and resistance loads under the condition that an equation is met, wherein the equation, R and L are respectively an resistance load and an inductance load; u refers to the voltage vector generated by the inverter, e refers to the voltage vector of the power grid, and I refers to current vectors of the loads; a voltage uaN, a voltage ubN and a voltage ucN of output phases of the inverter are converted through Clark, and an abc three-phase static coordinate system is converted into an alpha-phase and a beta-phase static coordinate system.

The invention discloses a water quality parameter prediction method based on a multilayer RNN and a D-S evidence theory. Aimed at a high-dimension, multi-peak, discontinuous and non-convex complex nonlinear system of the water environment, historical data of water quality parameter content is used to train a deep RNN predication model, the model is constructed, and the future content of the waterquality parameter is predicted preliminarily; and the problem of uncertainty can be solved in the mathematical theory via the D-S evidence theory. Thus, conflict solution and evidence fusion can be realized by utilizing the D-S evidence theory on the basis of the RNN model prediction result, the prediction precision of the single parameter is improved, and early warning of multiple parameters is more accurate.