42results about How to "Guaranteed identification accuracy" patented technology

The invention discloses a neural-network self-correcting control method of a permanent magnet synchronous motor speed loop. The method is characterized by: taking a current loop and a motor as generalized objects; firstly, collecting information, such as a rotating speed, a current and the like; using an adaptive linear time-delay neural network to carry out off-line parameter identification to the motor; then, taking a weight obtained through off-line learning as an initial value of on-line learning; finally, carrying out on-line parameter identification to the system, calculating a load torque of the motor according to the identified parameter; designing a neural-network self-correcting control law according to the obtained parameter value and a load disturbance value, adjusting the network weight on line according to an error between a controlled object and an identification model, and then setting the parameter of the neural-network self-correcting controller on line so as to realize online adjustment of the controller parameter. Uncertainty of the system and influence brought by the external disturbance can be eliminated. Dynamic performance and an anti-disturbance ability of a servo system can be improved.

The present invention proposes a parameter identification method for the dynamic model of an industrial robot, comprising: establishing a dynamic model of an industrial robot with six joints connected in series; setting a periodic excitation signal, and using the periodic excitation signal to optimize the identification track of the dynamic model of the industrial robot ;Measure the data of the dynamic model of the industrial robot at multiple time points, and average the data; estimate the parameters of the dynamic model of the industrial robot according to the data average processing results, and obtain the estimator of the unknown parameter vector; The industrial robot dynamics model and estimated parameters are verified, including: calculating the prediction error of the estimated parameters and the measured parameters of the industrial robot dynamics model, and using the residual root mean square of the prediction error to verify and evaluate the industrial robot dynamics model. The invention reduces the influence of measurement noise on parameter estimation and improves the precision of dynamic parameter identification.

Provided is an on-line identification method for unbalance of a magnetic suspension rotor based on current test mass. First, a mathematical model of static and dynamic unbalance of the rotor is established, then at rated rotating speed, a current transformer measures a current of a magnetic bearing coil, and a displacement sensor measures the displacement of a radial channel of the magnetic suspension rotor; secondly, the rotating speed is kept unchanged, a test mass current is added for the first time, the current of the magnetic bearing coil is measured, and the displacement of the radial channel of the rotor is measured; at the same rotating speed, the test mass current is added for the second time, the current of the magnetic bearing coil is measured, and the displacement of the rotor on the radial channel is measured; finally, the static and dynamic unbalance of the rotor is identified according to the currents and displacements measured at three times. The on-line identification method is simple and easy to implement by starting a car at one time and is applied to identification of the static and dynamic unbalance of the magnetic suspension rotor in the on-line dynamic balance process.

The invention discloses an industrial robot dynamic parameter identification method considering joint elasticity. The problem that in the prior art, each joint needs to be provided with double encoders to achieve identification is solved. the beneficial effect of accurately identifying the dynamic parameters is achieved; According to the scheme, the industrial robot dynamic parameter identification method considering the joint elasticity comprises the steps of analyzing and classifying unknown parameters, establishing a linear identification model considering the friction coefficient of a motor, combining a static test to realize respective identification of the unknown parameters, and utilizing a separation identification strategy and an approximate processing method to accurately identify the dynamic parameters.

The invention discloses a rotary shaft and position-independent geometric error identification method based on ball-bar measurement. The method comprises the following steps of determining the measurement position of a ball-bar according to the structure of a machine tool and the type of rotary shafts, mounting a spindle tool cup and a base tool cup and conducting correction; mounting the ball-bar, detecting the shaft A and the shaft C without a lengthening bar to obtain ball-bar readings, and conducting compensation calculation on position errors of the shaft A and the shaft C; changing the length of the ball-bar by additionally arranging the lengthening bar, changing the position of the spindle tool cup, rotating the shaft A and the shaft C correspondingly to obtain the ball-bar readingsand obtaining eight position-independent geometric errors of the rotary shafts of the machine tool through calculation. Through the method, the position-independent geometric errors of the rotary shafts can be identified quickly and effectively, the identification precision is high, and the practicality is good.

The invention discloses a flux linkage full-rank identification method for a permanent magnet of a PMSM. According to the method, an unscented-Kalman-filter-algorithm-based parameter sensitivity analysis for the flux linkage identification precision of a permanent magnet of a PMSM is carried out; a permanent magnet flux linkage full-rank identification method of different rotating speed zones of a PMSM drive system is determined. With the unscented-Kalman-filter-algorithm-based permanent-magnet flux linkage full-rank identification method of different rotating speed zones of a PMSM drive system, influences on the permanent magnet flux linkage identification precision by the system measurement noises, PMSM parameter changing, and a degenerate-rank phenomenon of an identification model can be eliminated; linear error occurrence and complicated Jacobian matrix calculation during the state identification process of the traditional extended Kalman filter algorithm can be avoided; and the algorithm implementation difficulty can be reduce. Meanwhile, the number of to-be-identified parameters can be reduced at a high-speed operating zone in a PMSM drive system; the operand of the identification algorithm can be reduced; and reasonable consideration of the identification precision and the identification speed can be realized.

The invention discloses a machine tool rotating shaft geometric error identification method based on ballbar measurement. The machine tool rotating shaft geometric error identification method based onballbar measurement comprises the following steps of according to the positions of a machine tool structure and a rotating shaft, installing a ballbar, and calibrating a main shaft tool cup and a base tool cup; utilizing a corresponding machine tool code for controlling only one rotating shaft of the machine tool to rotate; utilizing the ballbar for preliminarily identifying geometric errors, irrelevant to the position, of the rotating shaft; and changing the installation position of a ballbar base, utilizing the ballbar for accurately identifying the geometric errors, irrelevant to the position, of the rotating shaft, and obtaining eight geometric errors, irrelevant to the position, of the rotating shaft of the machine tool. The method provided by the invention can be used for quickly and effectively identifying the geometric errors of the rotating shaft of the machine tool, and is high in identification accuracy and good in practicability.

The invention discloses an unbalance compensation method for rotor unbalance coefficient variable-angle iterative search. The method comprises the steps of vibration signal processing, variable anglecompensation algorithm recognition coefficient searching and compensation current calculation, wherein a vibration signal processing module calculates a rotor unbalance vibration signal amplitude according to rotor vibration information, rotor rotation angle information and rotation speed information; the variable-angle compensation algorithm is used for searching for recognition coefficients, the rotor unbalance vibration signal amplitude serves as the judgment basis, the unbalance coefficients are repeatedly identified and optimized through a variable-angle iterative search method, and a set of unbalance coefficients enabling the unbalance vibration amplitude to be reduced to the minimum are obtained through iteration. Corresponding compensation current is calculated according to the unbalance coefficient and input into the electromagnetic coil to restrain unbalance vibration of the rotor. The method can be used for an active rotor system with an active control unit, unbalance on-line compensation of the active rotor system is achieved, and unbalance vibration in the acceleration and deceleration operation process within the whole rotating speed range is restrained.

The invention relates to a plant identification method based on a cloud model and a TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. The method comprises the following steps: constructing a plant shape feature specimen database; utilizing a trapezium-cloud model to compare the shape features of a plant to be identified with the plant shape feature specimen database to acquire a comparative membership between the plant to be identified and the shape feature specimen database, thus completing the primary identification of the plant to be identified; when a plurality of identification results exist, utilizing a normal cloud model to carry out accurate matching calculation on the retrieval results so as to acquire a comparative accurate membership between the plant to be identified and the shape feature specimen database; and comprehensively evaluating the membership by utilizing the TOPSIS method to identify the plant. The method can comprehensively evaluate the final identification result by adopting the TOPSIS method, and can completely, reasonably and accurately carry out advantage and disadvantage sequencing according to certain evaluation indexes, so that the evaluation process is clear, and the evaluation result is objective.

An adaptive active damping control method for a three-phase current-type PWM rectifier includes the following steps of 1, establishing a parameter adjustable model based on a model reference adaptiveidentification principle; 2, sampling and reconstructing a critical voltage and current signal; 3, establishing a value optimization function; 4, obtaining an adjustable model adaptive adjustment lawaccording to the value optimization function and an optimization purpose; 5, designing the optimal virtual resistance of an LC filter based on parameter online identification; and 6, performing adaptive active damping control based on the parameter online identification.

The invention provides a battery thermal parameter identification method based on a dimensionless model, and belongs to the technical field of batteries. The method comprises the following steps of bonding two same battery samples by using a heating sheet; establishing a dimensionalized heat transfer model for the battery sample and carrying out non-dimensionalization; respectively arranging a plurality of thermocouples on the non-contact surfaces of the two battery samples and the heating sheet; and after the heating time is determined, heating the heating sheet to obtain a battery temperature curve corresponding to the position point of each thermocouple on the two battery samples, and optimizing the dimensionless heat transfer model to finally obtain a thermal parameter identification result of the battery samples. According to the method, the thermal parameters of the battery are rapidly identified while the identification precision is ensured, and the identification efficiency isimproved.

The invention discloses a virtual load dominant parameter identification method based on incremental learning. The virtual load dominant parameter identification method comprises the steps that (1) enabling virtual load model dominant parameters to be subjected to random value simulation; (2) establishing a deep learning neural network; (3) carrying out deep neural network incremental learning; and (4) carrying out online rapid identification and cyclic training. The feasibility of incremental learning applied to power system analysis is mainly introduced and is combined with load parameter identification, the training efficiency is improved while the identification precision is ensured, the storage overhead is maintained while catastrophic forgetting is prevented, a new thought is provided for processing training samples in parameter identification, and a technical support is provided for online identification of dominant parameters of the virtual load model; by means of the thought of continuous training online rapid identification, the convolutional neural network is applied to parameter identification of the load model, and online identification, continuous circulation and continuous training of dominant parameters of the virtual load model are achieved on a power grid big data platform.

The invention discloses an airplane icing on-line detection method based on statistical test and filtering. The airplane icing on-line detection method at least comprises the following steps that S1,flight state measurement data, engine thrust data and rudder deviation input data in the flight process are collected; S2, an icing starting moment is detected by utilizing a generalized likelihood ratio test method; S3, a disturbance signal is generated to be superposed on the rudder deviation input data, and joint state estimation and aerodynamic derivative identification influenced by icing areconducted on the flight state measurement data by utilizing an H-infinity filtering method; S4, an icing end moment is detected by utilizing the generalized likelihood ratio test method; and S5, generating and superposing of the rudder deflection disturbance signal are stopped, and joint state estimation and aerodynamic derivative identification influenced by icing continue to be conducted on themeasurement data by utilizing H-infinity filtering until the flight is finished. By combining quick icing detection and a parameter estimation method, an airplane icing detection algorithm with stronger functions is obtained.

The present invention provides a power battery parameter identification method suitable for sparse data. Based on a set membership identification algorithm, when it is difficult to determine statistical distribution features of actual system noise, it is not needed to perform assumption of the statistical distribution features of the system noise, the boundary of the system noise is only needed, asensor is configured to measure introduced errors, and round-off errors and model errors of the machine number can be taken as a form of boundary errors. The algorithm has a capacity of identification of redundant data and can ensure identification precision when the battery management system sample interval is increased, is specially suitable for a condition of sparse data, can observably improve the reliability of the power battery management system, etc.

The invention discloses a resonant frequency identification method and device for grid-connected converter equipment, an electronic device and a computer readable storage medium. The method comprisesthe following steps: acquiring a capacitor voltage signal of an LCL filter; extracting a fluctuation component signal of the capacitor voltage signal; sending the fluctuation component signals to twopreset identification controllers with the same structure and different center frequencies to obtain two output identification variable values; performing PI control based on the difference value of the two identification variable values so as to adjust the center frequency of the two preset identification controllers in real time until a closed-loop steady state is entered; and determining the average value of the center frequencies of the two preset identification controllers in the closed-loop steady state as the resonant frequency of the grid-connected converter equipment. By utilizing thefrequency characteristics of the identification results of the two preset identification controllers with different center frequencies relative to the resonant frequency and combining PI closed-loopcontrol, the resonant frequency of the grid-connected converter equipment can be effectively tracked and identified, and the identification rate, accuracy and real-time performance are effectively improved.

The invention belongs to the technical field of servo system model identification and motion control, and discloses a control-oriented permanent magnet synchronous linear motor system identification method, which comprises the steps of: S1, establishing a motor dynamic model; S2, considering an interference environment to obtain model comprehensive description; S3, identifying a positioning force; S4, identifying a friction force; S5, taking a chaotic signal as a driver input signal, and collecting motor speed and position signals; S6, after the influence of nonlinear factors is counteracted by the input signal, acquiring effective excitation, and identifying a linear link transfer function; and S7, completing servo control parameter setting in combination with an identification result. By analyzing the system working principle and the environmental influence, typical nonlinear factors are identified under a servo full-closed-loop condition, and on the basis of separating the typical nonlinear factors, linearized identification of remaining modules is achieved; the problems that a traditional linear identification method is low in identification precision, and a nonlinear identification method is unclear in control strategy and parameter design are solved.

The invention discloses a two-stage online identification method and system for a multivariable system. The method comprises the following steps: acquiring continuous historical data of each controlled variable and each controlled variable in the multivariable system; constructing a data sample by adopting a CARMA model; obtaining the optimal pure lag time from each controlled variable to each controlled variable in the multivariable system, and establishing an optimal pure lag CARMA model; obtaining a step response curve of each controlled variable when each controlled variable is subjected to step change on the basis of an optimal pure lag CARMA model; acquiring continuous change data of the step response curve as an identification sample, acquiring the optimal pure lag time from each controlled variable to any controlled variable in the single-variable system based on a second-order pure lag CARMA model, and establishing a second-order optimal pure lag CARMA model; and performing matrix combination on the second-order optimal pure lag CARMA model to obtain a multivariable system identification model. According to the method, the multivariable model is decoupled and simplified while the model precision is ensured, and prediction control can be realized.