9 results about "Large-signal model" patented technology

The application discloses a kind of LADRC control methods based on DAB converter, comprising: with the unit value of transmission power as control quantity, LADRC control is carried out through DAB converter linear large signal model;Wherein, the DAB converter linear large signal model is based on single-sided asymmetric duty cycle modulation method, and the control of output voltage is realized by directly controlling quantized transmission power.The control method proposed in the application improves the traditional linear active disturbance rejection controller under the premise of guaranteeing system performance, and optimizes the parameter setting process.Finally, it is verified through a simulation platform, and the overall efficiency in the full power range is significantly improved.The control proposed has better robustness than the traditional PI control, and greatly simplifies the problem of too many parameters and difficult to value in the design of traditional ADRC controller.

The application discloses a kind of GaN HEMT equivalent circuit model parameter self-adapting extraction method and system, comprising: extracting parasitic parameter value by small signal equivalent circuit parasitic parameter extraction method, establishes initial population;Stripping parasitic parameter in initial population obtains each bias eigenvalue by network analysis, and simulation equivalent circuit S parameter data is obtained by calculation;Judgment is calculated according to S parameter data individual fitness;New individual is generated to replace singular individual in population until population is non-singular;According to the search range of adjustment algorithm of non-singular population;According to the individual selection, crossover, variation rule set to the individual in initial population is carried out genetic calculation, and new genetic individual is obtained;Whether the preset target is satisfied is judged, and the element parameter corresponding to optimal individual is output as the final parameter of small signal model;I-V model is optimized by genetic algorithm, and the weight matrix of fitness function is updated according to model error, and optimization is carried out again to meet the preset target, and the small signal model parameter is combined to establish large signal equivalent circuit model.The application greatly improves the modeling speed of small signal model and the accuracy of large signal model without sacrificing accuracy, which provides key technical support for reliable design of high-power, high-frequency microwave circuit.

The application relates to a DNN-based large-signal model modeling method, system, device and storage medium, and relates to the technical field of integrated circuits. The method is based on a GaN microwave device including a GaN transistor. The method comprises the following steps: performing large-signal testing on the GaN transistor to obtain a traveling wave characteristic; processing input layer characteristics and output layer characteristics in the traveling wave characteristic to obtain input layer data and output layer data; extracting real part and imaginary part data and training a high-power area deep neural network model by using a DNN; extracting amplitude and phase data and training a low-power area deep neural network model by using a DNN; training the traveling wave characteristic according to the high-power area deep neural network model and the low-power area deep neural network model to obtain a trained traveling wave characteristic, and converting the trained traveling wave characteristic into a voltage-current characteristic; and constructing a large-signal behavior model according to the voltage-current characteristic. The application has the technical effect of improving the accuracy of a large-signal modeling model of a GaN microwave device.

This invention discloses a method and system for constructing an equivalent model of GaNHEMT based on a nonlinear scaling rule. This method addresses the insufficient scaling accuracy of traditional linear scaling techniques by introducing an exponential term to modify the GaNHEMT scaling rule, thus constructing a large-signal model of GaNHEMT based on the nonlinear scaling rule. The impact of the nonlinear scaling rule on the S-parameters of GaNHEMT devices is analyzed. The accuracy differences between the nonlinear and linear scaling rules are compared, ultimately finding that the nonlinear scaling rule significantly improves the fitting accuracy of the S-parameters compared to the linear scaling rule. Without increasing model complexity, this method improves the modeling efficiency and accuracy of GaNHEMT devices. This method is applicable to modeling equivalent models of GaNHEMTs with different gate widths within the same batch.

The application relates to the technical field of wireless power transmission, and particularly discloses a WPT system modeling method based on data driving, which comprises the following steps: firstly, determining the circuit structure and system parameters of the WPT system, establishing a differential equation, then constructing a GSSA model (large signal model) based on the system differential equation, and obtaining a linear small signal model of the system by adding disturbance, further establishing a Hammerstein model based on the large signal model and the small signal model through a data driving modeling method, and performing order reduction to obtain a second-order Hammerstein model, finally constructing an optimization problem based on the second-order Hammerstein model, and finally solving the optimization problem by adopting a method combining the least square method and Newton iteration search, so that the model offset parameters and system communication delay of the WPT system can be estimated more accurately.

The application discloses a bidirectional CLLLc state observation control method based on extended describing function modeling, belongs to the field of power electronic technology application, and is based on solving the wide voltage output of the bidirectional CLLLc converter and the output voltage stability problem under different load working conditions, and through the establishment of a bidirectional CLLLc harmonic equivalent circuit, the establishment of a large signal model by using an extended describing function, the approximate linearization of the model by using partial derivatives and steady-state solutions, the creation of a bidirectional CLLLc converter state observation equation, and the design of a controller, accurate control of the bidirectional CLLLc converter is realized. The state observation matrix of the application is related to the circuit topology and system control parameters, and therefore the bidirectional CLLLc converter state observation control method based on the extended describing function modeling is suitable for DC / DC conversion under various application scenarios, and under different degrees of load working conditions, the effect of stable output voltage is achieved.

The application provides a predefined finite time control method and system for an island micro-grid, and the method comprises the following steps: establishing a large signal model of a distributed power generation unit (DG) based on an inverter; obtaining a second-order feedback system of a single distributed power generation unit (DG) through input-output feedback linearization; designing a disturbance observer for estimating nonlinear uncertain items of the feedback system; designing a distributed virtual controller based on predefined finite time; and constructing an overall Lyapunov function to verify that all signals are bounded in the distributed power generation units (DG) realized by all designed backstepping control strategies, and the synchronization error converges to a predefined finite time function within a predefined finite time. The application solves the technical problems that the inertia of the DG is small, the IMG is affected by uncertain and intermittent environmental changes, the transient performance index is poor, and the secondary voltage control effect of the IMG is restricted.

The invention discloses a step-by-step field effect transistor modeling method and system based on neural network space mapping, and belongs to the technical field of microwave circuit and device modeling. Three types of core models are constructed in stages: a static neural network space mapping model and a dynamic neural network space mapping model are established as intermediate modeling; constructing a dynamic neural network model containing more hidden layer neurons as a final target model; and the direct current data, the small signal S parameter data and the large signal harmonic balance data are respectively adopted to carry out targeted training on the model in each stage, and finally a high-precision transistor large signal model is obtained. A constraint condition formula is introduced in the training process, and it is ensured that training in the subsequent stage does not change the training result achieved in the early stage. According to the method, one-stop modeling of the transistor large signal model of the field effect transistor is realized, the modeling efficiency and the model precision are effectively improved, and an efficient and reliable solution is provided for transistor modeling in microwave circuit design.

The application discloses a kind of GaN HEMT physical base large signal model parameter extraction methods for switch, belong to semiconductor device modeling and design field, including transistor current model parameter extraction;Transistor intrinsic gate-source and gate-drain capacitance calculation;Edge capacitance model parameter extraction.The application is accurately extracted by the mode of considering the equivalent gate voltage model parameter of source-drain interaction effect under reverse bias condition, accurately characterizes the current characteristics under the condition of drain reverse bias;Using the parameter extraction method of edge capacitance bias-dependent model, the accurate characterization of deep pinch-off region nonlinear capacitance is realized, and then the precise modeling of small signal and large signal characteristics under the off state of switch device is successfully realized.