6 results about "Forgetting factor" patented technology

The application relates to an adaptive wavefront control method and system based on parameter identification and active disturbance rejection. The method comprises a digital architecture adopting PC data processing and FPGA control. The PC data processing module is used to perform complex calculations such as variable forgetting factor recursive least squares, is responsible for dimensionality reduction decoupling and high-precision estimation of disordered data, and utilizes the high-speed parallel processing capability of the FPGA control system to directly execute an active disturbance rejection control algorithm at the bottom layer to offset external interference. The hardware-software collaborative design effectively combines the accuracy of the complex algorithm and the real-time performance of the bottom-layer hardware, thereby significantly enhancing the anti-interference capability and response bandwidth of the system.

This application discloses an online identification method and system for pipe resistance coefficient based on an improved recursive least squares method, belonging to the technical field of heating systems. The method includes: collecting operating data of the heating pipe network; constructing a resistance coefficient identification model based on the operating data; using an improved recursive least squares method to identify the resistance coefficient online based on the identification model; evaluating the reliability of the identification results; and updating the verified resistance coefficients to the intelligent valve control system in real time. The method boasts strong real-time performance, enabling online real-time updates of the resistance coefficient to adapt to dynamic changes in the pipe network. The improved RLS algorithm enhances identification accuracy, with a measured error of <5%. No special testing conditions are required, and it does not affect the normal operation of the system. A variable forgetting factor design allows the algorithm to adapt to different operating states. It is suitable for real-time operation in embedded systems and has been verified in actual projects.

This invention discloses a server intelligent scheduling and control method, relating to the field of industrial process control technology. It addresses the control instability problems caused by thermal response lag and model time-varying behavior of the controlled object. First, the flow excitation and thermal response signals are synchronously differentially sampled. A recursive least squares method with a forgetting factor is used to identify the dynamic transfer function online, extracting the thermal response lag time constant and steady-state gain in real time. Then, based on the time constant, the stability of the step response is analyzed, the maximum input rheological rate is calculated, and a dynamic safe operating envelope domain is constructed by combining the thermal load boundary mapped by the steady-state gain. Under the constraints of this safe domain, a model predictive control objective function is established to solve for the optimal flow allocation and operating condition commands. Finally, a cooperative tracking algorithm drives the flow gateway and voltage module to achieve closed-loop control. The forgetting factor is corrected online using the state observer residual, eliminating model mismatch errors and achieving high-precision adaptive thermal safety control of the server cluster.

The application discloses a systematic interval optimization method for a forgetting factor in parameter identification of a lithium battery equivalent circuit model, and belongs to the technical field of battery management. The method comprises the following steps: firstly, obtaining initial values of parameters based on a standard recursive least square method; then, selecting a plurality of forgetting factors in a preset interval, respectively performing recursive least square identification with the forgetting factors, and obtaining parameter identification sequences; then, calculating root mean square trends of the parameter sequences with respect to the forgetting factors, and determining a forgetting factor subinterval at which each parameter keeps stable convergence; finally, obtaining a common intersection of all the subintervals as an optimized interval, and taking a middle value of the optimized interval as a recommended forgetting factor. The application overcomes blindness in empirical selection, can automatically determine an optimal forgetting factor suitable for current data, and improves convergence, stability and precision of online parameter identification.

The application discloses a battery model parameter identification method based on a forgetting recursive least square method with deviation compensation, and belongs to the field of battery model parameter identification.The method comprises the following steps: S1, a second-order equivalent circuit model is established, and model parameters to be identified are determined; S2, a load end voltage and an end current at the k moment are collected in real time; S3, a lower discharge rate is used to collect a state of charge (SOC) and an open circuit voltage (OCV) of the battery, and a relationship expression of the state of charge (SOC) and the open circuit voltage (OCV) is determined through fitting; S4, a discrete regression equation used for model parameter identification is established, and model parameters are updated on line by using an end voltage value and a current input at the k moment; S5, average weighted variances of noises in the voltage and the current are calculated; and S6, the result of the recursive least square method with a forgetting factor in S4 is updated according to the average weighted variances of the voltage and the current noises obtained in S5, and identification parameters at the k moment are obtained.The application can realize the update of a parameter vector and reduce the influence of noises on the estimation accuracy of a model.