4 results about "Least mean square algorithm" patented technology

This invention relates to a low-current harmonic direct torque control method for a dual three-phase permanent magnet synchronous motor (PMSM), belonging to the field of PMSM control technology. The method is as follows: S1: Establish mathematical models of the fundamental and harmonic subspaces of the dual three-phase PMSM under vector space decoupling, and clarify the voltage vector distribution characteristics of the two subspaces; S2: In the fundamental subspace, design a duty cycle modulation strategy based on the principle of minimizing the error vector, and an improved deadbeat flux linkage torque controller; S3: In the harmonic subspace, design an online learning harmonic canceller, using the least mean square algorithm to update the weight coefficients in real time; S4: Select the D1 vector set as the effective vector of the harmonic subspace and perform sector division; S5: Add the effective voltage vector action time of the fundamental and harmonic subspaces to generate the final switching sequence, which is output to the dual three-phase inverter to effectively suppress torque ripple and harmonic current of the dual three-phase PMSM.

The application provides a kind of mechanical arm visual servo control method and system based on self-learning disturbance observer, it is related to robot control technical field, the method comprises: obtaining real-time image data;Extract image feature vector;State estimation is carried out by extended Kalman filtering, and the filtered image feature state is obtained;Disturbance estimation value is obtained by kernel least mean square algorithm online learning;Based on the ideal model without disturbance, nominal control sequence and nominal state trajectory are generated;According to the deviation and disturbance estimation value, the final control command is generated and the robot arm is driven to move.The application combines extended Kalman filtering and kernel least mean square algorithm to construct a composite disturbance observer, realizes adaptive online learning and high-precision estimation of unknown disturbance, and forms a double-layer robust control structure combined with tube model predictive control, which significantly improves the control accuracy and robust stability of the mechanical arm visual servo system in complex disturbance environment.

The application provides a precipitation estimation method, system, device and storage medium applied in low-altitude economic services, and the method comprises the following steps: acquiring dual-polarization radar echo data and environmental parameters in a low-altitude atmosphere layer and taking the data and the parameters as inputs of a least mean square algorithm, combining a fuzzy logic control algorithm to dynamically adjust weight coefficients of an adaptive filter, using the adaptive filter with the adjusted weight to suppress noise of the dual-polarization radar echo data, and obtaining filtered radar echo data; extracting key precipitation characteristic parameters from the filtered radar echo data and calculating radar original precipitation estimation values; acquiring observation data of a ground meteorological station and performing fusion processing to generate regional precipitation reference data; using the regional precipitation reference data, determining correction coefficients by using a regularized linear regression method, correcting the radar original precipitation estimation values by using the correction coefficients, and outputting final precipitation estimation values. The application effectively improves the precipitation estimation precision in a low-altitude scene.

A first calibrator detects mismatches in the capacitor array and updates a look-up table (LUT) with calibrated weights that are copied to a positive LUT and a negative LUT, which are then adjusted for non-linear errors by a second calibrator using a least mean square (LMS) algorithm. The binary code in a successive approximation register (SAR) is complemented to generate a complement with a sign bit. When the sign bit is positive, entries from the positive LUT with complemented data bits = 1 are read and summed, a first offset is added, and the sum is normalized to obtain a corrected code. When the sign bit is negative, entries from the negative LUT with complemented data bits = 0 are read and summed, a second offset is added, and the sum is normalized to obtain a corrected code. A multivariate stochastic gradient descent generates polynomial coefficients that further correct the corrected code.