4results about How to "Improve position estimation accuracy" patented technology

The invention belongs to the technical field of AC motor transmission control, and relates to a permanent magnet synchronous motor dead zone compensation method based on a nonlinear flux observer. Measuring a motor induction current under a natural coordinate system, and obtaining an alpha-beta axis current through conversion; a non-linear flux observer in an alpha-beta coordinate system is used as a position observation model to obtain an actual rotor position estimation value, a rotating speed ring is formed, and sensorless control of the permanent magnet synchronous motor is achieved; constructing a q-axis voltage disturbance observer by measuring the phase voltage output by the inverter; taking the output of the q-axis disturbance observer as the input of a dead time calculation module to obtain a dead time estimation value; and according to the estimated value, calculating and obtaining a dead-zone compensation voltage, and compensating the output phase voltage of the inverter to realize dead-zone compensation. On the basis of a non-linear flux observer, a position-free control closed-loop structure is constructed, and instruction voltage is corrected, so that the influence of inverter dead time on the permanent magnet synchronous motor is reduced, and the position-free control performance is improved.

The application provides an improved sparse learning out-of-grid direction-of-arrival estimation method based on variational Bayesian. The method is characterized in that: the original data received by a hydrophone array is preprocessed, a real value transformation is used to convert a vectorized covariance matrix signal in a complex number field to a real number field, and the idea of variational sparse Bayesian learning and grid evolution is combined to make the grid evolve from an initial uniform grid to a non-uniform grid adaptively in an iteration process. The evolution process includes grid updating and grid fission. The evolved grid points are gradually close to the real source position through the alternately iterative grid updating process and grid fission process. Compared with the traditional compressed sensing method, the method has higher DOA estimation accuracy, reduces the operation complexity, optimizes the operation efficiency, improves the resolution capacity of the source, and has higher application value in actual engineering, especially in the case of few snapshots and low signal-to-noise ratio.

The present invention belongs to the field of motor control, and specifically relates to a method for controlling a switched reluctance motor without a position sensor. Based on the simplified Fourier series expression of the inductance of the switched reluctance motor and the relationship between the incremental inductance and the inductance, a simplified incremental inductance model is established; the incremental inductance of the switched reluctance motor under different phase currents at three special rotor positions is measured, so as to obtain the relationship between the incremental inductance model coefficient and the phase current, and establish a nonlinear incremental inductance model of the switched reluctance motor; according to the incremental inductance model, an analytical expression for estimating the rotor position of the switched reluctance motor from the phase incremental inductance and its coefficient is derived; when the motor is running, the phase current and bus voltage are detected, the incremental inductance of the excitation phase is estimated by the current slope difference method, the current rotor position is calculated by using the derived rotor position analytical expression, and the position sensorless operation control of the switched reluctance motor is realized according to the estimated rotor position. The control method is simple, has a small amount of calculation, is easy to implement digitally, and has a high position estimation accuracy.

This invention discloses an automatic personnel trajectory tracking method and system in hazardous areas, relating to the field of navigation technology. It involves synchronously acquiring satellite data and inertial measurement data, and using a Kalman filter for state prediction based on the inertial data. The availability of satellite data is evaluated; if available, multi-system fusion calculations are performed to calculate the observation error; if unavailable, pseudo-observation errors are generated using historical inertial data and a pre-trained error prediction model. The observation error or pseudo-observation error is then used to correct the state prediction using a Kalman filter, outputting optimal position information, and the generated trajectory is visualized in real-time on an offline map. This method can continuously correct inertial navigation drift even when satellites are unavailable, achieving continuous and reliable personnel trajectory tracking.