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 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 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, including: S1, establishing a dynamic model of the motor; S2, considering the interference environment, and obtaining a comprehensive description of the model; S3 , identification of positioning force; S4, identification of frictional force; S5, use chaotic signal as the input signal of the driver, and collect the motor speed and position signal; S6, obtain effective excitation after offsetting the influence of nonlinear factors on the input signal, and identify the linear link transfer function; S7. Combining with the identification results, the tuning of the servo control parameters is completed. The invention analyzes the working principle of the system and the influence of the environment, completes the identification of typical nonlinear factors under the condition of full servo closed loop, realizes the linear identification of the remaining modules on the basis of separating typical nonlinear factors, and solves the problem of traditional linear identification The method identification accuracy is low, and the non-linear identification method is not clear about the control strategy and parameter design.

The invention discloses an online detection method for aircraft icing based on statistical inspection and filtering. The online detection method for aircraft icing at least includes the following steps: S1: collecting flight state measurement data, engine thrust data and rudder deviation during flight Input data; S2: Use the generalized likelihood ratio test method to detect the beginning of icing; S3: Generate a disturbance signal and superimpose it on the rudder deviation input data, and use H ∞ The filtering method performs joint state estimation and icing-affected aerodynamic derivative identification on the flight state measurement data; S4: uses the generalized likelihood ratio test method to detect the end time of icing; S5: stops generating and superimposing the rudder deviation disturbance signal, and continues to use the H ∞ Filtering performs joint state estimation and identification of aerodynamic derivatives affected by icing on the measured data until the end of the flight. By combining the rapid detection of icing with the parameter estimation method, a more powerful aircraft icing detection algorithm is obtained.