The rotational inertial navigation system (RINS) could greatly improve navigation accuracy by rotating the inertial measurement unit with gimbals. But it will also excite and amplify the corresponding error parameters, so it is more necessary to calibrate the typical error parameters accurately. Fortunately, the self-calibration strategy could be achieved conveniently in RINS. In this paper, an innovative self-calibration strategy for error parameters of dual-axis is proposed. The calibration strategy is designed that the inner and outer gimbals rotate at different angular speeds at the same time, and the whole calibration process lasts for 12 min. The relationship between the error parameters to be calibrated and the navigation error is derived, and the principle of error separation is analyzed, which proves the rationality of the strategy. The calibration strategy is verified by simulation and experiment, and the 21 error parameters of the system can be well estimated. The 3h static navigation experiment shows that the position error is less than 0.15nmile/h(CEP) after compensating the error parameters calibrated by the self-calibration strategy, and achieved the expected accuracy of the dual-axis RINS. Finally, it is proved that the calibration strategy has the obvious advantages of short calibration time and simple process, which is of great significance to improve the navigation accuracy of dual-axis RINS.
Abstract To solve the problem of Volterra frequency‐domain kernels ( VFKs ) of nonlinear systems, which can be difficult to identify, we propose a novel non‐parametric identification method based on multitone excitation. First, we have studied the output properties of VFKs of nonlinear systems excited by the multitone signal, and derived a formula for identifying VFKs . Second, to improve the efficiency of the non‐parametric identification method, we suggest an increase in the number of tones for multitone excitation to simultaneously identify multi‐point VFKs with one excitation. We also propose an algorithm for searching the frequency base of multitone excitation. Finally, we use the interpolation method to separate every order output of VFK and extract its output frequency components, then use the derived formula to calculate the VFKs . The theoretical analysis and simulation results indicate that the non‐parametric method has a high precision and convenience of operation, improving the conventional methods, which have the defects of being unable to precisely identify VFKs and identification results are limited to three‐order VFK .
Abstract A nonlinear dynamic model of Bending-Torsional-Axial-Pendular (BTAP) has been developed for a Coaxial Reverse Closed Differential Herringbone Gear Transmission System (CRCDHGTS) with consideration of gear floating. This model takes into account factors such as gear floating backlash, tooth surface friction, gyroscopic effect, Time Varying Meshing Stiffness (TVMS), meshing damping, and dynamic meshing parameters. To investigate the impact of gear floating on the nonlinear dynamic characteristics of the system, a gear floating model was developed using the concept of gear floating. The calculations included determining gear floating backlash and TVMS with consideration for gear floating. The impact of input speed, initial backlash, gear float value, and system transmission error on the nonlinear dynamic vibration characteristics is analyzed using various diagrams including bifurcation diagram, Maximum Lyapunov Exponent (MLE), time history diagram, frequency diagram, phase diagram, and Poincaré section diagram. The research reveals that gear floating diminish the chaotic motion behavior of the system under different excitation factors, improving the system's global bifurcation characteristics. The developed BTAP coupled nonlinear dynamic model provides more accurate numerical solutions compared to models with fixed meshing parameters, making it more suitable for analyzing the system's dynamic characteristics. Analysis of the gear floating value indicates an optimal range of 0-20μm and 34-43μm for generating periodic motion, with floating values around 10-20μm showing better performance in reducing the negative effects of initial backlash and transmission error.
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