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Through the analysis of working principle of hydraulic rock drill, the calculation formula of optimal axial thrust for the propulsion system of hydraulic rock drill was obtained. At the same time, on the basis of the existing electro-hydraulic control of hydraulic rock drill, the paper constructed valve-controlled-cylinder system of compound control based on electro-hydraulic proportional pressure-reducing valve and high speed on-off valve as the core, and deduced the mathematical model formula. Finally, this work introduced the fuzzy PID control strategy, and considered optimal axial thrust as the goal to realize on-line, adaptive control to axial thrust of the propulsion system of hydraulic rock drill. Mathematical modeling and simulation study showed that fuzzy PID control method had its advantages as the fast response follow by target, high control precision, and small fluctuation, and provided theory reference for adaptive control of the propulsion system of hydraulic rock drill.

The design of a linear array of antennas with moduls as the elemental radiators is theoretically investigated. This paper first presents the Floquet modes of the array radiation with Taylor excitation distribution in terms of closed-form formulation. The design of subarray and aperture modules with using an optimized radiation is afterward described to suppress the grating lobes. Numerical results are shown to demonstrate the grating lobe suppression with sidelobe level lower than -40dB.

Misalignment angles of gyroscope unit can result in gyro drift. Thus, the precision of Inertial Navigation System (INS) is reduced. In this paper, based on profound analysis of the misalignment angle model, position method and rotating experiment method are proposed to calibrate misalignment angles of FOG Unit. In the end error calibration model of FOG Unit when misalignment angles are considered is deduced.

In this paper, a LS-SVM model based RLG's scale factor temperature data modeling method is studied. Using traditional least square linear model to modeling nonlinear ring laser gyro scale factor test data has its intrinsic shortcoming, and sometimes it is difficult to meet the application requirements. Recently, nonlinear function approximation based modeling methods such as the BP networks are introduced to modeling the temperature data. But in the BP networks will suffer the problem of overfitting and the existence of many local minima. To avoid these shortcomings the LS-SVM is used to modeling the scale factor temperature data. Base on the analysis of the test scale factor data, the scale factor test data is modeled as the function of the temperature and its increment, and LS-SVM model is employed to estimate the nonlinear function. The simulation results show that the LS-SVM model can approach scale factor data accurately, and its precision is much higher than the least square model. The mean squared deviation of LS-SVM model is smaller than 0.51times10-6("/pulse). Base on considerately designed test procedure and large numbers of experimental data, a practical temperature model can be established.

In this paper, observable degree of SINS based on singular value decomposition (SVD) is analyzed during initial alignment, and an improved method using the right singular vectors to calculate the observable degrees is proposed. Firstly, an error model is introduced for SINS on rocking base. Secondly, the model is demonstrated to satisfy the piece-wise constant system (PWCS) analysis law. Thus, the observability of system states can be simply analyzed by using the stripped observability matrix (SOM) as the substitute for the complex total observability matrix (TOM). Then the observable degrees of system states of SINS are analyzed based on the SVD of PWCS observability matrix, and the method to calculate the observable degrees is improved. Finally, computer simulation is carried out, and the results of simulation indicate that observable degree indexes are able to better predict Kalman filtering errors of system states. Therefore, the improved method discussed in this paper is effective.

This paper is dedicated to the application of artificial neural networks in building prediction models of mechanical properties of new high-speed steel, including predictions of hardness (H) and impact toughness (Ak) according to quenching and tempering temperatures (T1, T2). Multilayer backpropagation (BP) networks are created and trained using comprehensive datasets tested by the authors. And very good performances of the neural networks are achieved. The prediction values sufficiently mine the basic domain knowledge of heat treatment process of HSS. A new application field of neural network was developed by the authors.