Uniform steady-state and small-signal circuit models for pulsewidth-modulated dc-dc converters operated in discontinuous conduction mode are presented. The models are developed using a new approach that accurately accounts for nonlinearity of current waveform due to component parasitic resistances. The uniform model structure for a set of dc-dc converters is transformed by substituting the practical inductor with an equivalent inductor in series connection with the output capacitor. The resulting models are used to derive the uniform expressions of voltage transfer ratio, efficiency, and various transfer functions. The proposed models can be considered as an alternative to the previously established techniques, and are shown to be fairly accurate in steady-state and small-signal representations through detailed simulations and experimental tests.

Permanent magnet synchronous motor (PMSM) AC servo system has the characteristics of uncertainty, time-varying, nonlinear and fractional order. Applying the traditional control method is difficult to achieve the desired control effect. The fuzzy control has strong adaptability to the parameter change, nonlinearity and imprecise model of the controlled object. The simulation model of permanent magnet synchronous motor three closed-loop systems is established. The control principle and the realization of space vector pulse width modulation (SVPWM) are studied by using a vector control strategy. Due to the fractional-order characteristic of the motor, a fuzzy logic algorithm is used to realize the parameter self-tuning of the fractional-order proportional integral differential (PID) controller. The controller is selected as the position regulator of the servo motor. It combines the precision of fractional-order PID controller with the adaptability of fuzzy control and adds feed-forward to improve the response speed. The path tracking experiments on several different paths are carried out, and the results show that the control method is effective and can meet the trajectory tracking requirements of servo control. Finally, the speed and position tracking test of the PMSM AC servo system is carried out on the test verification platform, which verifies the effectiveness of the control algorithm.

The collaborative design of the multi-module satellite component (equipment) assignment and layout is the key aspect of the overall satellite design, and the two parts are closely related. In the past, satellite module component layout optimization usually adopted fixed component assignments, which remained constant in the layout optimization stage. If the components were improperly distributed in these modules, it would seriously affect the layout optimization. To overcome this disadvantage, a collaborative design method for the component assignment and layout design is presented for the multi-module (or multi-bearing plate) satellite component layout problem, based on a multi-agent system. First, the component assignment agent adopted a multi-objective optimization method (the non-dominated sorting genetic algorithm II, NSGA-II) to obtain the approximate Pareto solution set of the satellite component assignment scheme. Second, it adopted a fuzzy multi-objective decision method to select a high-quality component assignment scheme from the approximate Pareto solution set. Third, the layout agent employed a dual system co-evolutionary method for the layout optimization design. In the process of the layout optimization, the layout result is fed back to the component assignment design, and the component assignment is adjusted according to the result of the layout optimization. Thus, the above process is continually iterated to achieve the optimal collaborative design of the component assignment and the layout. The proposed method is applied to a simplified multi-module satellite component assignment and layout optimization problem and aims to provide a reference and technical support for other similar multi-module equipment assignment and layout optimization problems.

To measure the moisture content of granules during the industrial fluidized bed drying (FBD) process, a semi-supervised calibration model is proposed for using the near-infrared (NIR) spectroscopy to conduct in situ measurement. To solve the dilemma of lacking sufficiently labeled samples as often encountered in various batch FBD processes, a semi-supervised variational inference partial least-squares (PLS) method is proposed to use up all the labeled and unlabeled spectra measured for calibration model building. Moreover, an adaptive Gamma distribution-based sparsing algorithm is established to select the spectral variables for modeling, to overcome the high-dimensional input collinearity. Owing to the use of a variational inference learning approach, the constructed model can ensure not only prediction accuracy but also credibility. A numerical example and experiments on batch FBD processes of silica gel granules are shown to demonstrate the effectiveness and merit of the proposed method.