Abstract. The performance of an HTPB/N2O hybrid motor was experimentally investigated. A hybrid motor was designed and manufactured in a laboratory with the purpose of studying the e ects of various parameters on the motor's performance, including fuel regression rate and speci c impulse. A series of tests were conducted to nd a correlation between the fuel regression rate and the oxidizer's mass ux. The e ects of chamber's pressure on the regression rate as well as other performance parameters were investigated. While the burning rate did not change dramatically, both the e ciency and ISP of the motor increased. The local fuel regression rate and the fuel port were also calculated. In addition, instantaneous regression rate was calculated using a special technique.
In order to save cost and time, sounding rockets are effectively used to develop space technologies. In a biological payload that is under the study on this investigation, reentry rate regulation is one of the critical issues to be solved. Because of non-linear time-varying dynamics of these payloads and presence of high aerodynamic disturbances during reentry, choosing an appropriate stability and control mechanism can be an engineering challenge. Having a lot of benefits, nowadays, moving-mass actuation systems are used in a variety of aerospace applications. As an innovative approach, a moving-mass system is designed and analysed to regulate the payload body rates during reentry phase. Simulation results indicate significant performance of suggested application for moving-mass control systems.
The main purpose of the paper is to present sophisticated means to implement Q-guidance scheme in satellite injection missions. Optimality and simplicity of Q-guidance approach makes it appropriate for practical settings of a wide range of launch systems. Calculation of required velocity as a fundamental and inherent concept of Q-guidance implementation for orbital injection missions has been proposed through two methods based on two different concepts; single impulse and bi-impulse orbital transfer. Implementation and comparison of the schemes on a test problem are invoked. Stochastic simulation and performance analysis in presence of uncertainties are also investigated. Accuracy and robustness of the algorithms are discussed accordingly.
An adaptive intelligent control strategy based on a brain emotional learning model is investigated in the application of the rate regulation of suborbital reentry payloads. Because of nonlinear time-varying dynamics of these payloads, choosing an appropriate control mechanism and stability strategy can be an engineering challenge. Thus in a new approach, a moving mass control system in conjunction with brain emotional learning-based intelligent control is used to fulfill payload de-tumbling. The contribution of brain emotional learning-based intelligent control in handling the nonlinear time-varying dynamics is shown by comparison with results obtained from a linear proportional–integral controller. The results demonstrate excellent performance of brain emotional learning-based intelligent control in learning dynamic couplings and improvement of behavior without any considerable control system complexity.
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