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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.

This paper addresses the attitude path design of a remote sensing satellite in various imaging modes. The novel path design algorithm is based on local polynomial regression, which produces a smooth attitude path by receiving the size and timing of maneuvers in each axis according to the desired imaging mode. This algorithm is described for stereo and snapshot modes in an imaging operation. The adaptive robust tracking control (ARTC) law is designed using quaternion algebra to perform the required maneuvers in an attitude path. The ARTC structure includes a sliding mode strategy, a projection-based adaptive model compensation, and a linear feedback term. Suitable conditions for imaging in each mode and the time of taking the image are determined by defining and evaluating the attitude control indices. These indices are defined by the half-cone error along the payload line of sight and relative performance error as a jitter with a 3-sigma confidence level. Despite the challenges in the path design, such as smoothness, system agility, and finite time realization of indices, in a conventional stereo imaging mode, taking an image in the nadir-pointing attitude is neglected. As a result, our work provides suitable conditions for taking the image in this attitude with an interval of 1.2 s. Finally, numerical simulations and verification are performed using multidisciplinary simulation, indicating the effectiveness of the proposed algorithm and model-based ARTC law.

PurposeThe purpose of this paper is to identify linear model parameters of launch vehicles based on the actual flight test data. To compare the estimated parameters with the ones obtained by two other approaches: identification based on the recorded data from six‐degree‐freedom simulation of motion and linearization of the equations of motion via small‐disturbance theory as an analytical method.Design/methodology/approachAs the vehicle contains all the key issues in system identification such as time‐varying, unstable, nonlinear, and closed‐loop dynamics, Kalman filter method under the autoregressive with exogenous input model structure is used as a powerful method to estimate the dynamic parameters.FindingsSimulation results demonstrate that the linear model parameters used in the vehicle design and analysis should be validated by flight test data to accurate the vehicle dynamic model as more as possible.Practical implicationsOne of the most important usages of a linear model of aerospace vehicles is to design their controller. Another application of the algorithm presented in this paper is to estimate online dynamic parameters of the vehicle when they are required for the operation of the control system.Originality/valueBeing strongly affected by vehicle dynamic characteristics, linear model parameters of launch vehicles play important part in their design and analysis.