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Stage separation dynamics modeling is a critical capability of future launchers preparatory studies. The development of stage separation frameworks integrable in end-to-end launch vehicle trajectory simulations have been presented in the relevant literature but none profiting from the object-oriented and equation-based acausal modeling properties of MODELICA. The objective of this paper is therefore to present such an approach to this problematic. Based on the Constraint Force Equation (CFE) methodology, two case studies to evaluate the proposed approach are considered. Results demonstrate that the approach corresponds very well with the physics behind separation. In addition, we found easiness of implementation of the method within a single environment such as DYMOLA, demonstrating the benefits of an integrated approach.

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This paper presents a control allocation solution for the technology demonstrator missionReFEx, which focuses on a vertical takeoff and horizontal landing strategy with autonomous navigation, online guidance, and controlled flight throughout the mission. The trajectory for the demonstration flight is aimed as one for a winged launch vehicle first stage: maintaining stability and control of the vehicle while reaching a predefined target. During the atmospheric phase the vehicle is stabilized by using an active aerodynamic control system which transforms inputs from the guidance and navigation systems into control commands for the individual actuators. In that sense, the control allocation subsystem translates commanded moments into commanded aerodynamic surface deflections. Due to the effect of modeling uncertainties, navigation errors, and underactuated regions, this subsystem needs to be robustified. The algorithm proposed in this paper addresses this challenge via a combination of the deflections required to trim the vehicle together with delta-deflections that aim at converging iteratively to the commanded moments. The combination of these two contributions is able to respond fast to state changes, compensate for modeling uncertainties and navigation errors, and provide a safe mode for the underactuated regions. The performance of the system is studied using a high-fidelity simulator. Nomenclature𝛼, 𝛽, 𝜇 = angle of attack, angle of sideslip, bank angle 𝜔 = angular rate of the body frame w.r.t the inertial frame q = dynamic pressure 𝐼 = moment of inertia 𝐶 𝑙 , 𝐶 𝑚 , 𝐶 𝑛 = aerodynamic coefficients for roll, pitch and yaw moments 𝐶 = aerodynamic coefficients vector, [𝐶 𝑙 , 𝐶 𝑚 , 𝐶 𝑛 ] 𝑇 Ma = Mach 𝜂 𝑆 , 𝜂 𝐴 = symmetric and asymmetric deflection of the canards 𝜁 = rudder deflection 𝛿 = deflection vector, [𝜂 𝑆 , 𝜂 𝐴 , 𝜁] 𝑇 𝑓 = generic function

This paper presents a sampled-data form of the recently reformulated incremental nonlinear dynamic inversion (INDI) applied for robust spacecraft attitude control. INDI is a combined model-and sensor-based approach mostly applied for attitude control that only requires an accurate control effectiveness model and measurements of the state and some of its derivatives. This results in a reduced dependency on exact knowledge of system dynamics which is known as a major disadvantage of model-based nonlinear dynamic inversion controllers. However, most of the INDI derivations proposed in the literature assume a very high sampling rate of the system and its controller while also not explicitly considering the available sampling time of the digital control computer. Neglecting the sampling time and its effect in the controller derivations can lead to stability and performance issues of the resulting closed-loop nonlinear system. Therefore, our objective is to bridge this gap between continuous-time, highly sampled INDI formulations and their discrete, lowly sampled counterparts in the context of spacecraft attitude control where low sampling rates are common. Our sampled-data reformulation allows explicit consideration of the sampling time via an approximate sampled-data model in normal form widely known in the literature. The resulting sampled-data INDI control is still robust up to a certain sampling time since it remains only sensitive to parametric uncertainties. Simulation experiments for this particular problem demonstrate the bridge considered between INDI formulations which allows for low sampling control rates.