IJIRCSTJournal International Journal of Innovative Research in Computer Science and Technology I S Volume 13 Issue 2 Computer Science English March - April 2025 N N Y 2025 04 14 Computer Sciences Augmented Adaptive Control Methodologies for Advanced Orbital Launch Architectures English Y 58 66 Dragos Popescu English Y Milan Jovanovic English N Ivana Kovacic English N Hanspeter Baumann English N Zeynep Yilmaz English N Baran John English N Krzysztof Zalewski English N https://doi.org/10.55524/ijircst.2025.13.2.9 A new and more sophisticated reinterpretation of a legacy adaptive control system and an advanced deep learning–integrated estimation algorithms are applied to enable stable attitude control in sophisticated orbital launch systems. In the demanding environment of aerospace flight qualification, purely adaptive control algorithms are frequently unworkable due to analytical impossibility, ostensible non-compliance with classical stability requirements, and overwhelming modeling complexity of high-fidelity launch vehicles. Many of these adaptive approaches are inherently inappropriate for conditionally stable fusions with complex flexible-body behaviour, like the kind we often see in today’s orbital delivery systems. The method is based on, but different from, classical multiplicative forward loop gain adaptation algorithms and has hybridised architecture, involving deep learning–based nonlinear observers and feature extractors. Using these sophisticated computational intelligence algorithms, the control system is robust and more flexible, with optimal thrust vector control and attitude/attitude-rate command monitoring. This solution is in-line with the existing traditional autopilot design philosophies (phase stabilization of lateral bending modes and propellant slosh dynamics via linear filtering) and yet elegantly retains the well-known classical gain and phase margin stability measurements. Evidence based experiments from the Institute of Advanced Aerospace Systems Engineering at the University of Crescere, Italy show that the new control algorithm undoes once unstable flight conditions with extraordinary efficiency. The deep learning–augmented adaptation as viewed from frequency-domain stability dimensions enable resilience and improved performance during extreme fault conditions. Simulation findings also support that this next generation integrated adaptive-deep-learning control strategy is more reliable and robust against realistic in-flight surprises. English Adaptive Control, Deep Learning Integration, Attitude Regulation, Orbital Launch Architectures https://ijircst.org/abstract.php?article_id=1358