Multidisciplinary Design Optimization for Space Applications

Multidisciplinary Design Optimization (MDO) has been increasingly studied in aerospace engineering with the main purpose of reducing monetary and schedule costs. The traditional design approach of optimizing each discipline separately and manually iterating to achieve good solutions is substituted by exploiting the interactions between the disciplines and concurrently optimizing every subsystem. The target of the research was the development of a flexible software suite capable of concurrently optimizing the design of a rocket propellant launch vehicle for multiple objectives. The possibility of combining the advantages of global and local searches have been exploited in both the MDO architecture and in the selected and self developed optimization methodologies. Those have been compared according to computational efficiency and performance criteria. Results have been critically analyzed to identify the most suitable optimization approach for the targeted MDO problem

From engineering models to knowledge graph : delivering new insights into models

Essential information on the early stages of a mission design is contained in Engineering Models. Yet, these models are often uneasy to visualise, query, let alone compare. This study demonstrates how Knowledge Graphs can overcome these data silos, interconnect information, provide a big-picture perspective, and infer new knowledge that would have remained hidden otherwise. Following the migration of CubeSats Engineering Models to a Knowledge Graph, two case studies are explored. The first case study illustrates how graph inference can derive implicit knowledge from existing explicit concepts. In the second case study, a Natural Language Processing layer is adjoined to the Knowledge Graph to enhances the analysis of textual content. The Natural Language Processing layer relies on the document embedding method doc2v

An intrusive approach to uncertainty propagation in orbital mechanics based on Tchebycheff polynomial algebra

The paper presents an intrusive approach to propagate uncertainty in orbital mechanics. The approach is based on an expansion of the uncertain quantities in Tchebicheff series and a propagation through the dynamics using a generalised polynomial algebra. Tchebicheff series expansions offer a fast uniform convergence with relaxed continuity and smothness requirements. The paper details the proposed approach and illustrates its applicability through a set of test cases considering both parameter and model uncertainties. This novel intrusive technique is then comapred against its non-intrusive counterpart in terms of approximation accuracy and computational cost

Trajectory optimization of a reusable launch vehicle

A comparison between different direct trajectory optimization methods for a Single Stage to Orbit Reusable Launch Vehicle is carried out and presented. Collocation and multiple-shooting approaches are compared in terms of accuracy, scalability, computational costs, and sensitivity to irregularities of models. The ascent trajectory optimization of the FESTIP-FSS5 Reusable Launch Vehicle is considered

A novel update mechanism for Q-Networks based on extreme learning machines

Reinforcement learning is a popular machine learning paradigm which can find near optimal solutions to complex problems. Most often, these procedures involve function approximation using neural networks with gradient based updates to optimise weights for the problem being considered. While this common approach generally works well, there are other update mechanisms which are largely unexplored in reinforcement learning. One such mechanism is Extreme Learning Machines. These were initially proposed to drastically improve the training speed of neural networks and have since seen many applications. Here we attempt to apply extreme learning machines to a reinforcement learning problem in the same manner as gradient based updates. This new algorithm is called Extreme Q-Learning Machine (EQLM). We compare its performance to a typical Q-Network on the cart-pole task - a benchmark reinforcement learning problem - and show EQLM has similar long-term learning performance to a Q-Network

Towards intelligent control via genetic programming

In this paper an initial approach to Intelligent Control (IC) using Genetic Programming (GP) for access to space applications is presented. GP can be employed successfully to design a controller even for complex systems, where classical controllers fail because of the high nonlinearity of the systems. The main property of GP, that is its ability to autonomously create explicit mathematical equations starting from a very poor knowledge of the considered plant, or just data, can be exploited for a vast range of applications. Here, GP has been used to design the control law in an Intelligent Control framework for a modified version of the Goddard Rocket problem in 3 different failure scenarios, where the approach to IC consists in an online re-evaluation of the control law using GP when a considerably big change in the environment or in the plant happens. The presented results are then used to highlight the potential benefits of the method, as well as aspects that will need further developments

Set propagation in dynamical systems with generalised polynomial algebra and its computational complexity

This paper presents an approach to propagate sets of initial conditions and model parameters through dynamical systems. It is assumed that the dynamics is dependent on a number of model parameters and that the state of the system evolves from some initial conditions. Both model parameters and initial conditions vary within a set Ω. The paper presents an approach to approximate the set Ω with a polynomial expansion and to propagate, under some regularity assumptions, the polynomial representation through the dynamical system. The approach is based on a generalised polynomial algebra that replaces algebraic operators between real numbers with operators between polynomials. The paper first introduces the concept of generalised polynomial algebra and its use to propagate sets through dynamical systems. Then it analyses, both theoretically and experimentally, its time complexity and compares it against the time complexity of a non-intrusive counterpart. Finally, the paper provides an empirical convergence analysis on two illustrative examples of linear and non-linear dynamical systems

Collision avoidance as a robust reachability problem under model uncertainty

The paper presents an approach to the design of an optimal collision avoidance maneuver under model uncertainty. The dynamical model is assumed to be only partially known and the missing components are modeled with a polynomial expansion whose coefficients are recovered from sparse observations. The resulting optimal control problem is then translated into a robust reachability problem in which a controlled object has to avoid the region of possible collisions, in a given time, with a given target. The paper will present a solution for a circular orbit in the case in which the reachable set is given by the level set of an artificial potential function

A variance-based estimation of the resilience indices in the preliminary design optimisation of engineering systems under epistemic uncertainty

This paper presents novel heuristics for the fast conservative approximation of resilience indices in the preliminary design optimisation of engineering systems under uncertainty. Since the uncertain in the early phases of the design process is mainly of an epistemic nature, Dempster–Shafer theory of evidence is proposed as the reasoning framework. The heuristics proposed in this paper are used to partition the uncertainty space in a collection of subsets that is smaller than the full set of focal elements but still provides a good approximation of Belief and Plausibility. Under suitable assumptions, this methodology renders the approximation of the Belief and Plausibility curves cost-effective for large-scale evidence-based models. Its application to the preliminary-design sizing of a small spacecraft solar array under epistemic uncertainty will be demonstrated

Optimisation of ascent and descent trajectories for lifting body space access vehicles

One of the forerunners for future space access vehicles is the spaceplane, a lifting body vehicle capable of powered horizontal take-off and landing. Employing strategies from multidisciplinary design optimisation, this paper outlines the approaches and models used towards developing an integrated design platform to assess the preliminary design and performance of a spaceplane. The trajectory and control is optimised, based on different mission objectives and constraints, for the ascent and descent mission segments of a conceptual single stage to orbit vehicle, to a circular low Earth orbits from different take-off and landing sites. A modular approach is employed, dividing the mission into phases based on model discontinuities, changes in the operating environment or vehicle operation, mission objectives or constraints. The problem is reformulated by direct transcription using multiple shooting into a constrained NLP problem, and solved by a combination of genetic algorithms for a global search, and SQP plus interior point methods for local refinement with hard constraints