Multicomplex number class for Matlab, with a focus on the accurate calculation of small imaginary terms for multicomplex step sensitivity calculations

A Matlab class for multicomplex numbers was developed with particular attention paid to the robust and accurate handling of smallimaginary components. This is primarily to allow the class to be used to obtainn-order derivative information using the multicomplexstep method for, amongst other applications, gradient-based optimization and optimum control problems. The algebra of multicomplexnumbers is described as is its accurate computational implementation, considering small term approximations and the identification ofprinciple values. The implementation of the method in Matlab is studied, and a class definition is constructed. This new class definitionenables Matlab to handlen-order multicomplex numbers, and perform arithmetic functions. It was found that with this method, thestep size could be arbitrarily decreased toward machine precision. Use of the method to obtain up to the 7th derivative of functions ispresented, as is timing data to demonstrate the efficiency of the class implementation

A methodology for robust optimization of low-thrust trajectories in multi-body environments

Issued as final reportThales Alenia Spac

Analytic Gradient Computation for Bounded-Impulse Trajectory Models Using Two-Sided Shooting

Many optimization methods require accurate partial derivative information in order to ensure efficient, robust, and accurate convergence. This work outlines analytic methods for computing the problem Jacobian for two different bounded-impulse spacecraft trajectory models solved using two-sided shooting. The specific two-body Keplerian propagation method used by both of these models is described. Methods for incorporating realistic operational constraints and hardware models at the preliminary stage of a trajectory design effort are also demonstrated and the analytic methods derived are tested for accuracy using automatic differentiation. A companion paper will solve several relevant problems that show the utility of employing analytic derivatives, i.e. compared to using derivatives found using finite differences

Higher order derivatives of matrix functions

We present theory for general partial derivatives of matrix functions on the form $f(A(x))$ where $A(x)$ is a matrix path of several variables ($x=(x_1,\dots,x_j)$). Building on results by Mathias [SIAM J. Matrix Anal. Appl., 17 (1996), pp. 610-620] for the first order derivative, we develop a block upper triangular form for higher order partial derivatives. This block form is used to derive conditions for existence and a generalized Dalecki\u{i}-Kre\u{i}n formula for higher order derivatives. We show that certain specializations of this formula lead to classical formulas of quantum perturbation theory. We show how our results are related to earlier results for higher order Fr\'echet derivatives. Block forms of complex step approximations are introduced and we show how those are related to evaluation of derivatives through the upper triangular form. These relations are illustrated with numerical examples.Comment: 24 pages, 2 figure

Computation of higher order Lie derivatives on the Infinity Computer

In this paper, we deal with the computation of Lie derivatives, which are required, for example, in some numerical methods for the solution of differential equations. One common way for computing them is to use symbolic computation. Computer algebra software, however, might fail if the function is complicated, and cannot be even performed if an explicit formulation of the function is not available, but we have only an algorithm for its computation. An alternative way to address the problem is to use automatic differentiation. In this case, we only need the implementation of the algorithm that evaluates the function in terms of its analytic expression in a programming language, but we cannot use this if we have only a compiled version of the function. In this paper, we present a novel approach for calculating the Lie derivative of a function, even in the case where its analytical expression is not available, that is based on the Infinity Computer arithmetic. A comparison with symbolic and automatic differentiation shows the potentiality of the proposed technique

Algèbres hypercomplexes pour le Calcul

Dans les domaines mathématique ou applicatif, la multiplication de nombres possède un rôle clef pour le Calcul. En Science et en Ingénierie, la nonlinéarité offre de grands défis de modélisation mais aussi de résolution. Notre approche vise, via la multiplication, l'étude de certains phénomènes non linéaires que l'on retrouve fréquemment dans le domaine de la Science et de l'Industrie. Pour cela, nous étudions dans cette thèse la multiplication de nombres multidimensionnels, associée à des structures algébriques en dimension finie appelées algèbres hypercomplexes. Nous utilisons la multiplication comme lien entre les divisions apparentes des différents domaines théorique et pratique que nous abordons par une approche transdisciplinaire. Nous effectuons une analyse comparative entre les algèbres hypercomplexes et les principaux outils de Calcul, approche qui n’est pas développée dans la littérature existante. Nous présentons une synthèse des applications existantes (par ex. robotique, modélisation 3D, électromagnétisme) et des principaux avantages des algèbres hypercomplexes, pour la Science et l’Ingénierie. A partir des conséquences de l’utilisation des structures alternatives (autres que réelles ou complexes), nous proposons une extension nouvelle de la théorie spectrale présentée sous le nom de couplage spectral. Grâce aux algèbres hypercomplexes et à la théorie du couplage spectral, nous présentons des applications inédites à la mécanique et à la chimie ainsi que des perspectives pour le domaine du calcul quantique. Pour les domaines d’applications présentés, existants ou inédits, nous étudions les aspects de modélisation théorique et aussi d’analyse numérique. Nous montrons que suivant les cas d'étude, les aspects numériques avantageux découlent d'un choix judicieux des modèles et des algèbres hypercomplexes associées. Ces avantages sont principalement dus à la manière de définir la multiplication dans les algèbres concernées. Dans les domaines applicatifs abordés, une grande partie des modèles théoriques et numériques repose actuellement sur l’utilisation des nombres réels ou complexes ainsi que sur l’algèbre linéaire. Nous montrons dans cette thèse que les algèbres hypercomplexes sont complémentaires des outils algébriques actuellement utilisés et possèdent un vaste potentiel théorique et pratique, grandement sous-exploité pour le Calcul

Low Thrust Trajectory Optimization in Cislunar and Translunar Space

Low-thrust propulsion technologies such as electric propulsion and solar sails are key to enabling many space missions which would be impractical with chemical propulsion. With exhaust velocities 10x higher than chemical rockets, electric propulsion systems can deliver a spacecraft to its target state for a fraction of the fuel. Due to the low thrust, the control must remain active for weeks or even years. When three-body dynamics are considered, the change in dynamics over the course of a trajectory can be extreme. This greatly complicates low-thrust mission design and navigation in cislunar and translunar space, making it an area of active research. Deterministic strategies for trajectory design and optimization rely on linearizing the problem and solving a series of linearized problems. In regimes with simple or slowly-varying dynamics, the linearization holds “true enough”, and we can easily arrive at a solution. However, three-body environments readily provide real cases where the linearization for all but the most carefully-chosen problem descriptions break down. This thesis presents a few modifications to existing algorithms to improve convergence. This thesis then uses this fast, robust method for trajectory optimization to generate training samples for a machine learning approach to optimal trajectory correction. We begin with one optimal low-thrust transfer. Then, we optimize thousands of transfers in the neighborhood of the nominal transfer. These transfers are described in the language of indirect optimal control, with the optimal control given as a function of Lawden’s primer vector. We see that for a slightly different initial condition, the states and the costates both follow a slightly different trajectory to the target. A feedforward artificial neural network is trained to map the difference in states to the difference in costates, with a high degree of accuracy. Finally, we explore a potential application of this neural network: spacecraft that can navigate themselves autonomously in the presence of errors. We propose this as a method for future spacecraft that can optimally correct their trajectories without ground contacts. We demonstrate neural network navigation in two simplified dynamical environments: two-body heliocentric gravity, and the Earth-Moon circular restricted three body problem