Low-thrust Trajectory Optimization Research Articles

Minimum-Fuel Low-Thrust Trajectory Optimization via a Direct Adaptive Evolutionary Approach

Minimum-Fuel Low-Thrust Trajectory Optimization via a Direct Adaptive Evolutionary Approach

Low-Thrust Trajectory Optimization with Averaged Dynamics Using Analytical Switching Detection

This paper presents an efficient indirect optimization method for the time- and fuel-optimal low-thrust many-revolution transfers with averaged dynamics. Compared with the previous indirect method, the proposed method involves a new set of variables and several near-optimal control laws to formulate the two-point boundary value problem based on three practical assumptions. Then, the costate transformations between two reference frames are first derived with respect to the the corresponding Eulerian angles, such that the switching function can be expressed with a six-degree polynomial function and the number of parameters used to determine the energy-optimal solutions is reduced to 6. Finally, a neural network is trained to generated the initial guesses, and the solution algorithms for solving the time- and fuel-optimal problem are formulated. Numerical simulations demonstrate that the convergence ratio of initial guesses is higher than 98%, the proposed assumptions are reasonable for guaranteeing the near optimality, and the presented method can obtain convergent solutions within tenths of a second for the Earth- and moon-centered external/interior transfers considering the shadow J2 and third-body perturbations.

Direct-to-indirect mapping for optimal low-thrust trajectories

Direct-to-indirect mapping for optimal low-thrust trajectories

On the Performance of Different Smoothing Methods for Indirect Low-Thrust Trajectory Optimization

On the Performance of Different Smoothing Methods for Indirect Low-Thrust Trajectory Optimization

A pseudoequinoctial shaping method with fourier approximation for low-thrust trajectory optimization

Shape-based methods are commonly used in solving the low-thrust trajectory optimization problem. Designing a shape trajectory by maximizing a given performance index has been widely researched in recent years. The main contribution of this paper is to present a new pseudoequinoctial shaping method with parameter optimization to solve general low-thrust spacecraft rendezvous problems. The shaping method can solve the rendezvous problem with a large inclination difference between the initial and target orbits and greatly improve the performance index of the trajectory while meeting the thrust constraints. At the same time, the shape can provide a better feasible solution for solving the low-thrust trajectory optimization problem by direct methods. Another contribution is to introduce a general adjoint estimation for indirect methods. By linearizing the equation of motion near the proposed shape-based trajectory and constructing the Hamiltonian function, the initial guess of the adjoint variables can be obtained in a closed form. The general linearization formulations applicable to all coordinate systems are presented, and three numerical examples are given, in which the first example verifies the superiority of the proposed shape-based method, the second example tests the feasibility of the shape as the initial value for a direct method, and the last example compares the convergence rates of the initial adjoint variables generated by the proposed method under different coordinate systems for an indirect method.

Duty-cycle-aware low-thrust trajectory optimization using embedded homotopy

In space mission design, an important operational constraint is planned periodic coast arcs during which various mission-critical tasks such as navigation or ground communications are performed. These constraints, typically implemented as thruster duty cycles, can be characterized as discrete coast arcs enforced periodically during intervals of deterministic thrusting. A typical approach to incorporate duty cycles is by enforcing maximum thrust levels that are less than a nominal value. This work presents an embedded homotopy indirect optimization method for a smooth enforcement of discrete duty-cycle constraints for low-thrust trajectory optimization. The complexity of enforcing such discrete constraints is two-fold: (1) a precise event-detection strategy is needed to determine the beginning of deterministic thrust arcs, which must be successively updated along the trajectory and (2) a smooth modeling of the discrete coast events occurring on the order of 10s or even 100s depending on the duration of deterministic thrust arcs. Duty cycle constraints are enforced at the level of the Hamiltonian using a Composite Smooth Control (CSC) method, producing high-resolution optimal control solutions conscious of planned duty cycle coast arcs. Numerical solutions are presented for the heliocentric transfer phases of three space missions. The results are then validated against the Evolutionary Mission Trajectory Generator (EMTG), one of NASA’s global trajectory optimization tools used for interplanetary mission design and capable of the explicit enforcement of periodic duty cycle coasting. The proposed indirect method is suitable for obtaining high-resolution trajectory solutions with realistically enforced thruster duty cycle, which could then be used as high-quality initial guesses in flight-fidelity mission planning tools. The proposed smooth modeling of discrete coast arcs can also be customized to enforce periodic and non-periodic thrust shutdown constraints.

Adaptive Estimation of Distribution Algorithms for Low-Thrust Trajectory Optimization

A direct adaptive scheme is presented as an alternative approach for minimum-fuel low-thrust trajectory design in non-coplanar orbit transfers using fitness landscape analysis (FLA). The spacecraft dynamics is modeled with respect to modified equinoctial elements by considering orbital perturbations. Taking into account the timings of thrust arcs, the discretization nodes for thrust profile, and the solution of multi-impulse orbit transfer, a constrained continuous optimization problem is formed for low-thrust orbital maneuvers. An adaptive method within the framework of the estimation of distribution algorithms (EDA) is proposed, which aims at conserving the feasibility of the solutions within the search process. Several problem identifiers for low-thrust trajectory optimization are introduced, and the complexity of the solution domain is analyzed by evaluating the landscape feature of the search space via FLA. Two adaptive operators are proposed, which control the search process based on the need for exploration and exploitation of the search domain to achieve optimal transfers. The adaptive operators are implemented in the presented EDA and several perturbed and nonperturbed orbit transfer problems are solved. The results confirm the effectiveness and reliability of the proposed approach in finding optimal low-thrust transfer trajectories.

Connecting the Equinoctial Elements and Rodrigues Parameters: A New Set of Elements

A geometric interpretation of the equinoctial elements is given with a connection to orthogonal rotations and attitude dynamics in Euclidean 3-space. An identification is made between the equinoctial elements and classic Rodrigues parameters. A new set of equinoctial elements is developed using the modified Rodrigues parameters, thereby removing the coordinate singularity for retrograde equatorial orbits present in previous versions of these elements. A low-thrust trajectory optimization problem is set up using the new elements to numerically verify convergence for the two-point boundary problem, as compared to their predecessors.

Indirect trajectory optimization via solar sailing primer vector theory: Minimum solar-angle transfers

Building upon the recent development of solar sailing primer vector theory and its successful applications, we propose a new formulation for indirect solar-sail trajectory optimization, which minimizes solar angle (cone angle between the sail normal and sunlight vectors) over a trajectory. The minimum solar-angle objective is introduced to address a typical demand in mission design: having the ability to design trajectories with fixed time-of-flight, which is especially crucial when the mission involves time-sensitive events, such as scientific observations with specific lighting conditions, gravity assists at specific epochs, to name a few. We derive an analytical optimal control law for minimum solar-angle sail transfers by applying Pontryagin’s optimality principle. Our theoretical results illuminate that a quantity termed solar sailing primer vector characterizes the optimal solar-sail control law for both minimum-time and minimum solar-angle transfers, as is the case in Lawden’s primer vector theory for conventional low-thrust transfers. We numerically demonstrate these theoretical results by applying them to asteroid rendezvous transfers for NASA’s NEA Scout mission. The numerical examples also reveal the numerical advantage of using the minimum solar-angle objective over the minimum-time one for solar-sail trajectory design because of the smoother optimal control profile. Solar sailing primer vector theory provides a tool to characterize the optimal control law independent of particular state representations or solution methods, enabling mission designers to combine it with a wealth of techniques developed for low-thrust trajectory optimization.

Regularized direct method for low–thrust trajectory optimization: Minimum–fuel transfer between cislunar periodic orbits

Low–thrust propulsion systems of high specific impulse are advantageous in the fuel efficiency. The fuel expenditure is further reduced together with optimization techniques though fuel–optimal low–thrust trajectories result in a discontinuous bang–bang control structure that can cause numerical difficulties. The present paper introduces regularized formulations into the direct multiple shooting method of minimizing fuel expenditure for low–thrust spacecraft trajectories. Instantaneous velocity changes approximating low–thrust maneuvers are expressed by regularized variables based on the Levi–Civita or Kustaanheimo–Stiefel transformation. The new formulation removes singularities due to null thrust impulses from derivatives of an objective function and constraints. The method automatically and robustly finds fuel–efficient bang–bang control structures for multi–revolutional transfers between periodic orbits in the Earth–Moon circular restricted three–body problem.

Densely rewarded reinforcement learning for robust low-thrust trajectory optimization

To overcome the time-consuming training caused by the sparse reward function in reinforcement learning, an efficient dense reward framework for robust low-thrust trajectory optimization is proposed. The dense reward functions are designed separately for the deterministic and considered uncertain scenarios, including state uncertainties, observation uncertainties and execution uncertainties. For the uncertainties, the dense reward function is designed to diminish the deviation with respect to the isochronous nominal state along the corresponding deterministic optimal trajectory at each step, rendering the reward function no longer sparse and suitable for complex multirevolution problems. In addition, a multistage reward function of the terminal constraints for the rendezvous missions is designed by incorporating some exponential acceleration terms, enabling significant improvement in training efficiency as the terminal errors become low. In addition, a dense reward function for the deterministic scenario is also proposed via the introduction of empirical forbidden zones and an exponential term. The effectiveness and efficiency of the proposed method is demonstrated in a simple Earth-Mars mission and a complex Earth-Venus multirevolution mission. The promising results verify the significant effect of the proposed method in speeding up the process of training an initial incapable agent to an ‘expert’ while guaranteeing or even improving the performance.

Orthogonal Approximation of Invariant Manifolds in the Circular Restricted Three-Body Problem

Methods to parameterize and approximate the hyperbolic invariant manifolds of particular solutions in the circular restricted three-body problem (CR3BP) are presented in this paper. Analytical representations obtained from these manifold approximations are instrumental in the synthesis of optimal trajectories for cislunar transport. A multivariate Chebyshev series is used to approximate the surfaces, thereby serving as tractable parametric representations of the complex properties of motion. It is demonstrated that the continuum of ballistic capture trajectories and their associated sensitivities on the manifold can be realized in functional form using simple algebraic operations. Two applications making use of the Chebyshev manifold approximations as a terminal constraint surface are presented. The first is a low-thrust trajectory optimization problem formulated such that the optimal free final state lying on the manifold is determined as an additional set of design parameters. The second is a guidance law designed to target the manifold in the vicinity of the nominal patch point. Each of these methods takes advantage of the Chebyshev approximations to provide additional flexibility for mission design in multibody dynamic environments. These applications offer tremendous optimism about the utility of function approximation methods in arriving at a formal representation for the invariant manifolds in the three-body problem for efficient generation of optimal trajectories.

Fuel optimization of low-thrust trajectories under thrust and coast times constraints: A novel indirect approach

This paper investigates the numerical computation of minimum-fuel low-thrust trajectories subject to bound constraints on the duration of thrust and coast arcs. First, an optimal control problem is formulated. The constraints on thrust and coast periods are written as a single discontinuous bound constraint on the control norm depending on time and on a set of free parameters considered as additional state variables. Then, the constraint is regularized and the problem is converted into an equivalent one where the norm of the control is simply upper-bounded by one. Finally, the latter problem is solved efficiently through an indirect single shooting algorithm. The great advantage of this approach is that it does not require knowing beforehand the control switching profile. Moreover, it has the ability to generate multiple suboptimal solutions in addition to the optimal one, thus offering more options for mission design. Numerical results are provided to illustrate the interest and effectiveness of the method. An electric small satellite rendezvous problem in circular orbit is considered. The fuel-optimal control of the constrained problem as well as several suboptimal controls are computed and analyzed, each of which showing a different number of thrust and coast arcs.

Approximations for Secular Variation Maxima of Classical Orbital Elements under Low Thrust

The reachability assessment of low-thrust spacecraft is of great significance for orbital transfer, because it can give a priori criteria for the challenging low-thrust trajectory design and optimization. This paper proposes an approximation method to obtain the variation maximum of each orbital element. Specifically, two steps organize the contribution of this study. First, combined with functional approximations, a set of analytical expressions for the variation maxima of orbital elements over one orbital revolution are derived. Second, the secular approximations for the variation maxima of the inclination and the right ascension of the ascending node are derived and expressed explicitly. An iterative algorithm is given to obtain the secular variation maxima of the other orbital elements the orbital elements other than the inclination and right ascension of the ascending node. Numerical simulations for approximating the variation maxima and a preliminary application in estimation of the velocity increment are given to demonstrate the efficiency and accuracy of the proposed method. Compared with the indirect method used alone for low-thrust trajectory optimization, the computation burden of the proposed method is reduced by over five orders of magnitude, and the computational accuracy is still high.

Low-thrust trajectory optimization for the solar system pony express

Low-thrust trajectory optimization for the solar system pony express

Solution space exploration of low-thrust minimum-time trajectory optimization by combining two homotopies

Solving the low-thrust trajectory optimization problem with indirect methods has two main challenges including initial guessing and global optimum searching. To overcome these difficulties, two homotopies, including the thrust-homotopy and the longitude-homotopy, are combined to investigate the solution space of the low-thrust minimum-time trajectory optimization problem. The thrust-homotopy can be interpreted as a continuation on the maximum thrust magnitude, and the longitude-homotopy is proposed via a continuation on the final cumulative true longitude. Afterwards, the structure of the solution space is revealed by simulating a transfer scenario from the geostationary transfer orbit to the geostationary orbit. A bounded and smooth solution surface, where each point represents a solution with specific thrust and final cumulative true longitude, is formed by intersecting longitude-homotopy paths and thrust-homotopy paths. Based on the solution surface, the thrust-longitude-combined homotopic approach and a new hybrid homotopy are proposed to obtain local solutions by tracking homotopy paths from a solution of an easy problem, avoiding initial guessing. Finally, the longitude-homotopy is applied to search the global minimum-time solution from an obtained local solution.

Performance Assessment of Convex Low-Thrust Trajectory Optimization Methods

Different discretization and trust-region methods are compared for the low-thrust fuel-optimal trajectory optimization problem using successive convex programming. In particular, the differential and integral formulations of the adaptive pseudospectral Legendre–Gauss–Radau method, an arbitrary-order Legendre–Gauss–Lobatto technique based on Hermite interpolation, and a first-order-hold discretization are considered. The number of discretization points and segments is varied. Moreover, two hard-trust-region methods and a soft-trust-region strategy are compared. It is briefly discussed whether these methods, if implemented on relevant hardware, would fulfill the general requirements for onboard guidance. A perturbed cubic interpolation and the propagation of the nonlinear dynamics are used to generate initial guesses of varying quality. Interplanetary transfers to a near-Earth asteroid, Venus, and asteroid Dionysus are chosen to assess the overall performance.

Convex Approach to Covariance Control with Application to Stochastic Low-Thrust Trajectory Optimization

This paper outlines a novel approach to the design of optimal space trajectories under significant uncertainty. Finite-horizon covariance control, i.e., the steering of a system from an initial probability distribution to a desired one at a prescribed time, is employed to plan an optimal nominal path along with a robust feedback controller that compensates for exogenous in-flight disturbances. A mindful convexification strategy is devised to recast the nonlinear covariance control problem as a deterministic convex optimization problem. The convexification is based on a convenient change of variables that allows for relaxing the covariance matrix discrete-time propagation into a set of semidefinite cone constraints. While featuring a larger feasible space, the relaxed problem shares the same optimal solution as the original one. The lossless property of the relaxation is analytically proven by leveraging optimal control theory and discussed on the basis of numerical experiments. Monte Carlo campaigns are carried out to validate the in-flight performance of the attained control policies.

GTOC 11: Results found at Beijing Institute of Technology and China Academy of Space Technology

This paper presents the crucial method for the results found at Beijing Institute of Technology and China Academy of Space Technology in the 11th Global Trajectory Optimization Competition (GTOC-11). To build 12 solar-power stations placed in the “Dyson Ring” orbit around the Sun, GTOC-11 focuses on the flyby trajectory design, low-thrust trajectory optimization, and target assignment problem. The mission with time and state constraints causes many difficulties in the design and optimization, which is performed in the following four steps. The first step is to design the orbital elements of the “Dyson Ring” by the grid search method. An approximate model is utilized to evaluate the minimum transfer time for each candidate at first, and the accurate solutions are obtained by the indirect method. The second step involves the Mother Ship trajectories design. A large quantity of earth-to-asteroid and asteroid-to-asteroid subsequences are generated by parallel beam search. The complete trajectories of Mother Ship are designed by connecting the subsequences using beam search. The third step involves the station assignment problem, which is required to design the asteroid transfer device activation epoch and the target station assignment. Based on the results above, the last step involves the rendezvous trajectories optimization to obtain the accurate thrust direction profile. Finally, the submitted and post-competition results are reported.

Robust Low-Thrust Trajectory Optimization Using Convex Programming and a Homotopic Approach

A robust algorithm to solve the low-thrust fuel-optimal trajectory optimization problem for interplanetary spacecraft is developed in this article. The original nonlinear optimal control problem is convexified and transformed into a parameter optimization problem using an arbitrary-order Gauss–Lobatto discretization scheme with nonlinear control interpolation. A homotopic approach that considers the energy-to-fuel smoothing path is combined with an adaptive second-order trust-region mechanism to increase performance. The overall robustness is assessed in several fuel-optimal transfers with poor initial guesses. The results show a superior performance in terms of convergence and computational time compared to standard convex programming approaches in the literature.