Ya-Zhong Luo

Optimization of Multiple-Impulse, Multiple-Revolution, Rendezvous- Phasing Maneuvers

A new hybrid optimization approach is proposed for the design of a rendezvous-phasing strategy with combined maneuvers, which is normally considered as a complex multiple-impulse, multiple-revolution, nonlinear rendezvous problem. In this approach, a feasible iteration optimization model is first formulated using a multiple-revolution Lambert algorithm, and a parallel simulated annealing algorithm is employed to locate the unperturbed solution. Subsequently, an infeasible iteration optimization model accounting for trajectory perturbations is formulated, and a sequential quadratic programming algorithm is used to obtain the perturbed solution, with the unperturbed solution as an initial reference solution. The global convergence ability of the proposed approach is verified by solving a classical same-circle rendezvous problem. Two different solutions satisfying Lawdens necessary optimality conditions are located and one solution outperforms an optimal solution previously reported. The proposed approach is further evaluated in a practical two-day rendezvous-phasing mission with different initial conditions. It is shown that this approach is effective and efficient and the combined maneuvers can save propellant at a range of 4-35% when compared with the special-point maneuvers.

Hybrid Approach to Optimize a Rendezvous Phasing Strategy

DOI: 10.2514/1.20232 The design of a rendezvous phasing strategy can be formulated as a mixed integer nonlinear programming problem. A new hybrid approach combining a genetic algorithm with Newton’s method is proposed for solving this problem. An integer-coded genetic algorithm is used to handle the discrete design variables, whereas Newton’s method is applied to handle the continuous design variables. Three improvements are imposed on the hybrid approach to make it more efficient and robust. The first improvement is not to impose the exact analysis on the explicitly constraint-violated design variables. The second is to use a memory database to record the previously completed analysis, and the third is to renew the initial guess to Newton’s method by the nearest one in the memory database. A two-day rendezvous phasing problem is used as an example. Results show that our hybrid approach is effective, efficient, and can find multiple solutions in a single run.

Multispacecraft Refueling Optimization Considering the J2 Perturbation and Window Constraints

The optimization of a near-circular low-Earth-orbit multispacecraft refueling problem is studied. The refueling sequence, service time, and orbital transfer time are used as design variables, whereas the mean mission completion time and mean propellant consumed by orbital maneuvers are used as design objectives. The J2 term of the Earth’s nonspherical gravity perturbation and the constraints of rendezvous time windows are taken into account. A hybrid-encoding genetic algorithm, which uses normal fitness assignment to find the minimum mean propellant-cost solution and fitness assignment based on the concept of Pareto-optimality to find multi-objective optimal solutions, is presented. The proposed approach is demonstrated for a typical multispacecraft refueling problem. The results show that the proposed approach is effective, and that the J2 perturbation and the time-window constraints have considerable influences on the optimization results. For the problems in which the J2 perturbation is not accounted f...

Optimization of Logistics Strategies for Long-Duration Space-Station Operation

In this study, the optimization of logistics strategies for long-duration space-station operations is investigated; both the visit times and payload manifests of a series of coordinated flights of cargo vehicles are considered at the same time. An optimization model is established that employs both the launch time and manifesting mix of payload classes as design variables and considers the rendezvous launch window, onboard consumption demand, and vehicles’ carrying capabilities as constraints. Four metrics are defined to quantify the utilization benefit and operational robustness of a space-station operational scenario. Each metric is used as the optimization objective function, and a genetic algorithm is employed to obtain the optimal solutions. The approach is demonstrated with a notional one-year operational scenario of China’s future space station. The results indicate that the visit times of cargo vehicles have a considerable influence on logistics strategies, and each objective function of different...

Multi-objective optimization of space station logistics strategies using physical programming

This study extends a previously proposed single-objective optimization formulation of space station logistics strategies to multi-objective optimization. The four-objective model seeks to maximize the mean utilization capacity index, total utilization capacity index, logistics robustness index and flight independency index, aiming to improve both the utilization benefit and the operational robustness of a space station operational scenario. Physical programming is employed to convert the four-objective optimization problem into a single-objective problem. A genetic algorithm is proposed to solve the resulting physical programming-based optimization problem. Moreover, the non-dominated sorting genetic algorithm-II is tested to obtain the Pareto-optimal solution set and verify the Pareto optimality of the physical programming-based solution. The proposed approach is demonstrated with a notional one-year scenario of Chinas future space station. It is shown that the designer-preferred compromise solution improving both the utilization benefit and the operational robustness is successfully obtained.

Constraint satisfaction and optimization for space station short-term mission planning based on an iterative conflict-repair method

This article studies the optimization of space station short-term mission planning (STMP) problems. The domain knowledge including description and the concept definitions of the STMP problem are presented, an STMP constraint satisfaction model is developed, and then an iterative conflict-repair method with the resolving strategies is proposed to satisfy complicated constraints. A genetic algorithm (GA) is adopted to optimize the STMP problem. The proposed approach is evaluated using a test case with 15 missions, 13 devices and three astronauts. The results show that the established STMP constraint satisfaction model is effective, and the iterative conflict-repair method can make the plan satisfy all constraints considered and can effectively improve the optimization performance of the GA.

Predictive control of teleoperation rendezvous with large time delay

Teleoperation rendezvous and docking (RVD) means the RVD mission to be performed is at a distance from the controllers, which can be used as a backup for autonomous rendezvous for an unmanned spacecraft or for guiding the chaser docking with an uncooperative target. However, due to the time delays caused by distance and computer processing at spacecraft and ground stations, stability and control performance of the teleoperation rendezvous system are seriously affected. For spacecraft in a low earth orbit, the time delays may approach 6s. To eliminate the effect of time delay, a predictive control algorithm is investigated in this paper. The control issue of teleoperation RVD is described first, and the composing of time delay is analyzed. Then a predictive model is built based on the Clohessy-Whiltshire equations, and the processed measure information through time delay is utilized to correct the predictive relative states of the spacecraft. After that, a phase plane control method is introduced to teleoperate the chaser rendezvous and dock with the target. At last, a semi-physical simulation system is developed, and experiments are carried out with different time delays to verify the methods presented in this paper. The results show that the predictive control is effective on alleviating the time delay during the process of teleoperation RVD, and the success probability and control precision can be improved. Teleoperation RVD using this method can be applied as a useful backup for autonomous RVD.

Space station short-term mission planning using ontology modelling and time iteration

This paper studies the problem of the space station short-term mission planning, which aims to allocate the executing time of missions effectively, schedule the corresponding resources reasonably and arrange the time of the astronauts properly. A domain model is developed by using the ontology theory to describe the concepts, constraints and relations of the planning domain formally, abstractly and normatively. A method based on time iteration is adopted to solve the short-term planning problem. Meanwhile, the resolving strategies are proposed to resolve different kinds of conflicts induced by the constraints of power, heat, resource, astronaut and relationship. The proposed approach is evaluated in a test case with fifteen missions, thirteen resources and three astronauts. The results show that the developed domain ontology model is reasonable, and the time iteration method using the proposed resolving strategies can successfully obtain the plan satisfying all considered constraints.

Multi-objective optimisation and decision-making of space station logistics strategies

ABSTRACT Space station logistics strategy optimisation is a complex engineering problem with multiple objectives. Finding a decision-maker-preferred compromise solution becomes more significant when solving such a problem. However, the designer-preferred solution is not easy to determine using the traditional method. Thus, a hybrid approach that combines the multi-objective evolutionary algorithm, physical programming, and differential evolution (DE) algorithm is proposed to deal with the optimisation and decision-making of space station logistics strategies. A multi-objective evolutionary algorithm is used to acquire a Pareto frontier and help determine the range parameters of the physical programming. Physical programming is employed to convert the four-objective problem into a single-objective problem, and a DE algorithm is applied to solve the resulting physical programming-based optimisation problem. Five kinds of objective preference are simulated and compared. The simulation results indicate that the proposed approach can produce good compromise solutions corresponding to different decision-makers’ preferences.

Multi-objective evolutionary optimization of evasive maneuvers including observability performance

This paper investigates optimal orbital evasion problem with considering observability performance by using a multi-objective optimization approach. The degree of observability is defined as a new performance index, which has a negative correlation with the accuracy degree of relative state estimation. A two-objective optimization model is then formulated and the NSGA-II algorithm is employed to obtain the Pareto-optimal solution set. The numerical results show that the proposed approach can effectively and efficiently demonstrate the relations among the evasive mission characteristic parameters. The proposed approach offers a novel view in solving orbital evasion problem.