Solving multi-objective aeroassisted spacecraft trajectory optimization problems using extended NSGA-II

This paper focuses on the application of an extended NSGA-II algorithm to the aero-assisted spacecraft trajectory optimization problems. A new multi-objective spacecraft optimal control model is formulated and parameterized using discretization method. The resulting multi-objective nonlinear programming problem is then solved via the multi-objective evolutionary solver. In order to deal with path constraints that naturally arise in practical trajectory planning problems, the original NSGA-II approach is extended by introducing a new constraint handling strategy. Simulation results are provided to illustrate the effectiveness and feasibility of the enhanced NSGA-II algorithm in dealing with spacecraft trajectory optimization problems

Two-stage trajectory optimization for autonomous ground vehicles parking maneuver

This paper proposes a two-stage optimization framework for generating the optimal parking motion trajectory of autonomous ground vehicles. The motivation for the use of this multi-layer optimization strategy relies on its enhanced convergence ability and computational efficiency in terms of finding optimal solutions under the constrained environment. In the first optimization stage, the designed optimizer applies an improved particle swarm optimization technique to produce a near-optimal parking movement. Subsequently, the motion trajectory obtained from the first stage is used to start the second optimization stage, where gradient-based techniques are applied. The established methodology is tested to explore the optimal parking maneuver for a car-like autonomous vehicle with the consideration of irregularly parked obstacles. Simulation results were produced and comparative studies were conducted for different mission cases. The obtained results not only confirm the effectiveness but also reveal the enhanced performance of the proposed optimization framework

Multi-objective trajectory optimization of Space Maneuver Vehicle using adaptive differential evolution and modified game theory

Highly constrained trajectory optimization for Space Manoeuvre Vehicles (SMV) is a challenging problem. In practice, this problem becomes more difficult when multiple mission requirements are taken into account. Because of the nonlinearity in the dynamic model and even the objectives, it is usually hard for designers to generate a compromised trajectory without violating strict path and box constraints. In this paper, a new multi-objective SMV optimal control model is formulated and parameterized using combined shooting-collocation technique. A modified game theory approach, coupled with an adaptive differential evolution algorithm, is designed in order to generate the pareto front of the multi-objective trajectory optimization problem. In addition, to improve the quality of obtained solutions, a control logic is embedded in the framework of the proposed approach. Several existing multi-objective evolutionary algorithms are studied and compared with the proposed method. Simulation results indicate that without driving the solution out of the feasible region, the proposed method can perform better in terms of convergence ability and convergence speed than its counterparts. Moreover, the quality of the pareto set generated using the proposed method is higher than other multi-objective evolutionary algorithms, which means the newly proposed algorithm is more attractive for solving multi-criteria SMV trajectory planning problem

Improved gradient-based algorithm for solving aeroassisted vehicle trajectory optimization problems

Space maneuver vehicles (SMVs) [1,2] will play an increasingly important role in the future exploration of space because their on-orbit maneuverability can greatly increase the operational flexibility, and they are more difficult as a target to be tracked and intercepted. Therefore, a well-designed trajectory, particularly in the skip entry phase, is a key for stable flight and for improved guidance control of the vehicle [3,4]. Trajectory design for space vehicles can be treated as an optimal control problem. Because of the highly nonlinear characteristics and strict path constraints of the problem, direct methods are usually applied to calculate the optimal trajectories, such as the direct multiple shooting method [5], direct collocation method [5,6], or hp hp -adaptive pseudospectral method [7,8]

Unified multiobjective optimization scheme for aeroassisted vehicle trajectory planning

In this work, a multiobjective aeroassisted trajectory optimization problem with mission priority constraints is constructed and studied. To effectively embed the priority requirements into the optimization model, a specific transformation technique is applied and the original problem is then transcribed to a single-objective formulation. The resulting single-objective programming model is solved via an evolutionary optimization algorithm. Such a design is unlike most traditional approaches where the nondominated sorting procedure is required to be performed to rank all the objectives. Moreover, in order to enhance the local search ability of the optimization process, a hybrid gradient-based operator is introduced. Simulation results indicate that the proposed design can produce feasible and high-quality flight trajectories. Comparative simulations with other typical methods are also performed, and the results show that the proposed approach can achieve a better performance in terms of satisfying the prespecified priority requirements

Solving multiobjective constrained trajectory optimization problem by an extended evolutionary algorithm

Highly constrained trajectory optimization problems are usually difficult to solve. Due to some real-world requirements, a typical trajectory optimization model may need to be formulated containing several objectives. Because of the discontinuity or nonlinearity in the vehicle dynamics and mission objectives, it is challenging to generate a compromised trajectory that can satisfy constraints and optimize objectives. To address the multiobjective trajectory planning problem, this paper applies a specific multiple-shooting discretization technique with the newest NSGA-III optimization algorithm and constructs a new evolutionary optimal control solver. In addition, three constraint handling algorithms are incorporated in this evolutionary optimal control framework. The performance of using different constraint handling strategies is detailed and analyzed. The proposed approach is compared with other well-developed multiobjective techniques. Experimental studies demonstrate that the present method can outperform other evolutionary-based solvers investigated in this paper with respect to convergence ability and distribution of the Pareto-optimal solutions. Therefore, the present evolutionary optimal control solver is more attractive and can offer an alternative for optimizing multiobjective continuous-time trajectory optimization problems

Optimal fuel consumption finite-thrust orbital hopping of aeroassisted spacecraft

In the paper, the problem of minimum-fuel aeroassisted spacecraft regional reconnaissance (orbital hopping) is considered. A new nonlinear constrained optimal control formulation is designed and constructed so as to describe this mission scenario. This formulation contains multiple exo-atmospheric and atmospheric flight phases and correspondingly, two sets of flight dynamics. The constructed continuous-time optimal control system is then discretized via a multi-phase global collocation technique. The resulting discrete-time system is optimized using a newly proposed gradient-based optimization algorithm. Several comparative simulations are carried out and the obtained optimal results indicate that it is effective and feasible to use the proposed multi-phase optimal control design for achieving the aeroassisted vehicle orbital hopping mission

A review of optimization techniques in spacecraft flight trajectory design

For most atmospheric or exo-atmospheric spacecraft flight scenarios, a well-designed trajectory is usually a key for stable flight and for improved guidance and control of the vehicle. Although extensive research work has been carried out on the design of spacecraft trajectories for different mission profiles and many effective tools were successfully developed for optimizing the flight path, it is only in the recent five years that there has been a growing interest in planning the flight trajectories with the consideration of multiple mission objectives and various model errors/uncertainties. It is worth noting that in many practical spacecraft guidance, navigation and control systems, multiple performance indices and different types of uncertainties must frequently be considered during the path planning phase. As a result, these requirements bring the development of multi-objective spacecraft trajectory optimization methods as well as stochastic spacecraft trajectory optimization algorithms. This paper aims to broadly review the state-of-the-art development in numerical multi-objective trajectory optimization algorithms and stochastic trajectory planning techniques for spacecraft flight operations. A brief description of the mathematical formulation of the problem is firstly introduced. Following that, various optimization methods that can be effective for solving spacecraft trajectory planning problems are reviewed, including the gradient-based methods, the convexification-based methods, and the evolutionary/metaheuristic methods. The multi-objective spacecraft trajectory optimization formulation, together with different class of multi-objective optimization algorithms, is then overviewed. The key features such as the advantages and disadvantages of these recently-developed multi-objective techniques are summarised. Moreover, attentions are given to extend the original deterministic problem to a stochastic version. Some robust optimization strategies are also outlined to deal with the stochastic trajectory planning formulation. In addition, a special focus will be given on the recent applications of the optimized trajectory. Finally, some conclusions are drawn and future research on the development of multi-objective and stochastic trajectory optimization techniques is discussed

Stochastic spacecraft trajectory optimization with the consideration of chance constraints

This brief investigates a computational framework based on optimal control for addressing the problem of stochastic trajectory optimization with the consideration of chance constraints. This design employs a discretization technique to parameterize uncertain variables and create the trajectory ensemble. Subsequently, the resulting discretized version of the problem is solved by applying standard optimal control solvers. In order to provide reliable gradient information to the optimization algorithm, a smooth and differentiable chance-constraint approximation method is proposed to replace the original probability constraints. The established methodology is implemented to explore the optimal trajectories for a spacecraft entry flight planning scenario with noise-perturbed dynamics and probabilistic constraints. Simulation results and comparative studies demonstrate that the present chance-constraint handling strategy can outperform other existing approaches analyzed in this brief, and this computational framework can produce reliable and less conservative solutions for the chance-constrained stochastic spacecraft trajectory planning problem

Optimal tracking guidance for aeroassisted spacecraft reconnaissance mission based on receding horizon control

This paper focuses on the application of model predictive control (MPC) for the spacecraft trajectory tracking problems. The motivation of the use of MPC, also known as receding horizon control, relies on its ability in dealing with control, state and path constraints that naturally arise in practical trajectory planning problems. Two different MPC schemes are constructed to solve the reconnaissance trajectory tracking problem. Since the MPC solves the online optimal control problems at each sampling instant, the computational cost associated with it can be high. In order to decrease the computational demand due to the optimization process, a newly proposed two-nested gradient method is used and embedded in the two MPC schemes. Simulation results are provided to illustrate the effectiveness and feasibility of the two MPC tracking algorithms combined with the improved optimization technique