Runqi Chai

Improved Gradient-Based Algorithm for Solving Aeroassisted Vehicle Trajectory Optimization Problems

The Space Maneuver Vehicles (SMV) [1, 2] will play an increasingly important role in the future exploration of space, since their on-orbit maneuverability can greatly increase the operational flexibility and are more difficult as a target to be tracked and intercepted. Therefore, a well-designed trajectory, particularly in 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. Due to the high nonlinear characteristics and strict path constraints of the problem, direct methods are usually applied to calculate the optimal trajectories, such as direct multiple shooting method [5], direct collocation method [5, 6], or hp-adaptive pseudospectral method [7, 8]. Nevertheless, all the direct methods aim to transcribe the continuous-time optimal control problems to a Nonlinear Programming Problem (NLP). The resulting NLP can be solved numerically by well-developed algorithms such as Sequential Quadratic Programming (SQP) and Interior Point method (IP) [9, 10]. SQP methods are used successfully for the solution of large scale NLPs. Each Newton iteration of the SQP requires the solution of a quadratic programming subproblem

Trajectory optimization of space maneuver vehicle using a hybrid optimal control solver

In this paper, a constrained space maneuver vehicles trajectory optimization problem is formulated and solved using a new three-layer-hybrid optimal control solver. To decrease the sensitivity of the initial guess and enhance the stability of the algorithm, an initial guess generator based on a specific stochastic algorithm is applied. In addition, an improved gradient-based algorithm is used as the inner solver, which can offer the user more flexibility to control the optimization process. Furthermore, in order to analyze the quality of the solution, the optimality verification conditions are derived. Numerical simulations were carried out by using the proposed hybrid solver and the results indicate that the proposed strategy can have better performance in terms of convergence speed and convergence ability when compared with other typical optimal control solvers. A Monte-Carlo simulation was performed and the results show a robust performance of the proposed algorithm in dispersed conditions.

Violation Learning Differential Evolution-Based hp-Adaptive Pseudospectral Method for Trajectory Optimization of Space Maneuver Vehicle

The sensitivity of the initial guess in terms of optimizer based on an hp-adaptive pseudospectral method for solving a space maneuver vehicles (SMV) trajectory optimization problem has long been recognized as a difficult problem. Because of the sensitivity with regard to the initial guess, it may cost the solver a large amount of time to do the Newton iteration and get the optimal solution or even the local optimal solution. In this paper, to provide the optimizer a better initial guess and solve the SMV trajectory optimization problem, an initial guess generator using a violation learning differential evolution algorithm is introduced. A new constraint-handling strategy without using penalty function is presented to modify the fitness values so that the performance of each candidate can be generalized. In addition, a learning strategy is designed to add diversity for the population in order to improve the convergency speed and avoid local optima. Several simulation results are conducted by using the combination algorithm; simulation results indicated that using limited computational efforts, the method proposed to generate initial guess can have better performance in terms of convergence ability and convergence speed compared with other approaches. By using the initial guess, the combinational method can also enhance the quality of the solution and reduce the number of Newton iteration and computational time. Therefore, the method is potentially feasible for solving the SMV trajectory optimization problem.

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 aeroassisted 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 multiobjective 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.

Analysis of Optimization Strategies for Solving Space Manoeuvre Vehicle Trajectory Optimization Problem

In this paper, two types of optimization strategies are applied to solve the Space Manoeuvre Vehicle (SMV) trajectory optimization problem. The SMV dynamic model is constructed and discretized applying direct multiple shooting method. To solve the resulting Nonlinear Programming (NLP) problem, gradient-based and derivative free optimization techniques are used to calculate the optimal time history with respect to the states and controls. Simulation results indicate that the proposed strategies are effective and can provide feasible solutions for solving the constrained SMV trajectory design problem.

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 pres...