Fuzzy physical programming for Space Manoeuvre Vehicles trajectory optimization based on hp-adaptive pseudospectral method

IEEE Trans. Aerosp. Electron. Syst.

2017

Self Cite

The sensitivity of the initial guess in terms of optimizer based on an hp-adaptive pseudospectral method for solving a space maneuver vehicle's (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.

Section: Discussionmentioning

confidence: 99%

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

IEEE Trans. Aerosp. Electron. Syst.

2017

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“…This section presents the multi-objective optimization method used to solve the problem. It is worth noting that in [29], the authors designed and applied a fuzzy physical programming algorithm to transform multiple objectives into a single objective formulation. Subsequently, an interactive strategy was incorporated in the algorithm framework so as to better control the optimization process [30].…”

Section: A Discrete-time Optimal Control Modelmentioning

confidence: 99%

Solving Multiobjective Constrained Trajectory Optimization Problem by an Extended Evolutionary Algorithm

Xia

et al. 2020

IEEE Trans. Cybern.

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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 multi-objective trajectory planning problem, this study 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 multi-objective continuous-time trajectory optimization problems.

“…This is because in this paper, it is expected for the aeroassisted vehicle to have a multiple-hop trajectory in order to overfly different target regions and complete the reconnaissance mission. An example of a single-hop mission can be found in our previous work [22].…”

Section: Aeroassisted Spacecraft Reconnaissance Optimal Control Problemmentioning

confidence: 99%

“…A comprehensive experiment and analysis in terms of the highly correlated or contradicting relationships between aerodynamic heat, vehicle's speed and fuel consumption can be found in [22,23]. Altitude (m)…”

Section: Sensitivity With Respect To Path Constraintmentioning

confidence: 99%

See 1 more Smart Citation

Optimal fuel consumption finite-thrust orbital hopping of aeroassisted spacecraft

Aerospace Science and Technology

et al. 2018

Self Cite

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.