Evolutionary method based integrated guidance strategy for reentry vehicles

Sushnigdha, Gangireddy; Joshi, Ashok

doi:10.1016/j.engappai.2017.11.010

Cited by 12 publications

(2 citation statements)

References 16 publications

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“…Other uses of EAs in reentry applications can be found in [21], where GA is used to optimize the parameters of a sliding mode controller applied to the attitude control of a reentry vehicle. While, in [22] the evolutionary method Pigeon Inspired Optimization is combined with a Gauss Newton gradient based method to form a predictor-corrector guidance algorithm. The goal of this guidance scheme is to use the EA to generate an initial condition for the gradient-based method to solve the entry guidance problem.…”

Section: Related Workmentioning

confidence: 99%

Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties

Marchetti

Minisci

2021

Mathematics

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As technology improves, the complexity of controlled systems increases as well. Alongside it, these systems need to face new challenges, which are made available by this technology advancement. To overcome these challenges, the incorporation of AI into control systems is changing its status, from being just an experiment made in academia, towards a necessity. Several methods to perform this integration of AI into control systems have been considered in the past. In this work, an approach involving GP to produce, offline, a control law for a reentry vehicle in the presence of uncertainties on the environment and plant models is studied, implemented and tested. The results show the robustness of the proposed approach, which is capable of producing a control law of a complex nonlinear system in the presence of big uncertainties. This research aims to describe and analyze the effectiveness of a control approach to generate a nonlinear control law for a highly nonlinear system in an automated way. Such an approach would benefit the control practitioners by providing an alternative to classical control approaches, without having to rely on linearization techniques.

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Section: Related Workmentioning

confidence: 99%

Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties

Marchetti

Minisci

2021

Mathematics

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“…The PIO algorithm is a swarm intelligencebased algorithm and was first proposed by Duan in 2014. 16 The swarm intelligence-based algorithms have been widely applied in the path planning problem, and the advantage is to find a satisfactory solution within limited elapsed time, 17,18 which meets the requirement of an online planning problem. The validity of the PIO algorithm has been proved in the path planning problem, 19 and the PIO algorithm has demonstrated a better performance compared with other algorithms like genetic algorithm (GA) and particle swarm optimization (PSO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

A path planning model of a tiltrotor for approaching an aircraft carrier during landing

Su³

2021

Proceedings of the Institution of Mechanical Engineers, Part G:

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A path planning model concerning a tiltrotor approaching an aircraft carrier is established in this study. In the model, the characteristic of the tiltrotor, the landing task, and the environment of the carrier are taken into account. First, the motion equations and the maneuverability of the tiltrotor in each flight mode are presented, and the constraints of control variables and flight envelope are given. The returning flight of the tiltrotor is divided into three phases corresponding to the three flight modes of the tiltrotor, and the constraints in each phase and the goal are set. Considering the flight safety of the tiltrotor, the environment of the carrier is described as flyable space and no-fly zones, and the no-fly zones are set taking the influences of turbulence and wind field induced by the moving aircraft carrier into account. The path planning issue is formulated into an optimization problem under the constraints of control variables and state variables. According to the characteristic of the established model, a pigeon inspired optimization (PIO)-based path planning algorithm is developed integrating the “step-by-step” and “one effort” path search strategies. Simulation results demonstrate that the tiltrotor can reach the target point with a reasonable landing path. Comparison among different algorithms is also conducted to verify that the PIO algorithm is capable of solving this online path planning problem.

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Optimal 3-dimension trajectory-tracking guidance for reusable launch vehicle based on back-stepping adaptive dynamic programming

Wang

Quan²,

Zhang³

2022

Neural Comput & Applic

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Evolutionary method based integrated guidance strategy for reentry vehicles

Cited by 12 publications

References 16 publications

Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties

Genetic Programming Guidance Control System for a Reentry Vehicle under Uncertainties

A path planning model of a tiltrotor for approaching an aircraft carrier during landing

Optimal 3-dimension trajectory-tracking guidance for reusable launch vehicle based on back-stepping adaptive dynamic programming

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