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Статті в журналах з теми "Optimal Ascent Guidance"

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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

Lu, Ping, and Binfeng Pan. "Highly Constrained Optimal Launch Ascent Guidance." Journal of Guidance, Control, and Dynamics 33, no. 2 (March 2010): 404–14. http://dx.doi.org/10.2514/1.45632.

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2

Pan, Bin Feng, and Shuo Tang. "Numerical Improvements to Closed-Loop Ascent Guidance." Advanced Materials Research 383-390 (November 2011): 5076–81. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.5076.

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This paper presents numerical enhancements for optimal closed-loop ascent guidance through atmospheric. For 3-dimensional ascent formulation, optimal endo-atmospheric ascent trajectory is numerically obtained by the relaxation approach, and the exo-atmospheric ascent trajectory is generated by an analytical multiple-shooting method. A new root-finding method based on double dogleg method and More’s Levenberg-Marquardt method with Gaussian elimination is presented. The simulation results indicate that our new algorithm has remarkable computation and convergence performances.

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3

Lu, Ping, Brian J. Griffin, Gregory A. Dukeman, and Frank R. Chavez. "Rapid Optimal Multiburn Ascent Planning and Guidance." Journal of Guidance, Control, and Dynamics 31, no. 6 (November 2008): 1656–64. http://dx.doi.org/10.2514/1.36084.

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4

Hull, David G. "Optimal Guidance for Quasi-planar Lunar Ascent." Journal of Optimization Theory and Applications 151, no. 2 (July 1, 2011): 353–72. http://dx.doi.org/10.1007/s10957-011-9884-5.

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5

Benjamin, Gifty, and U. P. Rajeev. "Optimal endoatmospheric ascent phase guidance with load constraint." IFAC-PapersOnLine 53, no. 1 (2020): 266–71. http://dx.doi.org/10.1016/j.ifacol.2020.06.045.

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6

Zhao, Shilei, Wanchun Chen, and Liang Yang. "Endoatmospheric Ascent Optimal Guidance with Analytical Nonlinear Trajectory Prediction." International Journal of Aerospace Engineering 2022 (January 18, 2022): 1–26. http://dx.doi.org/10.1155/2022/5729335.

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In this paper, an endoatmospheric ascent optimal guidance law with terminal constraint is proposed, which is under the framework of predictor-corrector algorithm. Firstly, a precise analytical nonlinear trajectory prediction with arbitrary Angle of Attack (AOA) profile is derived. This derivation process is divided into two steps. The first step is to derive the analytical trajectory with zero AOA using a regular perturbation method. The other step is to employ pseudospectral collocation scheme and regular perturbation method to solve the increment equation so as to derive the analytical solution with arbitrary AOA profile. The increment equation is formulated by Taylor expansion around the trajectory with zero AOA which remains the second order increment terms. Therefore, the resulting analytical solutions are the nonlinear functions of high order terms of arbitrary AOA values discretized in Chebyshev-Gauss-Legendre points, which has high accuracy. Secondly, an iterative correction scheme using analytical gradient is proposed to solve the endoatmospheric ascent optimal guidance problem, in which the dynamical constraint is enforced by the resulting analytical solutions. It only takes a fraction of a second to get the guidance command. Nominal simulations, Monte Carlo simulations, and optimality verification are carried out to test the performance of the proposed guidance law. The results show that it not only performs well in providing the optimal guidance command, but also has great applicability, high guidance accuracy and computational efficiency. Moreover, it has great robustness even in large dispersions and uncertainties.

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7

Zhang, Da, Lei Liu, and Yongji Wang. "On-line Ascent Phase Trajectory Optimal Guidance Algorithm based on Pseudo-spectral Method and Sensitivity Updates." Journal of Navigation 68, no. 6 (June 10, 2015): 1056–74. http://dx.doi.org/10.1017/s0373463315000326.

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The objective of this paper is to investigate an online method to generate an optimal ascent trajectory for air-breathing hypersonic vehicles. A direct method called the Pseudo-spectral method shows promise for real-time optimal guidance. A significant barrier to this optimisation-based control strategy is computational delay, especially when the solution time of the non-linear programming problem exceeds the sampling time. Therefore, an online guidance algorithm for an air-breathing hypersonic vehicles with process constraints and terminal states constraints is proposed based on the Pseudo-spectral method and sensitivity analysis in this paper, which can reduce online computational costs and improve performance significantly. The proposed ascent optimal guidance method can successively generate online open-loop suboptimal controls without the design procedure of an inner-loop feedback controller. Considering model parameters' uncertainties and external disturbance, a sampling theorem is proposed that indicates the effect of the Lipschitz constant of the dynamics on sampling frequency. The simulation results indicate that the proposed method offers improved performance and has promising ability to generate an optimal ascent trajectory for air-breathing hypersonic vehicles.

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8

Lu, Xuefang, Yongji Wang, and Lei Liu. "Optimal Ascent Guidance for Air-Breathing Launch Vehicle Based on Optimal Trajectory Correction." Mathematical Problems in Engineering 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/313197.

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Анотація:

An optimal guidance algorithm for air-breathing launch vehicle is proposed based on optimal trajectory correction. The optimal trajectory correction problem is a nonlinear optimal feedback control problem with state inequality constraints which results in a nonlinear and nondifferentiable two-point boundary value problem (TPBVP). It is difficult to solve TPBVP on-board. To reduce the on-board calculation cost, the proposed guidance algorithm corrects the reference trajectory in every guidance cycle to satisfy the optimality condition of the optimal feedback control problem. By linearizing the optimality condition, the linear TPBVP is obtained for the optimal trajectory correction. The solution of the linear TPBVP is obtained by solving linear equations through the Simpson rule. Considering the solution of the linear TPBVP as the searching direction for the correction values, the updating step size is generated by linear search. Smooth approximation is applied to the inequality constraints for the nondifferentiable Hamiltonian. The sufficient condition for the global convergence of the algorithm is given in this paper. Finally, simulation results show the effectiveness of the proposed algorithm.

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9

Calise, Anthony J., Nahum Melamed, and Seungjae Lee. "Design and Evaluation of a Three-Dimensional Optimal Ascent Guidance Algorithm." Journal of Guidance, Control, and Dynamics 21, no. 6 (November 1998): 867–75. http://dx.doi.org/10.2514/2.4350.

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10

Pontani, Mauro, and Fabio Celani. "Lunar Ascent and Orbit Injection via Neighboring Optimal Guidance and Constrained Attitude Control." Journal of Aerospace Engineering 31, no. 5 (September 2018): 04018071. http://dx.doi.org/10.1061/(asce)as.1943-5525.0000908.

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11

Polovinchuk, N. Y., S. V. Ivanov, M. Y. Zhukova, and D. G. Belonozhko. "Method of terminal control in ascent segment of unmanned aerial vehicle with ballistic phase." Vestnik of Don State Technical University 19, no. 1 (April 1, 2019): 93–100. http://dx.doi.org/10.23947/1992-5980-2019-19-1-93-100.

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Introduction. The solution to the problem on the centroidal motion control synthesis (guidance problem) of an unmanned aerial vehicle (UAV) with long-range capabilities in the boost phase is considered. Control condition requires optimum fuel consumption. The principle of dynamic programming considering the restrictions to the vector modulus of the thrust output is used to solve the problem. The implementation of terminal guidance requires the formation of control as a function of the object state at the end of the ascent phase. The attainment of these boundary conditions determines the further transition to the ballistic flight phase.Materials and Methods. Bellman’s principle of dynamic programming is the most reasonable from the point of view of the implementability of the computationally efficient on-board algorithms and the solution to the problems in the form of synthesis. With natural scarcity of thrust and energy resources on board, this principle enables to obtain solutions free from the switching functions. In this case, the optimal control is a smooth function (without derivative discontinuity) of the current and final parameters of the UAV.Research Results. A new algorithmic method for the synthesis of terminal motion control is developed. Its difference is that the UAV movement control in the ascent phase is formed by the function of the motion actual and terminal parameters. This ensures movement along an energetically optimal trajectory into the given region of space. The problem solution results enable to build closed terminal guidance algorithms for the boost phase of the UAV trajectory with long-range capabilities. Such algorithms have good convergence and injection accuracy due to the prediction of parameters during the flight at a shorter time interval.Discussion and Conclusions. The most preferred is the principle of dynamic programming. It should be used when solving the problem on the centroidal motion control synthesis (guidance problem) of the UAV with long-range capabilities in the boost phase.

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12

Pontani, Mauro, and Fabio Celani. "Neighboring optimal guidance and constrained attitude control applied to three-dimensional lunar ascent and orbit injection." Acta Astronautica 156 (March 2019): 78–91. http://dx.doi.org/10.1016/j.actaastro.2018.08.039.

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13

Wei, Changzhu, Yepeng Han, Jialun Pu, Yuan Li, and Panxing Huang. "Rapid multi-layer method on solving optimal endo-atmospheric trajectory of launch vehicles." Aeronautical Journal 123, no. 1267 (June 6, 2019): 1396–414. http://dx.doi.org/10.1017/aer.2019.17.

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ABSTRACTIn order to increase the speed, precision and robustness against the engine failure in solving optimal endo-atmospheric ascent trajectory of a launch vehicle, a rapid multi-layer solving method with improved numerical algorithms was proposed. The proposed method is capable of decomposing a large number of intervals into multiple layers with advantageous convergence property. Firstly, the problem of solving optimal endo-atmospheric ascent trajectory, which was subjected to path constraints and terminal constraints, was transformed into a Hamilton Two Point Boundary Value Problem (TPBVP). Then, through the finite difference method and numerical solving algorithm, the Hamilton TPBVP was iteratively solved with fewer initial discrete intervals. The initial values of higher-layer iterations were obtained by interpolating convergent solutions at sparse nodes into the doubly discrete nodes of high layers. The process was repeatedly performed until the solving precision met the requirements. To decrease the calculation load in solving TPBVPs, two improved solving algorithms without and with fewer Jacobian calculations were studied, respectively the Derivative-free Spectral Algorithm for Nonlinear Equations(DF-SANE) combined with the improved derivative-free nonmonotone line search strategy, and the Modified Newton method with a relaxation factor in combination with the Inverse Broyden Quasi-Newton method, denoted as ‘MN-IBQ’. Simulation verifications showed that the multi-layer method had significantly higher solving speed than the single-layer method. For the improved numerical algorithms, the DF-SANE was trapped in the local convergence problem. While using the proposed MN-IBQ can further increase the solving rate. Typical engine failure simulations showed that the multi-layer method with the MN-IBQ algorithm had not only significantly higher solving speed but also stronger robustness, where the traditional single-layer method could not adapt. In addition, the thrust loss tolerance limits for the multi-layer solving method were given for different engine failure times. The results show promising potential of the proposed approach in trajectory online generation and closed-loop guidance of launch vehicles at the endo-atmospheric ascent stage.

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14

Rugescu, R. D., Cr E. Constantinescu, M. Al Barbelian, Alina Bogoi, and C. Dumitrache. "Orbital Injection Errors and Sensor Requirements for NERVA Space Launchers." Applied Mechanics and Materials 325-326 (June 2013): 813–18. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.813.

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The input module of the NERVA space launcher guidance system consisting of the inertial and sensor platform is responsible for the basic accuracy if the ascent trajectory and injection efficiency. The sample rate magnitude and data filtering along the real time trajectory are the only tools available for improving the guidance accuracy up to the level of requirements to secure admissible orbital injection error and the subsequent flight corridor during the orbital ascent. Analysis of the NERVA-1 flight telemetry flow from the onboard inertial platform raises the problem of the optimal selection of the onboard sample rate and of the rate of telemetry, which are not identical. The orbit injection errors are chosen from the orbit altitude constraints and subsequent accuracy requirements for the inertial sensors are derived. They show that the accuracy requirements are moderate and may be covered with almost conventional sensors. To improve the flight guidance accuracy the rocket motor chamber pressure and thrust are measured and observation of the preflight zero drift, recording noise and of the high level of embedded noise during both powered and coast atmospheric flight is performed. Simple filtering based on frequency Fourier analysis is delivered with conclusions regarding the intelligent algorithm enhancement that are developed and implemented on the next generation of flight research drone missiles RT-759M NERVA-2, right in preparation. The main rationale of that algorithm stands in the method of discriminating between false and true information on each measuring point immediately after the data are delivered by the sensors. Learning procedure from previous preflight recordings and from gradual accumulation of concurrent data streams subjected to FF spectral analysis are combined to improve data filtering, for immediate release to the next module of the autopilot. The rate of sampling is optimized from the analysis of the previous flight, inertial data records and test stand pressure and thrust records that show the level of noise. The behavior of the electronics under the dynamical loads of the rocket flight, involving overloads of more than 20 g-s and the level of vibration during the real flight and other sources of measuring errors are also focused in the research. During simulated work of the sensor platform the algorithm has been acceptably validated and prepared for real flight test performance. Information important for the NERVA autopilot design activity is structured through the multiple variance approach.

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15

Mozaffari, Mohiyeddin, Behrouz Safarinejadian, and Tahereh Binazadeh. "Optimal Guidance Law Based on Virtual Sliding Target." Journal of Aerospace Engineering 30, no. 3 (May 2017): 04016097. http://dx.doi.org/10.1061/(asce)as.1943-5525.0000692.

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16

Afshari, Hamed Hossein, Jafar Roshanian, and Alireza Novinzadeh. "Robust Nonlinear Optimal Solution to the Lunar Landing Guidance by Using Neighboring Optimal Control." Journal of Aerospace Engineering 24, no. 1 (January 2011): 20–30. http://dx.doi.org/10.1061/(asce)as.1943-5525.0000048.

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17

Jamilnia, Reza, and Abolghasem Naghash. "Optimal Guidance Based on Receding Horizon Control and Online Trajectory Optimization." Journal of Aerospace Engineering 26, no. 4 (October 2013): 786–93. http://dx.doi.org/10.1061/(asce)as.1943-5525.0000194.

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18

Pontani, Mauro, and Fabio Celani. "Neighboring Optimal Guidance and Attitude Control of Low-Thrust Earth Orbit Transfers." Journal of Aerospace Engineering 33, no. 6 (November 2020): 04020070. http://dx.doi.org/10.1061/(asce)as.1943-5525.0001190.

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19

Pontani, Mauro, and Fabio Celani. "Neighboring Optimal Guidance and Constrained Attitude Control for Accurate Orbit Injection." Aerotecnica Missili & Spazio, May 12, 2021. http://dx.doi.org/10.1007/s42496-021-00077-3.

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AbstractAccurate orbit injection represents a crucial issue in several mission scenarios, e.g., for spacecraft orbiting the Earth or for payload release from the upper stage of an ascent vehicle. This work considers a new guidance and control architecture based on the combined use of (i) the variable-time-domain neighboring optimal guidance technique (VTD-NOG), and (ii) the constrained proportional-derivative (CPD) algorithm for attitude control. More specifically, VTD-NOG & CPD is applied to two distinct injection maneuvers: (a) Hohmann-like finite-thrust transfer from a low Earth orbit to a geostationary orbit, and (b) orbit injection of the upper stage of a launch vehicle. Nonnominal flight conditions are modeled by assuming errors on the initial position, velocity, attitude, and attitude rate, as well as actuation deviations. Extensive Monte Carlo campaigns prove effectiveness and accuracy of the guidance and control methodology at hand, in the presence of realistic deviations from nominal flight conditions.

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20

Li, Yuan, Ruisheng Sun, and Wei Chen. "Online trajectory optimization and guidance algorithm for space interceptors with nonlinear terminal constraints via convex programming." Aircraft Engineering and Aerospace Technology, June 15, 2022. http://dx.doi.org/10.1108/aeat-01-2022-0005.

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Purpose In this paper, an online convex optimization method for the exoatmospheric ascent trajectory of space interceptors is proposed. The purpose of this paper is to transform the original trajectory optimization problem into a sequence of convex optimization subproblems. Design/methodology/approach For convenience in calculating accuracy and efficiency, the complex nonlinear terminal orbital elements constraints are converted into several quadratic equality constraints, which can be better computed by a two-step correction method during the iteration. First, the nonconvex thrust magnitude constraint is convexified by the lossless convexification technique. Then, discretization and successive linearization are introduced to transform the original problem into a sequence of one convex optimization subproblem, considering different flight phases. Parameters of trust-region and penalty are also applied to improve the computation performance. To correct the deviation in real time, the iterative guidance method is applied before orbit injection. Findings Numerical experiments show that the algorithm proposed in this paper has good convergence and accuracy. The successive progress can converge in a few steps and 3–4 s of CPU time. Even under engine failure or mission change, the algorithm can yield satisfactory results. Practical implications The convex optimization method presented in this paper is expected to generate a reliable optimal trajectory rapidly in different situations and has great potential for onboard applications of space interceptors. Originality/value The originality of this paper lies in the proposed online trajectory optimization method and guidance algorithm of the space inceptors, especially for onboard applications in emergency situations.

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