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Relevant bibliographies by topics / Optimal Ascent Guidance

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Academic literature on the topic 'Optimal Ascent Guidance'

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Published: 6 September 2023

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Journal articles on the topic "Optimal Ascent Guidance"

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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Optimal Ascent Guidance"

1

Griffin, Brian Joseph. "Improvements to an analytical multiple-shooting approach for optimal burn-coast-burn ascent guidance." [Ames, Iowa : Iowa State University], 2007.

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2

Yadav, Sparsh. "Direct Methods for Optimal Ascent Guidance." Thesis, 2022. https://etd.iisc.ac.in/handle/2005/5761.

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An ascent guidance algorithm determines the thrust vector that allows the spacecraft to reach the desired orbit. Generally, optimal ascent guidance algorithms try to reach the orbit while minimizing mission time or fuel. A renewed interest in new-generation space missions necessitates the development of optimal ascent guidance algorithms that are efficient in time and control and can accommodate ever-changing mission constraints. These guidance algorithms will pave the way for future autonomous space exploration. The first part of this thesis develops an ascent guidance algorithm that guides a spacecraft from a known initial position to an orbit of known apogee and perigee in minimum time. The algorithm follows an iterative approach that reduces the terminal error over successive iterations while keeping the control inputs within bounds. Every iteration consists of a model-predicting phase in which the initial conditions and system dynamics are used to calculate the error at the end of guidance. It is followed by an optimization phase that helps us to minimize time and accommodate path constraints. Numerical simulations are carried out using a point mass model of a spacecraft. The initial guess for control that is required for simulations is generated using an existing polynomial guidance method. Next, we study the algorithm's behavior for different guess inputs of the thrust and the final time. Further analysis is carried out by varying the learning parameter and initial position of the spacecraft. Finally, we do a comparative study of the algorithm with commercially available optimal control solvers. Simulation results show faster convergence of the proposed minimum-time algorithm compared to other optimal control software. Another essential and desirable characteristic of a guidance algorithm is lower control effort spent in achieving the mission objective. In the second part of this thesis, we augment the cost function of the algorithm with a weighted running cost on the control effort. The weights of the running cost allow us to tune the algorithm to achieve a balance between the mission time and the control effort invested in guidance. Numerical simulations are carried out to analyze algorithm behavior for different initial conditions and by steadily increasing the weight of the running cost. As the main result, we observe that the control effort can be reduced signi ficantly with a correspondingly small trade-off in mission time.

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Books on the topic "Optimal Ascent Guidance"

1

-C, Chou H., Bowles J, and United States. National Aeronautics and Space Administration., eds. Near-optimal operation of dual-fuel launch vehicles. Reston, VA: American Institute of Aeronautics and Astronautics, 1996.

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2

-C, Chou H., Bowles J, and United States. National Aeronautics and Space Administration., eds. Near-optimal operation of dual-fuel launch vehicles. Reston, VA: American Institute of Aeronautics and Astronautics, 1996.

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3

-C, Chou H., Bowles J, and United States. National Aeronautics and Space Administration., eds. Near-optimal operation of dual-fuel launch vehicles. Reston, VA: American Institute of Aeronautics and Astronautics, 1996.

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4

-C, Chou H., Bowles J, and United States. National Aeronautics and Space Administration., eds. Near-optimal operation of dual-fuel launch vehicles. Reston, VA: American Institute of Aeronautics and Astronautics, 1996.

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5

Optimal guidance law develpment for an advanced launch system. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1995.

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Book chapters on the topic "Optimal Ascent Guidance"

1

Song, Zhengyu, Cong Wang, and Yong He. "Autonomous Guidance Control for Ascent Flight." In Autonomous Trajectory Planning and Guidance Control for Launch Vehicles, 33–74. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0613-0_2.

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AbstractThe purpose of the guidance control is to release a payload into a prescribed target orbit (PTO) accurately. The parameters that determine an orbit are called orbital elements (OEs), which include the semi-major axis a, the eccentricity e, the argument of perigee $$\omega $$ ω , the inclination angle i, and the longitude of ascending node (LAN) or the right ascension of ascending node (RAAN) $$\Omega $$ Ω , where a and e can be converted to the perigee height $$h_p$$ h p and the apogee height $$h_a$$ h a . Thus, the guidance mission of a launcher is a typical optimal control problem with multi-terminal constraints, which requires complex iterative calculations. Considering various constraints in practical applications, such as the accuracy of inertial navigation systems and the performances of embedded computing devices (speed and storage capacity), guidance methods need to balance the mission requirements, hardware resources, and algorithm complexity. A variety of guidance methods has been developed with distinct era characteristics.

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2

Palaia, Guido, Marco Pallone, Mauro Pontani, and Paolo Teofilatto. "Ascent Trajectory Optimization and Neighboring Optimal Guidance of Multistage Launch Vehicles." In Springer Optimization and Its Applications, 343–71. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10501-3_13.

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3

Yan, Xiaodong, and Cong Zhou. "Ascent Predictive Guidance for Thrust Drop Fault of Launch Vehicles Using Improved GS-MPSP." In Autonomous Trajectory Planning and Guidance Control for Launch Vehicles, 75–98. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0613-0_3.

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AbstractIncreasing complex space missions require launch vehicles to be with greater load-carrying capacity, better orbit injection accuracy and higher reliability. Such demands also cause the increased complexity of the vehicle, leading to a higher probability of fault, especially for the propulsion system. To remedy this issue, an advanced and robust ascent guidance capable of fault-tolerant is critical for the success of mission. Iterative guidance method [1] (IGM) and powered explicit guidance [2] (PEG) are two commonly used methods for the ascent phase of launch vehicles. These two guidance methods work well in the nominal condition and can adapt to many off-nominal conditions [3]. However, they lack of strong adaptive capacity, which cannot guarantee the reliability when the dynamic model or parameters change significantly. Alternatively, numerical approaches based on the optimal control theory may be the better choice. The existing algorithms can be divided into direct methods and indirect methods. Using the indirect methods, the guidance problem is transformed into Hamilton two-point boundary value problems [4] (TPBVP), but the solving process of this Hamilton two-point boundary value problem is complicated and highly sensitive to the initial guess. Using the direct method, the guidance problem is transformed into a nonlinear programming problem [5] (NLP). However, solving such problem is extremely computational intensive, which is difficult to meet the real-time requirement for online application.

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Conference papers on the topic "Optimal Ascent Guidance"

1

Pan, Binfeng, and Ping Lu. "Improvements to Optimal Launch Ascent Guidance." In AIAA Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-8174.

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2

Lu, Ping, and Binfeng Pan. "Highly Constrained Optimal Launch Ascent Guidance." In AIAA Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-5961.

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3

Paus, Michael, and Klaus Well. "Optimal ascent guidance for a hypersonic vehicle." In Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1996. http://dx.doi.org/10.2514/6.1996-3901.

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4

Lu, Ping, Brian Griffin, Greg Dukeman, and Frank Chavez. "Rapid Optimal Multi-Burn Ascent Planning and Guidance." In AIAA Guidance, Navigation and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/6.2007-6773.

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5

Guan, Yingzi, Yuan Li, Jialun Pu, and Xiaozhe Ju. "Rapid Optimal Ascent Guidance for Multi-Engined Launch Vehicles." In 2018 IEEE CSAA Guidance, Navigation and Control Conference (GNCC). IEEE, 2018. http://dx.doi.org/10.1109/gncc42960.2018.9019012.

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6

Suzuki, Hirokazu, Hidenobu Fushimi, Yoshiaki Matsumoto, and Sota Kimura. "Optimal ascent trajectory and guidance law for aerospace plane." In 37th Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1999. http://dx.doi.org/10.2514/6.1999-386.

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7

Calise, Anthony, Nahum Melamed, Seungjae Lee, Anthony Calise, Nahum Melamed, and Seungjae Lee. "Design and evaluation of a 3-D optimal ascent guidance algorithm." In Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1997. http://dx.doi.org/10.2514/6.1997-3707.

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8

Liu, Qingyuan, Juanjuan Xu, and Huanshui Zhang. "Closed Loop Optimal Guidance Control For Aircraft In Ascent Phase *." In 2020 Chinese Automation Congress (CAC). IEEE, 2020. http://dx.doi.org/10.1109/cac51589.2020.9326969.

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9

Panxing Huang, Yingzi Guan, and Rong Huang. "A mixed variable variational method for optimal endo-atmospheric ascent guidance." In 2015 IEEE Aerospace Conference. IEEE, 2015. http://dx.doi.org/10.1109/aero.2015.7118986.

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10

Corban, J. E., A. J. Calise, and G. A. Flandro. "A Real-Time Guidance Algorithm for Aerospace Plane Optimal Ascent to Low Earth Orbit." In 1989 American Control Conference. IEEE, 1989. http://dx.doi.org/10.23919/acc.1989.4790604.

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