Angle-Constrained Terminal Guidance Using Quasi-Spectral Model Predictive Static Programming

Mondal, Sabyasachi; Padhi, Radhakant

doi:10.2514/1.g002893

Cited by 27 publications

(11 citation statements)

References 7 publications

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“…It should be noted that the time step of prediction simulation for MPSP, LPMPC and LPMPSC is 50 ms. The number of collocation points for MPSP is 121, and the collocation points for LPMPC and LPMPSC are 6 LG points. In comparison with LPMPC, the number of updated variables for LPMPSC is decreased from 12 to only 6.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Note that, because above guidance laws are under the assumptions of the PN philosophy, they cannot provide optimal guidance commands and suffer from many inherent drawbacks such as sharp lateral accelerations at the end of engagement, no trajectory shaping capability, and etc. [1], [6], [7]. Therefore, many researchers pay attention to developing the impact-angle-constrained guidance under the framework of optimal control theory [8]- [15] which can fully exert the maneuverability so as to increase the operational effectiveness for missile weapon system.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it has high computational efficiency in comparison with the conventional numerical methods and dynamic programming approach. Also, this method has been successfully applied to formulate several optimal guidance laws with terminal angle constraint [6], [19]- [21]. However, to ensure the Euler integration within a desired precision limitation, a large number of discrete points should be selected for MPSP.…”

Section: Introductionmentioning

confidence: 99%

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Suboptimal Impact-Angle-Constrained Guidance Law Using Linear Pseudospectral Model Predictive Spread Control

Chen

Yang

2020

IEEE Access

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This paper aims at proposing a nonlinear suboptimal impact-angle-constrained guidance law for an interceptor engaging against an incoming high-speed ballistic missile in three dimensions. The method is based on an improved Linear Pseudospectral Model Predictive Control (LPMPC), in which the control variable is parameterized as a sum of general standard basis functions with few undetermined coefficients. Although the final solution is suboptimal, a series of analytical improved formulas can be also successively derived to eliminate the final predictive errors, which remains the character that few points will achieve high accuracy. Furthermore, the number of improved variables will significantly decrease, which will further improve the computational efficiency and provide smoother control history. Several numerical simulations are carried out to evaluate the performance of the proposed method. Results show that it performs well in achieving desired impact angles and the near-zero miss distance with high accuracy. In comparison with other typical methods, the proposed method has superior performance in computational efficiency. Therefore, it has the potential to be applied in the framework of guidance.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Suboptimal Impact-Angle-Constrained Guidance Law Using Linear Pseudospectral Model Predictive Spread Control

Chen

Yang

2020

IEEE Access

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“…For example, the generalized MPSP [27] formulated the problem in continuous-time framework, not requiring any discretization process to begin with. Quasi-Spectral MPSP [31] expressed the control profile as a weighted sum of basis functions, enabling the method to optimize only a set of coefficients instead of optimizing the control variable at every grid point. Recently, Sakode and Padhi [32] extended MPSP to impulse control, but with fixed impulse time instance and continuous impulse magnitude.…”

Section: Introductionmentioning

confidence: 99%

Robust Bang-Off-Bang Low-Thrust Guidance Using Model Predictive Static Programming

Wang

Topputo

2020

Acta Astronautica

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“…Quasi-Spectral MPSP [28] expresses the control profile as a weighted sum of basis functions, enabling the method to optimize only a set of coefficients instead of optimizing the control variable at every grid point. However, most works assume continuity of the control profile, which impedes its application for low-thrust transfer missions with bang-off-bang control.…”

Section: Introductionmentioning

confidence: 99%

Robust Bang-Off-Bang Low-Thrust Guidance Using Model Predictive Static Programming

Wang,

Topputo

2019

Preprint

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Model Predictive Static Programming (MPSP) was always used under the assumption of continuous control, which impedes it for applications with bang-off-bang control directly. In this paper, MPSP is employed for the first time as a guidance scheme for low-thrust transfers with bang-off-bang control where the fuel-optimal trajectory is used as the nominal solution. In our method, dynamical equations in Cartesian coordinates are augmented by the mass costate equation, while the unconstrained velocity costate vector is used as control variable, and is expressed as a combination of Fourier basis functions with corresponding weights. A two-loop MPSP algorithm is designed where the weights and the initial mass costate are updated in the inner loop and continuation is conducted on the outer loop in case of large perturbations. The sensitivity matrix (SM) is recursively calculated using analytical derivatives and SM at switching points is compensated based on calculus of variations. An sample interplanetary CubeSat mission to an asteroid is used as study case to illustrate the effectiveness of the method developed.

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Angle-Constrained Terminal Guidance Using Quasi-Spectral Model Predictive Static Programming

Cited by 27 publications

References 7 publications

Suboptimal Impact-Angle-Constrained Guidance Law Using Linear Pseudospectral Model Predictive Spread Control

Suboptimal Impact-Angle-Constrained Guidance Law Using Linear Pseudospectral Model Predictive Spread Control

Robust Bang-Off-Bang Low-Thrust Guidance Using Model Predictive Static Programming

Robust Bang-Off-Bang Low-Thrust Guidance Using Model Predictive Static Programming

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