Robust entry guidance using multi-segment linear pseudospectral model predictive control

Yang, Liang; Chen, Wanchun; Liu, Xiaoming; Zhou, Hao

doi:10.21629/jsee.2017.01.13

Cited by 19 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Adding (21) into (24) and rearranging it, it can be found that the terminal state variation can be expressed as a linear function of the deviation of initial state , , ,…”

Section: B Linear Pseudospectral Model Predictive Spread Controlmentioning

confidence: 99%

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

Chen

Yang

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Adding (21) into (24) and rearranging it, it can be found that the terminal state variation can be expressed as a linear function of the deviation of initial state , , ,…”

Section: B Linear Pseudospectral Model Predictive Spread Controlmentioning

confidence: 99%

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

Chen

Yang

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although robust entry guidance algorithm using multisegment linear pseudospectral model predictive control is proposed in Ref [15], a brief summary of the salient steps are included in this section for completeness of the paper. The whole entry flight is divided into three phases: descent phase, glide phase and terminal adjustment phase.…”

Section: Linear Pseudospectral Model Predictive Entry Guidancementioning

confidence: 99%

“…n , ω i j are matrices dependent on the elements of Gauss differential approximation matrix and Gauss Quadrature weight, which are given in detail in [15]. Finally, the improved parameters used to eliminate the terminal errors are iteratively provided as u k+1 = u k − δu, E k+1 re1 = E k re1 − δE k ,If the lift-to-drag ratio command, u k+1 , is determined, the bank angle command is obtained as…”

Section: ) Correction Using Multi-segment Linear Pseudospectral Modementioning

confidence: 99%

“…Trajectory phugoid oscillation is a natural phenomenon during the entry flight and has a great impact on the precision of the entry guidance algorithm. Therefore, the trajectory damping control scheme proposed in [13], [15] is applied. The glide bank angle command, σ 2 , and lift coefficient, Cl (α 2 ), can be formulated as…”

Section: ) Trajectory Damping Control Schemementioning

confidence: 99%

“…Yu and Chen proposed an efficient entry guidance algorithm based on the trajectory damping control method and analytical solutions [13], [14]. Yang also designed an entry guidance law using linear pseudospectral model predictive control (LPMPC), which performs well in computational efficiency and robustness in comparison with traditional method [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Entry Guidance With No-Fly Zone Avoidance Using Linear Pseudospectral Model Predictive Control

Yang

Chen

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

This paper presents an efficient entry guidance law for no-fly zone avoidance, which is based on the recently developed linear pseudospectral model predictive control. First, a simple mapping relation of the position of the no-fly zone between the inertial frame (IF) and the auxiliary geographical frame (AGI) is derived in a geometric manner. Second, a model predictive method is used to judge whether the constraint of the no-fly zone is activated in AGI. Then, additional bank reversal is performed to shape the entry trajectory at the right time so as to avoid the no-fly zone. This method is very easy to be implemented onboard and does not increase the additional computational burden. The nominal and Monte Carlo simulations are conducted to show that the proposed method consistently offers very great, stable, and robust performances. INDEX TERMS Autonomous no-fly zone avoidance, entry guidance, linear pseudospectral model predictive control.

show abstract

Linear Pseudospectral Reentry Guidance with Adaptive Flight Phase Segmentation and Eliminating General Nominal Effort Miss Distance

Chen

Zhang

et al. 2020

Steady Glide Dynamics and Guidance of Hypersonic Vehicle

View full text Add to dashboard Cite

Robust entry guidance using multi-segment linear pseudospectral model predictive control

Cited by 19 publications

References 0 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

Entry Guidance With No-Fly Zone Avoidance Using Linear Pseudospectral Model Predictive Control

Linear Pseudospectral Reentry Guidance with Adaptive Flight Phase Segmentation and Eliminating General Nominal Effort Miss Distance

Contact Info

Product

Resources

About