Partial Integrated Guidance and Control of Interceptors for High-Speed Ballistic Targets

Padhi, Radhakant; Chawla, Charu; Das, Payel

doi:10.2514/1.61416

Cited by 58 publications

(22 citation statements)

References 11 publications

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“…b max = 0.0579 is estimated by using (51). Let cos γ = 1 and ε = 10 in (17), we can get the minimum speed v min = 177.8 m/s by computing (17) and (18). Using (15) and (16) …”

Section: Speed Learningmentioning

confidence: 99%

“…The existing control methods for these problems are mainly based on the experience database [4] or numerical solution method [8] to determine the initial control inputs. In [6,17,28], the ballistic control problems are studied by incorporating continuous control inputs throughout the flying process. Such methods are inapplicable since the control inputs are only the initial states of the projectile in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Initial states iterative learning for three-dimensional ballistic endpoint control

et al. 2016

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In this paper, an initial states iterative learning control algorithm is proposed for control of the ballistic endpoint displacement in three-dimensional space, where the target is moving and the projectile experiences system uncertainties. The characteristics of the three-dimensional ballistic process are formulated and explored, and the learning algorithm is proposed in the spatial domain. The algorithm consists of two parts. First, the initial speed and angles are iteratively learned to make the projectile attain a fixed position. Second, the shooting time is learned to tune the arrival time of the projectile. Since the dimensions of the solution space are larger than that of the task space, three control manners, including shooting speed, shooting angle and their combination, are researched respectively. Through rigorously analyzed, it is proved that the algorithm is convergent and the multiple initial states can be adjusted simultaneously. Finally, an example of practical cannonball projection is presented to verify the effectiveness of the proposed algorithms.

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“…b max = 0.0579 is estimated by using (51). Let cos γ = 1 and ε = 10 in (17), we can get the minimum speed v min = 177.8 m/s by computing (17) and (18). Using (15) and (16) …”

Section: Speed Learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Initial states iterative learning for three-dimensional ballistic endpoint control

et al. 2016

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“…Based on the feedback linearization, IGC was transformed into the trajectory optimization of finite-time linear quadratic form [11], [12], with the numerical solution of Riccati equation developed in [13]. In [1], [14], and [15], a robust partial IGC (PIGC) design was proposed. In the PIGC method, both the outer and inner loops were designed to preserve the philosophical benefits of both IGC and conventional separated design.…”

Section: Introductionmentioning

confidence: 99%

Fast Robust Integrated Guidance and Control Design of Interceptors

Song

Zhang

2016

IEEE Trans. Contr. Syst. Technol.

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In this brief, a fast robust integrated guidance and control design approach, considering the 3-D interception of interceptors for hypersonic vehicles, is proposed. The proposed method is designed using the modified fast terminal sliding mode control and dynamic surface control with signal compensation. The fractional integral fast terminal sliding functions are presented independently, and all the sliding phases run parallel to increase the system response speed. Utilizing the dynamic surface control method, the robust compensation signals are constructed to eliminate the influence of uncertainty equivalents thoroughly. Therefore, the proposed approach guarantees the fast convergence of line-of-sight angular rates and the system robustness for uncertainties. The simulation results demonstrate the robustness of the proposed controller, and a variety of comparison studies are also carried out to demonstrate the advantage of the new approach.Index Terms-Dynamic surface control, fast terminal sliding mode (FTSM) control, integrated guidance and control (IGC), interceptor.

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“…Also, note that the outermost loop of a classical three-loop guidance then control formulation is eliminated here, thereby retaining a very important advantage of IGC, namely, the minimization of the overall settling time. For further details of the PIGC philosophy, one can refer to [13,14].…”

Section: Introductionmentioning

confidence: 99%

Formation flying with nonlinear partial integrated guidance and control

Padhi

Rakesh

Venkataraman

2014

IEEE Trans. Aerosp. Electron. Syst.

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Using the dynamic inversion philosophy, a nonlinear partial integrated guidance and control approach is presented in this paper for formation flying. It is based on the evolving philosophy of integrated guidance and control. However, it also retains the advantages of the conventional guidance then control philosophy by retaining the timescale separation between translational and rotational dynamics explicitly. Simulation studies demonstrate that the proposed technique is effective in bringing the vehicles into formation quickly and maintaining the formation.

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Partial Integrated Guidance and Control of Interceptors for High-Speed Ballistic Targets

Cited by 58 publications

References 11 publications

Initial states iterative learning for three-dimensional ballistic endpoint control

Initial states iterative learning for three-dimensional ballistic endpoint control

Fast Robust Integrated Guidance and Control Design of Interceptors

Formation flying with nonlinear partial integrated guidance and control

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