Ballistic Trajectory Modeling for Missile with Deflectable Nose

With the development of science and technology, significant changes have taken place in the mode of modern wars. Application of military operating concepts such as ‘surgical precision strike' and ‘decapitation strike' make higher demands on precision-strike weapons. Ballistic trajectory correction ammunition is being rapidly developed due to its lucrative combination of low cost, high cost-effectiveness ratio, high damage rate, and applicability of existing inventory ammunition. Ballistic control technology has distinct advantages both in cost saving and improvement of the ammunition operational performance. Nose deflection is a feasible, effective, and fast-response flight control mode. The nose part of a projectile can be deflected at a certain angle relative to the projectile body axis, including a pressure difference between the windward and leeward sides of the warhead and generating the respective aerodynamic control force. In this study, a two-rigid-body trajectory model is established based on the multiple rigid body system theory. The proposed model is used to predict the flight trajectory of a projectile with the deflectable nose. Finally, the nose deflection effect on the ballistic trajectory variation is analyzed. The research results obtained provide the theoretical basis for the development of adaptive control smart ammunition and its engineering applications.

In-beam tests of a PET demonstrator (LAPD) for hadrontherapy beam ballistic control: data comparison to Geant4 Monte-Carlo predictions

This paper describes a small prototype of an in beam PET like detector, named ”Large Acceptance Pixelized Detector” (LAPD), developed to test technical concepts for the ion range control in the context of cancer treatments using proton or ion beams. The mechanical characteristics of this detector together with the read-out electronics are first presented. Then, results of a first experiment, performed on a 65 MeV proton beamline, are reported. Finally, we discuss the ability of Geant4 Monte-Carlo to reproduce the experimental data.

Monkeys time their pauses of movement and not their movement-kinematics during a synchronization-continuation rhythmic task

A critical question in tapping behavior is to understand whether the temporal control is exerted on the duration and trajectory of the downward-upward hand movement or on the pause between hand movements. In the present study, we determined the duration of both the movement execution and pauses of monkeys performing a synchronization-continuation task (SCT), using the speed profile of their tapping behavior. We found a linear increase in the variance of pause-duration as a function of interval, while the variance of the motor implementation was relatively constant across intervals. In fact, 96% of the variability of the duration of a complete tapping cycle (pause + movement) was due to the variability of the pause duration. In addition, we performed a Bayesian model selection to determine the effect of interval duration (450–1,000 ms), serial-order (1–6 produced intervals), task phase (sensory cued or internally driven), and marker modality (auditory or visual) on the duration of the movement-pause and tapping movement. The results showed that the most important parameter used to successfully perform the SCT was the control of the pause duration. We also found that the kinematics of the tapping movements was concordant with a stereotyped ballistic control of the hand pressing the push-button. The present findings support the idea that monkeys used an explicit timing strategy to perform the SCT, where a dedicated timing mechanism controlled the duration of the pauses of movement, while also triggered the execution of fixed movements across each interval of the rhythmic sequence.

Iterative Learning in Ballistic Control: Formulation of Spatial Learning Processes for Endpoint Control

In this paper, we formulate and explore the characteristics of iterative learning in ballistic control problems, where the projectile experiences a constant gravitational force and a fluid drag force that is quadratic in speed. Three scenarios are considered in the spatial learning process, where the shooting speed, shooting angle, or their combination, are, respectively, the manipulated variables. The viewed endpoint displacement is the controlled variable. Under the framework of iterative learning control, ballistic learning convergence is derived in the presence of process uncertainties. In the end, an illustrative example is provided to verify the validity of the proposed ballistic learning control schemes.