Although no known asteroid poses a threat to Earth for at least the next century, the catalogue of near-Earth asteroids is incomplete for objects whose impacts would produce regional devastation1,2. Several approaches have been proposed to potentially prevent an asteroid impact with Earth by deflecting or disrupting an asteroid1–3. A test of kinetic impact technology was identified as the highest-priority space mission related to asteroid mitigation1. NASA’s Double Asteroid Redirection Test (DART) mission is a full-scale test of kinetic impact technology. The mission’s target asteroid was Dimorphos, the secondary member of the S-type binary near-Earth asteroid (65803) Didymos. This binary asteroid system was chosen to enable ground-based telescopes to quantify the asteroid deflection caused by the impact of the DART spacecraft4. Although past missions have utilized impactors to investigate the properties of small bodies5,6, those earlier missions were not intended to deflect their targets and did not achieve measurable deflections. Here we report the DART spacecraft’s autonomous kinetic impact into Dimorphos and reconstruct the impact event, including the timeline leading to impact, the location and nature of the DART impact site, and the size and shape of Dimorphos. The successful impact of the DART spacecraft with Dimorphos and the resulting change in the orbit of Dimorphos7 demonstrates that kinetic impactor technology is a viable technique to potentially defend Earth if necessary.
In this paper, a supervised neural network training technique based on constrained optimization is developed for preserving prior knowledge of an input-output mapping during repeated incremental training sessions. The prior knowledge, referred to as long-term memory (LTM), is expressed in the form of equality constraints obtained by means of an algebraic training technique. Incremental training, which may be used to learn new short-term memories (STMs) online, is then formulated as an error minimization problem subject to equality constraints. The solution of this problem is simplified by implementing an adjoined error gradient that circumvents direct substitution and exploits classical backpropagation. A target application is neural network function approximation in adaptive critic designs. For illustrative purposes, constrained training is implemented to update an adaptive critic flight controller, while preserving prior knowledge of an established performance baseline that consists of classical gain-scheduled controllers. It is shown both analytically and numerically that the LTM is accurately preserved while the controller is repeatedly trained over time to assimilate new STMs.
A linear matrix inequalities (LMIs) framework for developing robust, adaptive nonlinear flight control systems is presented. The controller structure is that of a feedforward sigmoidal neural network that is both adaptive and reconfigurable, since the control law it approximates can be modified by updating its parameters during operation. The neural network controller is designed via LMIs to meet multiple control objectives, that include but are not limited to LQG and H∞ performance, pole placement, and closed-loop stability, as dictated by the application of interest. Prior knowledge of the linearized equations of motion is utilized in order to guarantee that the neural network controller meets these objectives when the aircraft is operating in its linear-parameter varying (LPV) regime, or steady-state flight envelope. However, should unexpected changes or failures occur during flight, the controller also is capable to reconfigure according to the new and, possibly, nonlinear dynamics. The adaptation consists of a constrained optimization problem that is computationally feasible because it takes place incrementally over time, accounting for the new dynamics only if and when they arise. The LPV performance of the controller is preserved and guaranteed throughout adaptation, by means of a novel constrained-training technique for neural networks.
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