440 research outputs found
Alpha Magnetic Spectrometer (AMS02) experiment on the International Space Station (ISS)
The Alpha Magnetic Spectrometer experiment is realized in two phases. A
precursor flight (STS-91) with a reduced experimental configuration (AMS01) has
successfully flown on space shuttle Discovery in June 1998. The final version
(AMS02) will be installed on the International Space Station (ISS) as an
independent module in early 2006 for an operational period of three years. The
main scientific objectives of AMS02 include the searches for the antimatter and
dark matter in cosmic rays. In this work we will discuss the experimental
details as well as the improved physics capabilities of AMS02 on ISS.Comment: 13 pages, 26 figures, Invited talk given at Shanghai Institute of
Nuclear Researc
Successive Convexification of Non-Convex Optimal Control Problems and Its Convergence Properties
This paper presents an algorithm to solve non-convex optimal control
problems, where non-convexity can arise from nonlinear dynamics, and non-convex
state and control constraints. This paper assumes that the state and control
constraints are already convex or convexified, the proposed algorithm
convexifies the nonlinear dynamics, via a linearization, in a successive
manner. Thus at each succession, a convex optimal control subproblem is solved.
Since the dynamics are linearized and other constraints are convex, after a
discretization, the subproblem can be expressed as a finite dimensional convex
programming subproblem. Since convex optimization problems can be solved very
efficiently, especially with custom solvers, this subproblem can be solved in
time-critical applications, such as real-time path planning for autonomous
vehicles. Several safe-guarding techniques are incorporated into the algorithm,
namely virtual control and trust regions, which add another layer of
algorithmic robustness. A convergence analysis is presented in continuous- time
setting. By doing so, our convergence results will be independent from any
numerical schemes used for discretization. Numerical simulations are performed
for an illustrative trajectory optimization example.Comment: Updates: corrected wordings for LICQ. This is the full version. A
brief version of this paper is published in 2016 IEEE 55th Conference on
Decision and Control (CDC). http://ieeexplore.ieee.org/document/7798816
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