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