University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Neural network approach to continuous-time direct adaptive optimal control for partially unknown nonlinear systems

Abstract: In this paper we present in a continuous-time framework an online approach to direct adaptive optimal control with infinite horizon cost for nonlinear systems. The algorithm converges online to the optimal control solution without knowledge of the internal system dynamics. Closed-loop dynamic stability is guaranteed throughout. The algorithm is based on a reinforcement learning scheme, namely Policy Iterations, and makes use of neural networks, in an Actor/Critic structure, to parametrically represent the control policy and the performance of the control system. The two neural networks are trained to express the optimal controller and optimal cost function which describes the infinite horizon control performance. Convergence of the algorithm is proven under the realistic assumption that the two neural networks do not provide perfect representations for the nonlinear control and cost functions. The result is a hybrid control structure which involves a continuous-time controller and a supervisory adaptation structure which operates based on data sampled from the plant and from the continuous-time performance dynamics. Such control structure is unlike any standard form of controllers previously seen in the literature. Simulation results, obtained considering two second-order nonlinear systems, are provided.

Robust backstepping control of nonlinear systems using neural networks

Abstract: A controller is proposed for the robust backstepping control of a class of general nonlinear systems using neural networks (NNs). A tuning scheme is proposed which can guarantee the boundedness of tracking error and weight updates. Compared with adaptive backstepping control schemes, we do not require the unknown parameters to be linear parametrizable. No regression matrices are needed, so no preliminary dynamical analysis is needed. One salient feature of our NN approach is that there is no need for the off-line learning phase. Three nonlinear systems, including a one-link robot, an induction motor, and a rigid-link flexible-joint robot, were used to demonstrate the effectiveness of the proposed scheme.

Observation and properties of the X(3872) decaying to J/ψπ+π- in pp̄ collisions at √s = 1.96 TeV

Abstract: We report the observation of the X(3872) in the J/psipi(+)pi(-) channel, with J/psi decaying to mu(+)mu(-), in p (p) over bar collisions at roots=1.96 TeV. Using approximately 230 pb(-1) of data collected with the Run II D0 detector, we observe 522+/-100 X(3872) candidates. The mass difference between the X(3872) state and the J/psi is measured to be 774.9+/-3.1(stat)+/-3.0(syst) MeV/c(2). We have investigated the production and decay characteristics of the X(3872) and find them to be similar to those of the psi(2S) state.

Performance of the ATLAS Trigger System in 2010

Abstract: Proton-proton collisions at root s = 7 TeV and heavy ion collisions at root(NN)-N-s = 2.76 TeV were produced by the LHC and recorded using the ATLAS experiment's trigger system in 2010. The LHC is designed with a maximum bunch crossing rate of 40 MHz and the ATLAS trigger system is designed to record approximately 200 of these per second. The trigger system selects events by rapidly identifying signatures of muon, electron, photon, tau lepton, jet, and B meson candidates, as well as using global event signatures, such as missing transverse energy. An overview of the ATLAS trigger system, the evolution of the system during 2010 and the performance of the trigger system components and selections based on the 2010 collision data are shown. A brief outline of plans for the trigger system in 2011 is presented.