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We present the calibration and background model for the Large Area X-ray Proportional Counter (LAXPC) detectors on-board AstroSat. LAXPC instrument has three nominally identical detectors to achieve large collecting area. These detectors are independent of each other and in the event analysis mode, they record the arrival time and energy of each photon that is detected. The detectors have a time-resolution of 10 µs and a dead-time of about 42 µs. This makes LAXPC ideal for timing studies. The energy resolution and peak channel to energy mapping were obtained from calibration on ground using radioactive sources coupled with GEANT4 simulations of the detectors. The response matrix was further refined from observations of the Crab after launch. At around 20 keV the energy resolution of detector is 10-15%, while the combined effective area of the 3 detectors is about 6000 cm 2 .

An output feedback structured model reference adaptive control law has been developed for spacecraft rendezvous and docking problems. The effect of bounded output errors on controller performance is studied in detail. Output errors can represent an aggregation of sensor calibration errors, systematic bias, or some stochastic disturbances present in any real sensor measurements or state estimates. The performance of the control laws for stable, bounded tracking of the relative position and attitude trajectories is evaluated, considering unmodeled external as well as parametric disturbances and realistic position and attitude measurement errors. Essential ideas and results from computer simulations are presented to illustrate the performance of the algorithm developed in the paper.

For a quadrotor, one can identify the two well-known inherent rotorcraft characteristics: underactuation and strong coupling in pitch-yaw-roll. To confront these problems and design a station-keeping and tracking controller, dynamic inversion is used. Typical applications of dynamic inversion require the selection of the output control variables to render the internal dynamics stable. This means that in many cases, perfect tracking cannot be guaranteed for the actual desired outputs. Instead, the internal dynamics of the feedback linearised system is stabilised using a robust control term. Unlike standard dynamic inversion, the linear controller gains are chosen uniquely to satisfy the tracking performance. Stability and tracking performance are guaranteed using a Lyapunov-type proof. Simulation with a typical nonlinear quadrotor dynamic model is performed to show the effectiveness of the designed control law in the presence of input disturbances.

Abstract. The Soft X-ray focusing Telescope (SXT), India's first X-ray telescope based on the principle of grazing incidence, was launched aboard the AstroSat and made operational on October 26, 2015. X-rays in the energy band of 0.3-8.0 keV are focussed on to a cooled charge coupled device thus providing medium resolution X-ray spectroscopy of cosmic X-ray sources of various types. It is the most sensitive X-ray instrument aboard the AstroSat. In its first year of operation, SXT has been used to observe objects ranging from active stars, compact binaries, supernova remnants, active galactic nuclei and clusters of galaxies in order to study its performance and quantify its characteriztics. Here, we present an overview of its design, mechanical hardware, electronics, data modes, observational constraints, pipeline processing and its in-orbit performance based on preliminary results from its characterization during the performance verification phase.

For a typical quadrotor model, one can identify the two well known inherent rotorcraft characteristics; underactuation and strong coupling in pitch-yaw-roll. To confront these problems and design a station-keeping tracking controller, dynamic inversion is used here. Typical applications of dynamic inversion require the selection of the output control variables to render the internal dynamics stable. This means that in many cases tracking can not be guaranteed for the actual desired outputs. Instead, here, the internal dynamics of the feedback linearized system is stabilized with a robust control term. Stability and tracking performance are guaranteed using a Lyapunov-type proof. The approach could be called "forward stepping" in contrast with the well known backstepping design. Simulation with a typical nonlinear quadrotor dynamic model is performed to show the effectiveness of the designed control law in the presence of noise and disturbances.

The aim of this paper is to present rigorous and efficient methods for designing flight controllers for unmanned helicopters that have guaranteed performance, intuitive appeal for the flight control engineer, and prescribed multivariable loop structures. Helicopter dynamics do not decouple as they do for the fixed-wing aircraft case, and so the design of helicopter flight controllers with a desirable and intuitive structure is not straightforward. We use an H 1 output-feedback design procedure that is simplified in the sense that rigorous controller designs are obtained by solving only two coupled-matrix design equations. An efficient algorithm is given for solving these that does not require initial stabilizing gains. An output-feedback approach is given that allows one to selectively close prescribed multivariable feedback loops using a reduced set of the states at each step. At each step, shaping filters may be added that improve performance and yield guaranteed robustness and speed of response. The net result yields an H 1 design with a control structure that has been historically accepted in the flight control community. As an example, a design for stationkeeping and hover of an unmanned helicopter is presented. The result is a stationkeeping hover controller with robust performance in the presence of disturbances (including wind gusts), excellent decoupling, and good speed of response. Nomenclature A = system or plant matrix a s = longitudinal blade angle B = control-input matrix b s = lateral blade angle C = output or measurement matrix D = disturbance matrix D in = inner-loop disturbance matrix D o = outer-loop disturbance matrix dt = disturbance G = nominal plant G s = loop-shaped plant K = static output-feedback gain matrix p = roll rate in the body-frame components Q = state weighting matrix q = pitch rate in the body-frame components R = control weighting matrix r = yaw rate in the body-frame components r fb = yaw-rate feedback U = velocity along the body-frame x axis ut = control input V = velocity along the body-frame y axis W = velocity along the body-frame z axis X = inertial position x axis x in t = inner-loop state vector x o t = outer-loop state vector Y = inertial position y axis y in t = inner-loop output vector y o t = outer-loop output vector Z = inertial position z axis zt = performance output = system L 2 gain in = L 2 gain inner loop o = L 2 gain outer loop = pitch angle = roll angle = yaw angle

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