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TianQin is a planned space-based gravitational wave (GW) observatory consisting of three Earth-orbiting satellites with an orbital radius of about $10^5 \, {\rm km}$. The satellites will form an equilateral triangle constellation the plane of which is nearly perpendicular to the ecliptic plane. TianQin aims to detect GWs between $10^{-4} \, {\rm Hz}$ and $1 \, {\rm Hz}$ that can be generated by a wide variety of important astrophysical and cosmological sources, including the inspiral of Galactic ultra-compact binaries, the inspiral of stellar-mass black hole binaries, extreme mass ratio inspirals, the merger of massive black hole binaries, and possibly the energetic processes in the very early universe and exotic sources such as cosmic strings. In order to start science operations around 2035, a roadmap called the 0123 plan is being used to bring the key technologies of TianQin to maturity, supported by the construction of a series of research facilities on the ground. Two major projects of the 0123 plan are being carried out. In this process, the team has created a new-generation $17 \, {\rm cm}$ single-body hollow corner-cube retro-reflector which was launched with the QueQiao satellite on 21 May 2018; a new laser-ranging station equipped with a $1.2 \, {\rm m}$ telescope has been constructed and the station has successfully ranged to all five retro-reflectors on the Moon; and the TianQin-1 experimental satellite was launched on 20 December 2019—the first-round result shows that the satellite has exceeded all of its mission requirements.

This brief concerns model and noise design relevant to drag-free and attitude control of the European satellite gravity field and steady-state ocean circulation explorer (GOCE) under different control modes. As the model must include accurate dynamics of disturbance and measurement drifts to be rejected/estimated, noise design aims to select, and to mark with the Boolean variables of the control modes, the necessary and sufficient feedback channels (noise estimator) connecting model error to noise, which are the paths through which model state variables can be updated in real-time. Noise design is applied to a generic model encompassing position and attitude control, fed by position, rate, attitude, and acceleration sensors. The resulting closed-loop becomes a state predictor, providing controllable and disturbance states to drag-free and attitude control law, switching smoothly from mode to mode. Noise estimator gains are tuned to robust performance and stability by properly assigning closed-loop eigenvalues. Tuning details and simulated results illustrating the different modes are provided.

The paper concerns Drag-Free and Attitude Control of the European satellite Gravity field and steady-state Ocean Circulation Explorer (GOCE) during the science phase. Design has followed Embedded Model Control, where a spacecraft/environment discrete-time model becomes the real-time control core and is interfaced to actuators and sensors via tuneable feedback laws. Drag-free control implies cancelling non-gravitational forces and all torques, leaving the satellite to free fall subject only to gravity. In addition, for reasons of science, the spacecraft must be carefully aligned to the local orbital frame, retrieved from range and rate of a Global Positioning System receiver. Accurate drag-free and attitude control requires proportional and low-noise thrusting, which in turn raises the problem of propellant saving. Six-axis drag-free control is driven by accurate acceleration measurements provided by the mission payload. Their angular components must be combined with the star-tracker attitude so as to compensate accelerometer drift. Simulated results are presented and discussed.Key words: satellite, drag-free, attitude, control, Embedded Model.inertia, and i , aligned with the cylinder axis, points toward v . O J j J i J k LEO sun-synchronous orbit O k k i O j v Sun O i Earth rotation centred equatorial frame { } , , , J J J

Robust control design guarantees closed-loop stability of a model-based control law in the presence of parametric uncertainties. Stability is guaranteed by introducing some ignorance coefficients and restricting the feedback control effort. Embedded Model Control shows that the model-based control law can be kept intact in the case of uncertainty, if the controllable dynamics is complemented by a suitable disturbance dynamics. The disturbance state must be driven by an unpredictable input vector, the noise, which can only be estimated from the model error i.e. the difference between plant and model output. The uncertainty-based design concerns the noise estimator, so as to prevent the model error from conveying uncertainty components which are command-dependent and thus prone to destabilizing the controlled plant. Separation of the components in the low and high frequency domain by the noise estimator itself allows stability recovery and guarantee, and the rejection of low frequency components. Two simple case studies help to understand the key assets of the methodology.