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This paper focuses on the orbit and formation control for the Next Generation Gravity Mission (NGGM), under study at the European Space Agency. In our past study an innovative integrated orbit/formation model (IFC) has been designed, introducing a novel set of Hill-type equations. The aim of this study is the refinement and the enhancement of the IFC architecture. The proposed solution is based on a modified state predictor plus an extended hierarchical and multi-rate structure of the control law, with respect to the preliminary design. Care was taken in the control design to reduce as much as possible the demanded extra-thrust effort. This improved control strategy has been shown to be far less sensitive to the initial formation perturbations as well as capable of keeping the formation variables stable within the required band, all over the 10-year mission, through a low-thrust authority in the order of few milli-newtons.

The identifiability (structure detectability and consistent parameter estimation) of linear multivariable stochastic system from the "joint process" statistics for general closed-loop characteristics is considered. Necessary and sufficient conditions for the identifiability of classes of systems are proven. Experimental tests to verify the identifiability and the use of dithers which guarantee it when the feedback is unknown are discussed.

The ESA space - astrometry mission Hipparcos will provide a uniform and high-precision stellar Catalogue of the whole sky. During 2.5 years 100,000 selected stars will be observed by a special telescope mounted on a satellite placed in earth stationary orbit and their observations will be processed on-ground by two independent scientific Consortia. The data processing requires that the star coordinates in the telescope focal plane be transformed into a common inertial reference frame. To correctly perform this transformation, the satellite three-axes attitude must be reconstituted within a specified accuracy - rms error < 0.1 arcsec on each angle-for the mission lifetime, by processing the data of the on-board attitude sensors: star mapper and gyros. In this paper, after an overview of the Hipparcos mission, the attitude reconstitution is formulated as a Gauss-Markov estimation problem and solved by processing only star mapper data. The Gauss-Markov procedure takes advantage from a guaranteed error linear model describing the attitude evolution over the desired time interval.

The paper is addressed to the approximation in norm of stochastic processes of arbitrary dimension by reduced order models. After a systematic review of the basic geometry of stationary process representation in Hilbert space, the approximation problem is developed along the following items:1)Using Hankel norm, a closed solution of the approximation problem is available in the literature, based on singular value decomposition. This offers the opportunity to compare diffe rent approaches based on Hankel matrix decomposition: i) factorization of the covariance Hankel matrix, proposed by Rissanen and Kailath and Faurre; ii) canonical correlation analy_ sis, recently discussed by Akaike.2)By singular value decomposition, the internal energetic structure of the process distinct from the canonical algebraic structure, is also elucidated.3)Often the process model is needed for prediction; this suggests an interesting extension to optimum predictor approximation.

The paper presents the major features of the ESPRIT Basic Research HIMAC, proposed to the EC after a thorough investigation of the state of the art of manufacturing systems control. HIMAC has followed a new approach, by developing a specific mathematIcs, the Manufactunng Algebra (MA), for modelling and controlling the production processes of discrete manufacturing systems. As a key feature, the MA models can be bottom-up aggregated starting from the very detailed levels used in simulation, thus making poSSIble a coherent model hierarchy at the base of the design and realization of Hierarchical control strategies and architectures. The formulation of the original mathematical approach and its validation criteria are outlined in the paper. For a deeper understanding, the mterested reader can take advantage of the list of references given at the end.

The paper presents a further original development of the Manufacturing Algebra aiming at describing the dynamics of the production procesess taking place in factories. Starting from the definition of events and event sequences, a theory of discrete-event dynamic systems is developed which is appropriate for modelling the time and space evolution of discrete (or by part) production processes, at any level of detail. The result is a set of state equations modellling the state evolution of the storage and production units of a factory under the action of a real-time production control.