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— The optimal recursive estimation problem for general time-variant descriptor systems is considered in this paper. We show that the filter recursion can be obtained as solution of appropriate data fitting problems. We can consider the fitting evolving the entire trajectory at once or consider a one step correction.
— This paper develops information filter and array algorithms for a linear minimum mean square error estimator of discrete-time Markovian jump linear systems. A numerical example for a two-mode Markovian jump linear system, to show the advantage of using array algorithms to filter this class of systems, is provided.
—In this note, the presence of impulsive responses in descriptor systems and how it relates to impulse controllability and impulse observ-ability is considered. It is shown that the equivalence between impulse controllability (observability) and the existence of an impulse eliminating semistate feedback (output injection) gain, although true for square(More)
In this paper, robotic systems when two or more underactuated manipulators are working in cooperative way are studied. The underactuation effects on object to be controlled and on load capacity of the cooperative arms are analyzed. A hybrid control of motion and squeeze force is proposed. For the motion control, a Jacobian matrix that relates the torques in(More)
This paper addresses the H<sub>&#x221E;</sub> robust control problem for robot manipulators using unit dual quaternion representation, which allows an utter description of the end-effector transformation without decoupling rotational and translational dynamics. We propose three different H<sub>&#x221E;</sub> control criteria that ensure asymptotic(More)
In this paper, the Kalman filter and the corresponding Riccati equation for discrete-time, time-variant descriptor systems are addressed in their most general formulation. A new “9-block” form for the optimal filter is derived using deterministic approach. This new expression, besides including one step delayed state, presents an interesting simple and(More)
This work addresses the problem of stochastic state estimation for hybrid Markovian switching systems. The proposed Multiple Hypotheses Mixing Filter (MHMF) combines the Generalized Pseudo Bayes' (GPB) multiple hypotheses tracking with the Interacting Multiple Model's (IMM) estimates mixing in order to improve performance, the later being a particular case(More)