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Expert schemes for the obtaining of discrete models for LTI systems using real sampling and parameter identification

De La Sen M., Garrido A.J., Barambones O., Maseda F.J.

Systems Science

2003

Vol. 29, nr 2

95-112

In this paper, we present an expert network scheme designed to obtain discrete transfer functions for LTI systems under real sampling of finite duration rather than an instantaneous ideal one. For this purpose, the expert network handles two different identification methods to derive parametric discrete models techniques of reduced mathematical complexity from measured input-output data series. One of the methods is based on a typically used least-squares minimization, while the other one is based on the Leverrier's algorithm; that is, using a data series of the impulse response of the system to identify a parametric discrete model. These techniques are of particular practical interest when the continuous-time system is unknown or when dealing with discrete-time systems whose analytical expression becomes very complex due, for instance, to the use of finite duration real sampling. The expert network improves the discretization process implementing a biestimation mechanism that switches to the model that provides a better performance at each estimation instant considered for different values of the hold order.

A new robust composite adaptive approach to the control of rigid robots which guarantees finite error convergence time.

Barambones O., Etxebarria V.

Systems Science

2000

Vol. 26, nr 3

69-81

In this paper a new robust adaptive control scheme for robotic manipulators is presented. The design consists of: 1) a composite adaptive controller, driven by both the tracking and the prediction errors, for learning the modelled dynamics; and 2) a nonlinear sliding mode control law that overcomes the unmodelled dynamics and noise. As a whole, the closed-loop system is proved to be stable and it is shown that the tracking error converges to zero in finite time.