Stability Criteria for Distributed Nonlinear Sampled-Data Systems Defined by Green’s Functions

1974 ◽  
Vol 96 (3) ◽  
pp. 315-321 ◽  
Author(s):  
G. Jumarie

Sampled-data, nonlinear, distributed systems, which exhibit a structure similar to that of the standard closed loop with lumped parameter, are investigated from the viewpoint of their input-output stability. These systems are governed by operational equations involving discrete Laplace-Green kernels. Their feedback gains are bounded by upper and lower values which depend explicitly on the time and the distributed parameter. The main result is: an input-output stability theorem is given which applies both in L∞ (O, ∞) and L2 (O, ∞). This criterion, which may be considered as being an extension of the ≪circle criterion≫, involves the mean square value on the bounds of the feedback gain. Stability conditions for continuous systems are derived from this result. In the special case of systems with distributed periodical time-varying feedback gains, a stability criterion is given which applies in Marcinkiewicz space M2 (O, ∞). This result which involves the mean square value of the feedback gain is generally less restrictive than the L2 (O, ∞) stability criterion mentioned above.

1991 ◽  
Vol 36 (1) ◽  
pp. 50-58 ◽  
Author(s):  
T. Chen ◽  
B.A. Francis

Spectral estimates obtained from randomly sampled data arrays incur excess variability over and above that arising from the stochastic character of the signal. In previous papers estimators have been derived for both the direct transform and the correlation plane methods. Expressions for the excess variability showed its dependence on the magnitude of the mean square of the data. Here we show how improved estimates can be obtained by filtering out parts of the spectral energy so that the actual analysis for other parts of the spectrum can be performed on data of smaller mean square. The variability associated with this filtering operation limits the improvement in the stability of estimates that can be achieved. Analytical expressions for the bias and variability of these new estimates are compared with numerical experiments on simulated data arrays.


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