Comparison between failure detection test signals for continuous systems and sampled-data systems

2006 ◽  
Author(s):  
D. Choe ◽  
S. L. Campbell ◽  
R. Nikoukhah
Keyword(s):  
Failure Detection ◽  
Continuous Systems ◽  
Data Systems ◽  
Sampled Data

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

1974 ◽  
Vol 96 (3) ◽  
pp. 315-321 ◽  
Author(s):  
G. Jumarie
Keyword(s):  
Stability Criterion ◽  
Feedback Gain ◽  
Continuous Systems ◽  
Mean Square ◽  
Data Systems ◽  
Sampled Data ◽  
Input Output ◽  
Output Stability ◽  
Lumped Parameter ◽  
The Mean

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.

A First Exposure to Discrete-Time Modelling of Linear Continuous Systems

1976 ◽  
Vol 13 (3) ◽  
pp. 271-278
Author(s):  
B. Haberland ◽  
S. S. Rao
Keyword(s):  
Discrete Time ◽  
Continuous System ◽  
Continuous Systems ◽  
Data Systems ◽  
Engineering Curriculum ◽  
Sampled Data ◽  
Convolution Integral ◽  
Discrete Time Models

The growing usage of discrete-time models to simulate continuous and sampled-data systems requires a fundamental and concise introduction in an engineering curriculum. In this paper the discrete-time models are developed from different approximations of the convolution integral representing the response of the continuous system. An example is presented to illustrate the merits of the various models.

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