In a recent paper, two least-squares (LS) based methods, which do not involve prefiltering of noisy measurements or parameter extraction, are established for unbiased identification of linear noisy input-output systems. This paper introduces more computationally efficient estimation schemes for the
Input-to-Output Linearization of Explicit Control Systems with Constrained Measurements
โ Scribed by K. Schlacher; A. Kugi
- Publisher
- John Wiley and Sons
- Year
- 2002
- Weight
- 89 KB
- Volume
- 1
- Category
- Article
- ISSN
- 1617-7061
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โฆ Synopsis
Input-to-Output Linearization of Explicit Control Systems with Constrained Measurements
Many design methods for non-linear control problems require the measurement of the whole state of the plant. This contribution deals with a refinement of the well established exact input-to-output linearization such that the control laws depend only on a predefined set of measurements. More precisely, we derive the determining equations for all static control laws, such that the input-to-output map from the new input to the new output is linear and such that the constraints concerning the measurements are met.
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