vault backup: 2024-10-16 10:17:20
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@ -12,5 +12,6 @@
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"renderCitationsReadingMode": true,
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"renderCitationsReadingMode": true,
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"renderLinkCitations": true,
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"renderLinkCitations": true,
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"pullFromZotero": true,
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"pullFromZotero": true,
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"cslStyleURL": "https://raw.githubusercontent.com/citation-style-language/styles/master/ieee.csl"
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"cslStyleURL": "https://raw.githubusercontent.com/citation-style-language/styles/master/ieee.csl",
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"enableCiteKeyCompletion": true
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}
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}
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@ -20,7 +20,7 @@ Robust control as a field determines how resilient a control system is to a diff
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Robustness is dependent on two features: the characteristic to be guaranteed, and the set of reasonably possible perturbed plants $\mathcal{P}$. Usually the characteristic we're interested in is internal stability or performance. The possible set of plants, however, is less straightforward. The set $\mathcal{P}$ can be structured or unstructured. A structured set in this instance can be a discrete number of possible perturbed plants, or possibly a parametric study with a finite number of parameters. Let's consider an example.
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Robustness is dependent on two features: the characteristic to be guaranteed, and the set of reasonably possible perturbed plants $\mathcal{P}$. Usually the characteristic we're interested in is internal stability or performance. The possible set of plants, however, is less straightforward. The set $\mathcal{P}$ can be structured or unstructured. A structured set in this instance can be a discrete number of possible perturbed plants, or possibly a parametric study with a finite number of parameters. Let's consider an example.
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Suppose a plant representing a spring-mass-damper system is described as follows:
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Suppose a plant representing a spring-mass-damper system is described as follows @controltutorialsformatlab&simulinkInvertedPendulumSystem:
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$$ P = \frac{X(s)}{F(s)} = \frac{1}{ms^2 + bs +k}$$
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$$ P = \frac{X(s)}{F(s)} = \frac{1}{ms^2 + bs +k}$$
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(The disk multiplicative perturbation)
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(The disk multiplicative perturbation)
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