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The existence conditions for these controllers take the forms of LMIs.
And then, a simple and effective anti-windup scheme, without an additional parameter, is designed for these controllers.
Synthesis method is used for the design of the three controllers, and analytical formulas are given for these controllers.
The classical tuning approach for these controllers is based on specifying a certain gain crossover frequency, a phase margin and a robustness to gain variations.
The performance characteristic considered for these controllers are settling time (ts), steady state error (Ess), rise time (tr) and maximum overshoot (Mp).
A novel structure is adopted for these controllers where the dynamics of the controller is affected by the continuous-time state vector and the sampled value of this vector.
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The use of machine learning techniques, such as evolutionary algorithms or artificial neural networks for the learning of these controllers allows to automate the design process.
Performance metrics for assessing the ability of these controllers to perform this mitigation are defined.
In this paper, a simple analytical method is proposed for tuning the parameters of these controllers.
The obtained results provide simple expressions for tuning these controllers.
For this plant, fuzzy confidence intervals are derived that allow representing the uncertainties associated with these disturbances, different performance objectives are tested, and a methodology for deriving these controllers is developed.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com