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In this paper, a multivariable nonlinear and minimum phase model of the AHU is considered in the presence of uncertainties.
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The design of a nonlinear robust controller for a non-minimum phase model of an air-breathing hypersonic vehicle is presented in this work.
As a digitized continuous-time system with a very small sampling period may become an unstable and/or non-minimum phase model, even if the original system is stable and/or minimum phase.
Thus if the resultant FIR model is not minimum phase the IIR model stability becomes a major issue.
Typically the controller consists of an identifier (or tuner) which is used to adjust the parameters of a linear time-invariant (LTI) compensator, and under suitable assumptions on the plant model uncertainty it is proven that good asymptotic behaviour is achieved, such as model matching (for minimum phase systems) or stability.
Initial model generation using minimum phase residue method; 18.
We derive charge-balanced minimum-power controls for a general phase model using the maximum principle, where the cases with unbounded and bounded control amplitude are examined.
A minimum phase approximation of the non-minimum phase nominal plant model is proposed by using Genetic Algorithm (GA) to tackle the internal stability problem of the DOB-based robust control systems.
The overall result of this work shows that the diurnal vTEC prediction performance of the model is generally better during the solar minimum phase than during solar maximum phase.
The control structure for the minimum phase system without infinite gain loop is clarified and a design method of model reference adaptive1 control system is proposed.
Suppose that the image can be modeled as an Auto Regressive Moving Average (ARMA) process with a known power spectrum such that where is the minimum phase spectral factor and is the variance of the white noise driving the model.
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