Robustified fractional-order controller based on adjustable fractional weights for a doubly fed induction generator

Abstract

In this paper, a robustification method of the primary fractional controller is proposed. This novel method uses the adjustable fractional weights on the H∞ mixed-sensitivity problem. It can achieve an enhancement in both nominal performance and robust stability margins for the uncertain plants while respecting the frequency-domain constraints, such as the tracking of the set-point references, load disturbance attenuation and measurement noise suppression. The proposed robustification holds two steps; in the first step, a primary fractional controller is designed from solving the H∞ mixed-sensitivity problem that uses fixed-integer weights. In the second step, the robustified fractional controller with adjustable fractional weights is designed in order to guarantee a good compromise between the nominal performance and the robust stability not only for the nominal plant, but also for all set of the neighbouring plants. The proposed robustified fractional controller is used to control the doubly fed induction generator. Its dynamic is modelled by the unstructured output-multiplicative uncertain plant. Simulation results given by both primary and robustified fractional controllers are compared in time and frequency domains with those given by the conventional integer H∞ controller in order to validate the effectiveness of the proposed method.

A robust performance enhancement of primaryH∞controller based on auto-selection of adjustable fractional weights: Application on a permanent magnet synchronous motor

“…On the other hand, the R S 0 condition is violated if the singular values plot of σ max [ S c 0 ( ω ) ] exceeds its upper bounds, σ max [ W T 0 ( s ) ] − 1 at any frequency point. Accordingly, a trade-off condition between N P 0 and R S 0 cannot be satisfied when either N P 0 or R S 0 condition is violated in any frequency point (Aidoud et al, 2016; Sedraoui et al, 2017). A good trade-off between N P 0 and …”

“…Recently, various advanced control strategies such as Neural Network Control (Kim and Lewis, 1999), fuzzy logic control (Lian et al, 2001), adaptive neuro-fuzzy control (Hwang, 2004; Ranjbar-Sahraei, 2012) and others have been proposed in the literature for ensuring the RP with high secure margin when the parameters of the model change in a wide range. Unfortunately, the obtained robustness properties cannot be shown in frequency domains due to no existing analytic expression for obtained controller (Kanade and Mathew, 2013, Sedraoui et al, 2017). Therefore, the obtained sensitivity functions cannot be analyzed in frequency domain.…”

“…Nevertheless, optimal integer weights cannot be guaranteed because the convergence of the optimization algorithm slows down as the optimal solution approaches. This is generally due to the high number of selected integer weights or specifications to be quantified (Amieur et al, 2017; Sedraoui et al, 2017).…”

Fractional-order Kalman filters for continuous-time fractional-order systems involving correlated and uncorrelated process and measurement noises

“…The controller design becomes flexible with the introduction of fractional-order operators, such as fractional-order sliding-mode controllers (Pashaei and Badamchizadeh, 2016; Zhong et al, 2016), fractional-order iterative learning controllers and fractional-order P I λ D μ controllers (Bettayeb et al, 2017; Lamba et al, 2017; Mandic et al, 2017; Pan et al, 2016). With the development of physics and technology, many researchers have found that fractional-order systems can reveal the dynamic characteristics of many physical systems (Liu et al, 2017; Lü et al, 2017; Luo, 2015; Sedraoui et al, 2016; Wei et al, 2017). Fractional-order calculus is also considered a novel topic and has gained considerable popularity over the past three decades; it has been the subject of specialized conferences, owing to its demonstrated applications in widespread fields of science and engineering (Wang et al, 2017a,c).…”

Optimal power tracking of doubly fed induction generator-based wind turbine using swarm moth–flame optimizer

“…Several studies have been carried out to investigate the robustness improvement of DFIG subject to various disturbances, e.g. internal model state-feedback control strategy considering unmodelled high-frequency system transients (Taveiros et al, 2015), an adjustable fraction weight-based robustified fractional order controller (Sedraoui et al, 2016) and multivariable generalized predictive control incorporated with robust H ∞ control (Mohammed et al, 2016), which aimed to improve the dynamical performance of DFIG.…”