Impulsive observer‐based output control for PMSG‐based Wind Energy Conversion System

Vinodkumar, A.; Mani, Prakash; Joo, Young Hoon

doi:10.1049/iet-cta.2018.6045

Cited by 18 publications

(9 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…and R ≜ R −1 > 0. Moreover, it is easily verified that the conditions (12) and (28) can be ensured by inequalities (38). Therefore, if the conditions (37) and (38) in Theorem 3 holds, the criteria (12) and (13) in Theorem 1 and (28) and (29) in Theorems 2 can be guaranteed simultaneously.…”

Section: ℒ I I I … I I I I … I I … I Imentioning

confidence: 86%

“…Moreover, it is easily verified that the conditions (12) and (28) can be ensured by inequalities (38). Therefore, if the conditions (37) and (38) in Theorem 3 holds, the criteria (12) and (13) in Theorem 1 and (28) and (29) in Theorems 2 can be guaranteed simultaneously. □ Remark 3: It is noted from (13) and (29) that the Lyapunov matrices P, R and the control gains K j are coupled in non-convex terms H T K j T B i T PB i K j H and (GHB i K j H) T RGHB i K j H, which is a key obstacle in the SOF control design.…”

Section: ℒ I I I … I I I I … I I … I Imentioning

confidence: 86%

“…To handle these terms, a simple method is to remove them from the inequalities (20). This yields the membership function independent (MFI) stability conditions (12) and (13) in Theorem 1. Although these MFI criteria may be conservative, they require less computation for seeking a set of feasible solutions.…”

Section: Stability Analysismentioning

confidence: 99%

“…Takagi-Sugeno (T-S) fuzzy modelling method has been extensively employed to denote processes with complex nonlinearities. In the T-S fuzzy model, the non-linear dynamics is represented by a set of local linear subsystems blended via membership functions (MFs) [10][11][12][13][14][15]. By using this T-S fuzzy modelling strategy, much research has been carried out on the fundamental problems of stability analysis and control synthesis [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Static output feedback sliding mode control under rice fading channel: an interval type‐2 fuzzy modelling method

Zhang

Niu

Chen

2020

IET Control Theory & Appl

View full text Add to dashboard Cite

This work investigates the static output feedback sliding mode control (SMC) design problem of uncertain non-linear systems with Rice fading channels. The interval type-2 Takagi-Sugeno fuzzy modelling approach is exploited to express nonlinear dynamics with uncertain parameters. As the wireless network between the sensor and the controller may be subject to channel fading, the premise variables are probably altered during their propagations. In such cases, a key issue is to synthesise a desired SMC law for stabilising the controlled non-linear systems. To this end, new membership functions are constructed via employing the fading measurements and the desired SMC law are subsequently synthesised. To deal with the disturbances in communication channels, the notion of input-to-state stable in probability (ISSiP) is utilised and sufficient criteria are deduced to guarantee the ISSiP of the resultant closed-loop systems and the reachability of the prescribed sliding surface. Finally, a simulation example illustrates the designed control strategy.

show abstract

Section: ℒ I I I … I I I I … I I … I Imentioning

confidence: 86%

Section: ℒ I I I … I I I I … I I … I Imentioning

confidence: 86%

Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Static output feedback sliding mode control under rice fading channel: an interval type‐2 fuzzy modelling method

Zhang

Niu

Chen

2020

IET Control Theory & Appl

View full text Add to dashboard Cite

show abstract

“…Generally, a PMSG-based WT is connected through a full-scale voltage source converter, which is used to control the torque and the speed of the rotor. In this regard, control theory provides a clear insight into developing the convenient controller that involves the non-linear characteristics and results in maximum energy production with fewer leakages (Vinodkumar et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Neural network adaptive backstepping control via uncertainty compensation for PMSG-based variable-speed wind turbine: Controller design and stability analysis

et al. 2021

View full text Add to dashboard Cite

This paper proposes a design scheme along with stability analysis of a new adaptive backstepping controller designed for permanent magnet synchronous generator-based wind turbine, by using artificial neural network-based uncertainty compensation. The idea is to control the rotor speed and the mechanical power generated under internal and external nonlinear parametric uncertainties. An uncertain model of permanent magnet synchronous generator is designed. Then, two artificial neural network compensators are built to compensate such uncertainties in the current loops. The stability of the closed-loop system is studied according to the Lyapunov function. Simulations of the dynamic model are performed under both variable step and random wind speeds by using the DEV-C++ software, and the results are plotted with MATLAB. Compared to the classical direct torque control technique, the obtained results show the robustness of the proposed controller despite the presence of different uncertainties.

show abstract

Fuzzy sampled‐data control of PMSG‐based wind turbine systems with random occurring uncertainties

Shanmugam

Joo

2023

Adaptive Control & Signal

Self Cite

View full text Add to dashboard Cite

SummaryThis article investigates the robust stability of permanent magnet synchronous generator (PMSG)‐based wind turbine (WT) systems with randomly occurring uncertainties (ROUs) under a fuzzy sampled‐data control scheme. To accomplish this, the norm‐bounded uncertainties in the inductance, system inertia, and stator resistance are considered, which are modeled as ROUs by using certain Bernoulli distributed white noise sequences. The nonlinear PMSG‐based WT systems can be expressed as local linear systems with a Takagi–Sugeno (T–S) fuzzy approach. To achieve stability performance against such kinds of uncertainties and external disturbances, fuzzy‐based sampled‐data control is designed for the proposed model. Then, the sufficient conditions are derived and expressed in the form of linear matrix inequalities based on looped‐Lyapunov functionals, which ensure the asymptotic stability of the closed‐loop T–S fuzzy model for the nonlinear PMSG–based WTs with an performance index. Finally, the derived conditions are validated with the proposed model and confirm the effectiveness of the proposed control scheme against the uncertainties and disturbances. Moreover, a comparison example is conducted to show the less conservative nature of the derived conditions.

show abstract

Impulsive observer‐based output control for PMSG‐based Wind Energy Conversion System

Cited by 18 publications

References 32 publications

Static output feedback sliding mode control under rice fading channel: an interval type‐2 fuzzy modelling method

Static output feedback sliding mode control under rice fading channel: an interval type‐2 fuzzy modelling method

Neural network adaptive backstepping control via uncertainty compensation for PMSG-based variable-speed wind turbine: Controller design and stability analysis

Fuzzy sampled‐data control of PMSG‐based wind turbine systems with random occurring uncertainties

Contact Info

Product

Resources

About