Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach

Bououden, Sofiane; Chadli, Mohammed; Filali, S.; Hajjaji, A. El

doi:10.1016/j.renene.2011.06.025

Cited by 157 publications

(57 citation statements)

References 13 publications

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“…To obtain a good performance, a process model describing the effects of all the different inputs on all the outputs must be developed. Although the linear model predictive control is suitable for processes that are not highly nonlinear, this strategy has been applied in the control of nonlinear systems, whether for SISO systems [1][2][3] or for MIMO systems [4][5][6][7]. But many industrial processes have strong nonlinearities and predictive control is applied in order to provide satisfactory control results.…”

Section: Introductionmentioning

confidence: 99%

“…fuzzy logic is among the most used strategies in all areas [4]. Despite the fact that this strategy has been developed in the last few years, the fuzzy models of the TS type remain among the most used methods to deal with the MPC control for nonlinear systems (NMPC), because of their capacity to give an accurate approximation of the complex nonlinear MIMO systems.…”

Section: Introductionmentioning

confidence: 99%

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Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

2017

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This paper introduces a new development for designing a Multi-Input Multi-Output (MIMO) Fuzzy Optimal Model Predictive Control (FOMPC) using the Adaptive Particle Swarm Optimization (APSO) algorithm. The aim of this proposed control, called FOMPC-APSO, is to develop an efficient algorithm that is able to have good performance by guaranteeing a minimal control. This is done by determining the optimal weights of the objective function. Our method is considered an optimization problem based on the APSO algorithm. The MIMO system to be controlled is modeled by a Takagi-Sugeno (TS) fuzzy system whose parameters are identified using weighted recursive least squares method. The utility of the proposed controller is demonstrated by applying it to two nonlinear processes, Continuous Stirred Tank Reactor (CSTR) and Tank system, where the proposed approach provides better performances compared with other methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

2017

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“…Mostafa [18] presented a MPC method where the linear models are switched according to wind speed condition. A fuzzy model-based multivariable predictive control approach is also proposed in [21] to improve control performance. In the state-space models for the studies above, the wind speed within the control horizon is often regarded as constant, and wind variations are omitted in the optimization solving process.…”

Section: Introductionmentioning

confidence: 99%

A Flexible Maximum Power Point Tracking Control Strategy Considering Both Conversion Efficiency and Power Fluctuation for Large-inertia Wind Turbines

et al. 2017

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Abstract:In wind turbine control, maximum power point tracking (MPPT) control is the main control mode for partial-load regimes. Efficiency potentiation of energy conversion and power smoothing are both two important control objectives in partial-load regime. However, on the one hand, low power fluctuation signifies inefficiency of energy conversion. On the other hand, enhancing efficiency may increase output power fluctuation as well. Thus the two objectives are contradictory and difficult to balance. This paper proposes a flexible MPPT control framework to improve the performance of both conversion efficiency and power smoothing, by adaptively compensating the torque reference value. The compensation was determined by a proposed model predictive control (MPC) method with dynamic weights in the cost function, which improved control performance. The computational burden of the MPC solver was reduced by transforming the cost function representation. Theoretical analysis proved the good stability and robustness. Simulation results showed that the proposed method not only kept efficiency at a high level, but also reduced power fluctuations as much as possible. Therefore, the proposed method could improve wind farm profits and power grid reliability.

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“…In this study we focused in control of wind system formed by a turbine, a driving shaft and a double fed induction machine .As well known, the primary energy source of the wind generators is the wind which is unpredictable and varies frequently [9]. Wind energy has greatly progressed by becoming one of most renewable energy competitive [5].The control plays a very important role to make wind technology more profitable and reliable. Then an H∞ output feedback controller is designed by taking into account directly wind speed as perturbation [15].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Robust H∞ Tracking Control For Wind Generator System: LMI approach

Lahmadi

Boumhidi

2017

TMLAI

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This study concerns the tracking control problem of the wind turbine generator system with uncertainties parameters and external disturbances. Based on T S fuzzy model, a fuzzy observer based and a fuzzy robust state feedback output tracking control are developed to reduce the tracking error by minimizing the disturbance level caused by the wind speed. Using a Lyapunov function combined with H∞ tracking criteria and a judicious of the famous Young relation, a sufficient stability condition for the robust fuzzy tracking control formulated in terms of linear matrix inequality, which can be very efficiently solved by using LMI optimization techniques. The simulation results are given to show the performance of the observer based tracking controller.

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Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach

Cited by 157 publications

References 13 publications

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

Parameter Optimization of MIMO Fuzzy Optimal Model Predictive Control By APSO

A Flexible Maximum Power Point Tracking Control Strategy Considering Both Conversion Efficiency and Power Fluctuation for Large-inertia Wind Turbines

Fuzzy Robust H∞ Tracking Control For Wind Generator System: LMI approach

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