Asymptotic tracking control for wind turbines in variable speed mode

Lin, Wei; Lu, Zongtao; Wei, Wei

doi:10.1109/jas.2017.7510568

Cited by 5 publications

(3 citation statements)

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“…Hence, finally, the augmented system state-space equations can be rewritten. By substituting (21) to (19), the state error equation becomes…”

Section: B the Augmented Systemsmentioning

confidence: 99%

“…Some researches about tracking control of step, ramp, and parabolic signals have been done. Some control techniques have been proposed, such as the Servo System with Polynomial Differential Operator [12], Coefficient Diagram Controller [13], State Feedback with One and a Half Integrator [14], Proportional Integral Derivative (PID) Controller [15], Type III PID Controller [16] [17], Fuzzy Controller [18], nonlinear tracking control [19] and backstepping [20].…”

Section: Introductionmentioning

confidence: 99%

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Tracking Control of High Order Input Reference Using Integrals State Feedback and Coefficient Diagram Method Tuning

et al. 2020

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The purpose of the research is tracking control to follow the input reference signal such as step, ramp, parabolic, and high order reference stably. The challenge is rising when high order input references, such as parabolic or polynomial, are used in the advanced system. Like in satellite and missile launcher systems, the triple integrator systems have to follow parabolic or polynomial trajectory with high stability required. The research proposed an integrals state feedback controller to combine simple state feedback control with cascade-layered integral control. The order of the input reference defines the structure and number of integrals used. Along with Coefficient Diagram Method in its tuning process, the proposed controller is guaranteed to have good stability and zero steady-state error. Simulation results and mathematical proofs of stability and zero steady-state error are provided on the paper. The proposed method can follow various input references such as the ramp, parabolic, polynomial, and higher-order reference based on the simulation and mathematical proofs. The stability using the pole location also shown the negative poles that give a stable system.

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“…Hence, finally, the augmented system state-space equations can be rewritten. By substituting (21) to (19), the state error equation becomes…”

Section: B the Augmented Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tracking Control of High Order Input Reference Using Integrals State Feedback and Coefficient Diagram Method Tuning

et al. 2020

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“…Li et al [10] applied a model predictive control (MPC) strategy to track the maximum power point. Lin et al [11] designed a nonlinear optimum rotor speed tracker to realize MPPT control. By comparing the MPPT control strategies with controlling generator torque and pitch angle, it can be concluded that controlling generator torque shows a better performance [12,13].…”

Section: Introductionmentioning

confidence: 99%

Maximum Power Point Tracking Control of Offshore Hydraulic Wind Turbine Based on Radial Basis Function Neural Network

Wang,

Du,

Chen

et al. 2024

Energies

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A maximum power point tracking control strategy for an affine nonlinear constant displacement pump-variable hydraulic motor actuation system with parameter uncertainty, used within an offshore hydraulic wind turbine, is studied in this paper. First, we used the feedback linearization method to solve the affine nonlinear problem in the system. However, offshore hydraulic wind turbines have strong parameter uncertainty characteristics. This conflict was resolved through the further application of RBF neural network adaptive control theory. So, we combined feedback linearization with RBF adaptive control as the control theory, and then two control laws were compared by setting the pump rate and rating as outputs, respectively. It is shown by the MATLABR2016a/Simulink emulation results that power control is smoother than speed and friendlier for electric networks. It is also shown by the emulation results, in terms of the undulatory wind speed condition, that the feedback linearization–RBF neural network adaptive control strategy has perfect robustness. According to the simulation results, the feedback linearization–RBF neural network adaptive control strategy adopts the RBF neural network to approach complex nonlinear models and solve the parameter uncertainty problem. This control law also avoids the use of feedback linearization control alone, which can result in the system becoming out of control.

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A nonlinear maximum power point tracking technique for DFIG-based wind energy conversion systems

Rezaei

2018

Engineering Science and Technology, an International Journal

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Asymptotic tracking control for wind turbines in variable speed mode

Cited by 5 publications

References 0 publications

Tracking Control of High Order Input Reference Using Integrals State Feedback and Coefficient Diagram Method Tuning

Tracking Control of High Order Input Reference Using Integrals State Feedback and Coefficient Diagram Method Tuning

Maximum Power Point Tracking Control of Offshore Hydraulic Wind Turbine Based on Radial Basis Function Neural Network

A nonlinear maximum power point tracking technique for DFIG-based wind energy conversion systems

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