Robust control approach for input–output linearizable nonlinear systems using high‐gain disturbance observer

Liu, Yan; Söffker, Dirk

doi:10.1002/rnc.2889

Cited by 14 publications

(9 citation statements)

References 24 publications

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“…The standard deviation of the root bending moment data appears as 16.8% load reduction when IPC is compared with PI-baseline controller. Another performance criteria which considers error and control energy as proposed by Liu and Söffker (2014) is also used to compare the two control schemes. Here, the amount of energy exploited for each control scheme is compared with achieved load reduction within a given time window T .…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Multivariable Control of Large Variable-Speed Wind Turbines for Generator Power Regulation and Load Reduction

2015

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In this paper a multivariate control strategy for regulating generator power and minimizing rotor blade loads during high wind speed is proposed. Due to high demand on wind energy, the current trend is to manufacture larger wind turbines with higher ratings. Consequently, structural loads and flexibility increase as the turbines size increases. Notwithstanding these emerging challenges, wind turbines have to cope with seasonal and intermittent wind variability. For this reason, innovative control strategies to limit power generated to the rated value and to mitigate the structural load are required. To achieve these objectives, generator torque is held constant at the rated value, and a fusion of two control algorithms working in tandem is used for pitch control: A proportional-integral (PI) baseline controller is used to generate demanded collective pitch angle for regulating generator speed and independent pitch control (IPC) is utilized to reduce structural load. To compute the overall instantaneous turbine control input, collective pitch angle is added to the perturbed IPC pitch angle demand input. The core contribution of this paper is realization of a multi-objective control strategy using IPC. The performance of the proposed scheme is compared with that of PI controller. The results indicate improved performance in terms of controlling speed/power and mitigating structural loads.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Multivariable Control of Large Variable-Speed Wind Turbines for Generator Power Regulation and Load Reduction

2015

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“…These requirements can be realized by using LQR method with suitable weighting matrices

Q_{o b s} = ([], \begin{matrix} left rightarray \\ array I_{n} & array 0 \\ array 0 & array q I_{r} \end{matrix}), R_{o b s} = I_{m},

with

q

as scalar design parameter so that

false‖ L_{2} {false‖}_{F} ≫ false‖ L_{1} {false‖}_{F}

with

q ≫ 1

expressing “high‐gain.”…”

Section: Wind Turbine Modeling and Controlmentioning

confidence: 99%

“…with q as scalar design parameter so that ||L 2 || F ≫ ||L 1 || F with q ≫ 1 expressing ''high-gain.'' 27 The overall PIO-based FSF control system for wind turbines is shown in Figure 2. The wind turbine is assumed to operate only at regions II or III.…”

Section: Pi Observermentioning

confidence: 99%

Structural load mitigation control for wind turbines: A new performance measure

2020

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Summary Structural loads of wind turbines are becoming critical because of the growing size of wind turbines in combination with the required dynamic output demands. Wind turbine tower and blades are therefore affected by structural loads. To mitigate the loads while maintaining other desired conditions such as the optimization of power generated or the regulation of rotor speed, advanced control schemes have been developed during the last decade. However, conflict and trade‐off between structural load reduction capacity of the controllers and other goals arise; when trying to reduce the structural loads, the power production or regulation performance may be also reduced. Suitable measures are needed when designing controllers to evaluate the control performance with respect to the conflicting control goals. Existing measures for structural loads only consider the loads without referring to the relationship between loads and other control performance aspects. In this contribution, the conflicts are clearly defined and expressed to evaluate the effectiveness of control methods by introducing novel measures. New measures considering structural loads, power production, and regulation to prove the control performance and to formulate criteria for controller design are proposed. The proposed measures allow graphical illustration and numerical criteria describing conflicting control goals and the relationship between goals. Two control approaches for wind turbines, PI and observer‐based state feedback, are defined and used to illustrate and to compare the newly introduced measures. The results are obtained by simulation using Fatigue, Aerodynamics, Structures, and Turbulence (FAST) tool, developed by the National Renewable Energy Laboratory (NREL), USA.

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“…This additional degree of freedom improves the steady-state estimation accuracy and also the estimation robustness against unknown inputs. With the combination of PI-Observer and nonlinear control methods, robust nonlinear control could be obtained [4]. With increasing the gain of PI-Observer the performance would be increased [5], however the performance is influenced by the measurement noise.…”

Section: Introductionmentioning

confidence: 99%

High‐Gain Scheduling of the Proportional‐Integral‐Observer

Bakhshande

Söffker

2014

Proc Appl Math and Mech

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Estimation of states and unknown inputs simultaneously can be done by Proportional-Integral-Observer (PIO). This observer is used in nonlinear and robust control. It has additional integral feedback loop in comparison with the usual Luenberger observer. Increasing the gain of this observer the performance would be increased, however due to this high gain the performance is influenced by measurement noise. Advanced PI-Observer (API) algorithm is a method which is used for scheduling the optimal gain of PI-Observer based on the cost function and by using a bank of PI-Observer. In this contribution the proposed method obtains relative optimal gain by using a given cost function. Afterwards the absolute optimal gain is computed by changing the design parameters at each step based on the desired performance criteria. The absolute optimal gain is found instead of relative optimal gain in comparison with the API-Observer. Simulation results estimating contact forces acting to a vibrating elastic beam based on indirect measurements illustrate the improvement of the proposed method.

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Robust control approach for input–output linearizable nonlinear systems using high‐gain disturbance observer

Cited by 14 publications

References 24 publications

Multivariable Control of Large Variable-Speed Wind Turbines for Generator Power Regulation and Load Reduction

Multivariable Control of Large Variable-Speed Wind Turbines for Generator Power Regulation and Load Reduction

Structural load mitigation control for wind turbines: A new performance measure

High‐Gain Scheduling of the Proportional‐Integral‐Observer

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