Design of Controls to Attenuate 

Loads in the Controls Advanced

Research Turbine

Preprint 

November 2003 • NREL/CP-500-35084 

A.D. Wright 

National Renewable Energy Laboratory

M.J. Balas 

University of Colorado at Boulder 

To be presented at the 2004 ASME Wind Energy 

Symposium 

Reno, Nevada 

January 5–8, 2004 

National Renewable Energy Laboratory 

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1 

DESIGN OF CONTROLS TO ATTENUATE LOADS IN THE CONTROLS ADVANCED 

RESEARCH TURBINE 

Alan D. Wright 

National Renewable Energy Laboratory 

1617 Cole Blvd. 

Golden, Colorado 80401 

Alan_wright@nrel.gov 

Mark J. Balas 

AIAA Fellow 

Department of Aerospace Engineering Science 

University of Colorado at Boulder 

Boulder, Colorado 80309-0429 

Mark.balas@colorado.edu 

ABSTRACT 

Designing wind turbines to maximize energy 

production and increase fatigue life is a major goal of 

the wind industry. To achieve this goal, we must 

design wind turbines to extract maximum energy and 

reduce component and system loads. This paper 

applies modern state-space control design methods to a 

two-bladed teetering-hub upwind machine located at 

the National Wind Technology Center∗

. The design 

objective is to regulate turbine speed in region 3 (above 

rated wind speed) and enhance damping in several lowdamped flexible modes of the turbine. The controls 

approach is based on the Disturbance Accommodating 

Control (DAC) method and provides accountability for 

wind-speed disturbances. First, controls are designed 

using the single control input rotor collective pitch to 

stabilize the first drive-train torsion as well as the tower 

first fore-aft bending modes. Generator torque is then 

incorporated as an additional control input. This 

reduces some of the demand placed on the rotor 

collective pitch control system and enhances first drive 

train torsion mode damping. Individual blade pitch 

control is then used to attenuate wind disturbances 

having spatial variation over the rotor and effectively 

reduces blade flap deflections caused by wind shear. 

INTRODUCTION

One of the main goals of wind turbine control is to 

increase power production and reduce loads using a 

minimum number of control inputs and required turbine 

 

∗

 This paper is declared a work of the U.S. Government and is not 

subject to copyright protection in the United States. 

measurements. Controls can often be designed to 

simultaneously satisfy more than one objective, i.e., 

regulate power and reduce loads. In the 1970s and 

1980s, classical control design methods, such as 

proportional integral, were used to design controllers to 

regulate power while adding damping to the first drive 

train torsional mode of the turbine [1]. In Barton et al. 

[2], a power system stabilizer was included to add 

damping to the drive train mode. 

Work has also been done in Europe using state-space 

methods for wind turbine control design. Mattson [3] 

designed a controller for regulation of power for a 

fixed-speed machine using blade pitch. In this work, 

rotor and generator rotation, drive train torsion, and 

tower fore-aft degrees of freedom (DOF) were modeled 

for use in control system design. Liebst [4] describes 

the use of individual blade periodic pitch control to 

reduce the loads on the Mod 0-A turbine caused by 

tower shadow, wind shear, and gravity. In Liebst, only 

blade DOF were modeled in the dynamics, using rigid 

blade/hinge models to represent the blade flap, lag, and 

pitch DOF. 

In the United States, Stol et al. [5] worked on the use of 

state-space methods to design Disturbance 

Accommodating Controls (DACs). They developed a 

linear model of a turbine using a rigid 

blade/tower/hinge approach to model blade and tower 

flexibility. They developed DAC from a linear model 

containing only rotor rotation as the DOF. They then 

showed that this DAC adequately controlled a turbine, 

as modeled in their nonlinear simulator-SymDyn with 

only the rotor rotation DOF. This system became 

unstable when more DOF were turned on in SymDyn 
2 

than included in the linear model for control design. In 

Wright and Balas [6], various flexible modes of a 

turbine were stabilized by appropriate control designs 

using rotor collective pitch. 

In a recent study, Stol and Balas [7] designed 

controllers with periodic gains to regulate turbine speed 

and reduce loads for a two-bladed teetering-hub 

machine. Further work in designing periodic controls 

for wind turbine speed regulation and load reduction 

has been reported by Stol [8]. 

The controls approach used in this paper is based on 

DAC and provides accountability for wind-speed 

disturbances. First, controls are designed using the 

single control input rotor collective pitch to stabilize the 

first drive train torsion as well as the tower first fore-aft 

bending modes. Generator torque is then incorporated 

as an additional control input. This reduces some of the 

demand placed on the rotor collective pitch control 

system and enhances first drive train torsion mode 

damping. Individual blade pitch control is then used to 

attenuate wind disturbances having spatial variation 

over the rotor and effectively reduces blade flap 

deflections caused by wind shear. 

We design controls based on linear models extracted 

from FAST [9]. After designing these controls, we test 

the closed-loop system through simulation using FAST. 

Finally, we draw conclusions and state future studies. 

We now review the turbine configuration. 

CART CONFIGURATION

The Controls Advanced Research Turbine (CART), 

shown in Figure 1, is a two-bladed, teetered, upwind, 

active-yaw wind turbine. This machine is used as a test 

bed for studying a number of aspects of wind turbine 

controls technology on medium to large-scale machines 

(see Table 1). 

The two bladed teetering upwind turbine operates 

variable speed with each blade capable of being 

independently pitched with its own electromechanical 

drive. Rated electrical power (600 kW at 42 RPM) is 

maintained in region 3 in a conventional variable speed 

approach. Power electronics are used to command 

constant torque from the generator and blade pitch 

controls the rotor speed. In region 2, the machine 

torque is varied to produce variable rotor speed in order 

to maintain optimum Cp . 

Table 1: CART Configuration 

Turbine Type Horizontal axis, upwind 

rotor, teetering hub

Number of Blades 2 

Rotor Speed (region 3) 42 rpm

Power Regulation Full-span blade pitch 

control

Yaw Configuration Upwind rotor with active 

yaw drive 

Rotor Diameter 43.3 m 

Height 36.6-m hub height

Coning 0° pre-cone

Tilt 4°

CONTROL DESIGN AND SIMULATION

Linear Models

Because we based all of our control design on linear 

control theory, we needed linear turbine models. We 

extracted linear models of a turbine from the FAST 

code [10]. These linear models contain a subset of the 

DOF allowable in FAST. 

These linear models can be expressed in state-space 

form as in Eq. (1). 

 (1)

D x Ax Bu u

y Cx

= + +Γ

=



where x is the state vector, u is the control input, Du

is the disturbance input, y is the measured output, 

A is the state matrix, B is the control input 

distribution matrix, Γ is the disturbance input 

distribution matrix, and C relates the measured output 

y to the turbine states. These vectors and matrices 

varied in size depending on the number of states and 

control inputs in the linear model. 

An example linear model used for control design 

includes generator speed as well as the first drive train 

torsion mode [10] and can be expressed as in Eq. (2). 
3 

[ ]

d d

1 1

2 d d2

3 3 d d

gen gen gen

rot rot rot rot rot

1

2

3

 - C ) C -1

x x

x K 0 -K x + 0 + 0 w 

x x C -C 0 0 1

 . 

(

I II I I

I II

x

y = 0 0 1 x

x

β

γ ζα

= δδ

               

     

     

 

 

 

   







 (2)

where, 

1 x = 4 δq , perturbed rotor speed, 

x2 = K ( d 4 15 δ δ q q − ) , perturbed drive train torsional 

spring force, 

x3 = 15 δq , perturbed generator speed. 

In this equation, the control input is u = δβ -perturbed 

rotor collective pitch, while the disturbance input is 

uD = δ w -perturbed wind disturbance (the uniform 

component over the rotor disk). The pitch input 

distribution matrix is rot

0

0

I

  ζ

 

 

=  

 

 

   

B

, while the disturbance 

input distribution matrix is rot

0

0

I

.

  α

 

 

Γ =  

 

 

   

. Here, ζ is the 

partial derivative of rotor aerodynamic torque with 

respect to rotor collective pitch angle, and α represents 

the partial derivative of rotor aerodynamic torque with 

respect to wind speed. In addition, in Eq. (2), Kd is 

the drive train torsional spring constant, while Cd is the 

torsional damping constant. rot I and gen I represent the 

rotational inertia of the rotor and generator about the 

spin axis, respectively. The torsional spring/damper 

connects the rotor to the generator modeled as lumped 

mass rotational inertia. 

The coefficients in this model may vary with turbine 

rotor speed, pitch angle, and wind speed. Figure 2 

shows the variation of aerodynamic torque with pitch 

angle for various wind speeds. For control design 

purposes, the linear model is evaluated at a particular 

turbine operating point called the control design point. 

The control system designed at a certain design point 

may perform poorly for other turbine operating points. 

It is important to test the designed control system 

through simulation for a variety of turbine operating 

points. One method of doing this is to input step winds 

into the turbine simulator, allowing the wind speed to 

vary above and below the control design point wind 

speed as shown in Figure 3. 

Disturbance Accommodating Control

We use DAC for the control designs shown in this 

paper. Disturbance accommodating control allows us 

to regulate turbine rotor speed while accounting for 

wind-speed disturbances and placing plant poles 

through full-state feedback [11]. It also allows us to 

use state estimation to provide the controller with 

values for those states that are not measured. This is 

important when limited turbine measurements are 

available. For the studies included in this paper, the 

only allowed turbine measurements are generator 

speed, tower-top fore-aft bending displacement, and 

blade-tip flap displacement. 

Another basic idea in DAC is that the wind disturbance 

gain is chosen to cancel or minimize the effect of windspeed disturbances. A variety of wind-speed 

disturbances can be modeled using DAC. To begin, we 

show control designs to accommodate wind-speed 

disturbances uniform over the rotor disk (have no 

spatial variation). For these disturbances, we can use 

the single control input rotor collective pitch, because 

these disturbances do not vary spatially over the rotor 

disk, and the pitch of each blade can be identical. 

Figure 4 shows a diagram of DAC for turbine speed 

regulation using rotor collective pitch. 

Rotor Collective Pitch Control

Rotor collective pitch control is used to regulate turbine 

speed in region 3 and enhance damping in the first drive 

train torsion mode as well as the tower first fore-aft 

bending mode [10] [12]. These modes are excited by 

wind disturbances uniform over the rotor disk. DAC is 

used to accommodate these wind-speed disturbances by 

modeling these disturbances as step functions [5] [11]. 

For most of the controls designed using rotor collective 

pitch, only generator speed is measured and state 

estimation is used to estimate plant states. 

Using DAC, plant poles can be placed using full-state 

feedback. Figure 5 shows the effect on regulated 

generator speed of placing plant poles to give high and 

low damping in the first drive train torsion mode, using 

the linear model shown in Eq. (2). The goal is to 

control generator speed to a 42-RPM set point. For the 
4 

low-damped case, oscillations in generator speed can be 

seen, corresponding to the first drive train torsion mode. 

These oscillations would cause an increase in fatigue 

loads in the drive train. 

Additional flexible modes of the turbine can be damped 

using rotor collective pitch if appropriate states are 

included in the linear model to describe these modes. 

One penalty of adding complexity to the linear model 

used for control design is that when more flexible 

modes are included in the model, the speed regulation 

becomes less robust in terms of performance. Figure 6 

compares speed regulation using two different 

controllers, one designed from a 3-state model and the 

other from a 7-state model (which includes the tower 

first fore-aft mode). In this figure, the turbine was 

excited with step changes in wind speed. The DAC 

design based on a linear model having only 3 states 

results in robust speed regulation to the 42-RPM set 

point over the full range of tested wind speeds. When 

additional flexible modes are included in the linear 

model for control design, the speed regulation becomes 

less robust. 

Figure 7 shows the benefit of including the tower first 

fore-aft mode in the linear control design model. When 

the model is excited by turbulent wind inflow, a 

dramatic reduction in tower-top fore-aft motion can be 

seen using the controller designed from the 7-state 

model, allowing significant damping to be added to the 

tower first fore-aft mode by the controller. These 

results are compared to a controller designed from a 

model neglecting this mode (the 5-state model, which 

includes rotor and generator speed, drive train torsion, 

and rotor first symmetric flap displacement and 

velocity). For the controller designed from the 7-state 

model (which includes the states in the 5-state model 

plus tower-top fore-aft deflection and velocity), a 

tower-top fore-aft displacement or acceleration 

measurement is needed. When only generator speed 

was measured, poor simulation results were obtained 

because of weak observability of the tower first fore-aft 

mode in the generator speed signal. 

An additional consideration is actuator duty, quantified 

in terms of blade pitch rates. Figure 8 shows simulated 

pitch actuator rates, using the controller designed from 

the 7-state linear model, when excited by turbulent 

wind inflow. These pitch rates sometimes exceed an 

actual limit of 18°/second set in the CART. This shows 

that the demand on the pitch control system is too high. 

We are performing several control objectives here: 

regulation of generator speed, enhanced damping of the 

first drive-train torsion mode, and enhanced damping of 

the tower first fore-aft mode. A consideration is 

whether additional control actuators can be used to 

perform the same function as the rotor collective pitch 

control system, thereby relieving some of the duty 

imposed on the pitch actuators. 

Generator torque is a possible control actuator. Perhaps 

generator torque control can be used to add damping to 

the drive train torsion mode, thus relieving some of the 

duty imposed on the pitch control system. 

Addition of Generator Torque Control

The primary goal in region 3 is to maintain rated power. 

This is done by maintaining constant generator torque 

(by commanding constant generator torque through the 

power electronics) and using rotor collective pitch to 

regulate aerodynamic torque, which regulates speed. 

If generator torque is now allowed to vary, there will be 

increased variation of generator power. Only small 

variations of generator torque can be allowed. The only 

function of the proposed generator torque control will 

be to add damping to the first drive train torsion mode, 

which should result in only small perturbations of 

generator torque about a mean value. Generator torque 

will not be used to regulate speed or perform any other 

function, as the generator torque excursions would then 

become too high for acceptable power regulation. 

The objectives of the pitch control system will be to 

regulate generator speed and enhance damping in the 

tower first fore-aft mode. We will remove the 

requirement of enhancing damping in the first drive 

train torsion mode from the rotor collective pitch 

control system as the generator torque control system 

now performs this function. 

Two separate controllers will be designed; the generator 

torque controller and the rotor collective pitch 

controller. The generator torque controller will be 

designed based on a reduced state-space model 

containing only the states needed to describe the first 

drive train torsion mode. This model can be expressed 

as in Eq. (3). 
5 

[ ]

d d

1 1

2 d d2

3 3 d d

gen gen gen gen

rot rot rot

1

2

3

 - C ) C -1

x x 0

x K 0 -K x + 0 

x x C -C -1 1

 . 

(

I II

I II I

x

y = 0 0 1 x

x

γ

= δ

               

     

     

 

 

 

   







gen T

 (3)

This system is much like the 3-state model shown in 

Eq. (2) with exactly the same states, except the control 

input is now δ gen T . Notice the control input gain 

gen

-1

I

in the 3rd row of B. This gain is constant ( gen I is 

constant) and does not change with wind speed or pitch. 

This means that controllability does not vary with wind 

speed and pitch as it does when rotor collective pitch 

control is used. Again, we measure only generator 

speed. 

The generator torque controller is designed from this 

state-space model. The poles are placed to add 

significant damping to the first drive train torsion mode, 

because the system is controllable. In addition, state 

estimation is made using generator speed as the only 

measurement, because the system is observable. 

The rotor collective pitch control system is designed 

from the linear models used previously, except that now 

the poles corresponding to the first drive train torsion 

mode are placed close to the open-loop values. This 

means that the rotor collective pitch control system 

does not add damping to this mode (this is now 

performed by the generator torque controller). The 

rotor collective pitch system is designed to regulate 

speed and accommodate wind disturbances using DAC. 

For collective pitch control designed from the 7-state 

model, tower-top fore-aft displacement or acceleration 

must also be measured. 

Figure 9 shows regulated speed from this controller as 

excited by turbulent wind inflow. A benefit is reduced 

pitch rates, as observed in Figure 10, when excited with 

turbulent wind inflow, using generator torque control 

versus pitch control to add drive train damping. Use of 

generator torque control removes the task of enhancing 

damping in the drive train mode from the pitch control 

system, thus reducing pitch rates. Figure 11 shows 

electrical power, showing higher excursions for the case 

of using generator torque control. A trade-off must be 

performed between the amount of damping added to the 

drive train from the generator torque control and the 

electrical power excursions. As the amount of damping 

is increased through pole placement, these excursions 

will increase. 

All of the controls designed so far have only 

accommodated wind disturbances uniform over the 

rotor disk. We now show control designs for 

attenuation of the effects of wind shear, using 

independent blade pitch control. 

Individual Blade Pitch Control

Rotor collective pitch control was adequate for 

accommodating uniform disturbances over the rotor 

disk. Control of the flap displacement of individual 

blades is not necessary in that case, because uniform 

wind inputs to the rotor excite only symmetric rotor 

modes, which can be controlled using rotor collective 

pitch. As soon as we allow wind disturbance 

components having spatial variation over the rotor disk, 

we need to control each blade independently. The goal 

of independent blade pitch control is to regulate turbine 

speed and reduce blade flap response in the presence of 

both uniform and spatially varying wind disturbances. 

An issue in independent blade pitch control is 

observability. When designing controls using only 

rotor collective pitch, we found that successful controls 

could be designed by only measuring generator speed 

(when tower fore-aft motion is ignored). The linear 

models used for control design contained the rotor 

symmetric flap mode, which is observable in the 

generator speed signal. Now that we are designing 

controls from linear models that contain the flap mode 

of each individual blade, the system is not observable 

when measuring only generator speed. Additional 

measurements are needed, such as the flap displacement 

of each blade. The best results are obtained when the 

flap displacement of each blade is measured and 

transformed into the rotor first asymmetric flap mode 

[10]. The improved control results are due to improved 

observability when directly measuring rotor first 

asymmetric flap. 

Another issue is the disturbances modeled with DAC. 

Before, when designing controls using rotor collective 

pitch, only uniform wind disturbances over the rotor 

disk were modeled. Now, when the wind is assumed to 

vary spatially over the rotor disk, a new disturbance 

term is needed. This disturbance term models the 

dominant component of wind shear, the once per 

revolution (1P) component that has spatial variation 
6 

over the rotor disk [10]. This new term has the form of 

Eq. (4). 

 ( sin ) . (4) hub

mr V h

Ψ

where r is the radial position along the blade from the 

center of the hub, Vhub is the wind speed at hub height 

h , m is the power law wind shear coefficient, and Ψ

is the blade azimuth angle [10]. 

We begin design of independent pitch control by using 

a linear model that describes just the flap displacement 

of each blade as well as rotor speed (5-state model), 

neglecting drive train torsion and tower fore-aft motion. 

These controls are tested using step changes in mean 

wind speed with a vertical wind shear profile 

superimposed. The coefficient m is assigned the value 

0.4, which represents a large variation in wind speed 

over the rotor disk. Figure 12 shows speed regulation 

and blade pitch using the new DAC. The pitch time 

series shows significant periodic behavior, 

demonstrating that the blade pitches to reduce the 

effects of wind shear. This is due to including the new 

disturbance term in the model. Figure 13 shows the 

flap deflection in response to the wind shear variation 

over the rotor disk. In the old DAC, the disturbance 

term shown in Eq. (4) has been ignored, while in the 

new DAC, this term has been included. The dramatic 

reduction in flap displacement when using the new 

DAC is very evident. The blade root flapwise bending 

moments are also significantly reduced using this new 

DAC, because these moments are directly related to the 

flap displacements. In these simulations, the drive train 

and tower were assumed to be rigid. 

We now add these DOF. We use a 9-state model, 

which includes the flap displacement and velocity of 

each blade, drive train torsion, generator and rotor 

speed, and tower first fore-aft mode displacement and 

velocity [10]. In addition, generator torque is used as a 

control actuator to enhance damping in the first drive 

train torsion mode. This DAC design will be shown to 

meet several control objectives simultaneously: 

regulation of turbine speed, enhanced damping and 

stabilization of several flexible modes such as the flap 

mode of each blade, the first drive train torsion mode, 

and tower first fore-aft mode. It will also successfully 

attenuate the 1P component of wind shear. 

This control algorithm is tested using turbulent wind 

inflow, with Figure 14 showing speed regulation at the 

42-RPM set point. In this controller, gains have been 

selected to place plant poles to give high damping in the 

drive train torsion and tower fore-aft bending modes. 

The gains corresponding to the wind-speed disturbances 

have been selected to give maximum attenuation of 

both the uniform and 1P components. We compare 

results from this controller to a controller designed with 

reduced gains, giving lower amounts of mode damping 

and disturbance attenuating effects. We compute power 

spectral densities (PSDs) of various outputs, including 

tower-top fore-aft deflection and blade-tip flap 

deflection. 

Figures 15, 16, and 17 show PSDs of the tower-top 

fore-aft displacement, blade-tip flap displacement, and 

shaft torque. The PSDs show peaks at frequencies 

corresponding to the tower first fore-aft bending mode, 

rotor rotational speed, and first drive train torsion mode. 

The higher gains result in reduced response at these 

frequencies, while the lower gains result in increased 

response. The load alleviating capability of the 

controller with high gains is very evident. 

Figure 18 shows the effect of high and low gains on 

blade pitch actuator rates. For the high-gain case, some 

of the pitch rates exceed the 18°/s limit imposed in the 

CART and must be reduced. A trade-off must be 

performed between allowable pitch rates and the 

amount of damping added to various flexible modes 

and amount of disturbance attenuation achieved by the 

controller. 

CONCLUSIONS

In this paper, rotor collective pitch control based on 

DAC was used to regulate turbine speed in region 3 and 

add damping to important flexible modes of the turbine. 

We concluded that it was possible to stabilize the first 

drive train torsional mode and the tower first fore-aft 

bending mode using only rotor collective pitch as the 

control input for the CART for these cases. By 

measuring only generator rotational speed, it was 

possible to use state estimation for the unmeasured 

states of the model containing the generator and rotor 

rotational speeds, drive train torsion, and rotor first 

symmetric flap DOF. When the tower first fore-aft 

mode was added, we needed an additional 

measurement: tower-top fore-aft deflection. 

Generator torque control was then added to add 

damping to the first drive train torsion mode. This 

reduced some of the demand on the rotor collective 

pitch control system, resulting in a modest reduction in 

pitch rates. Using generator torque to control the first 

drive train torsion mode results in excursions in 
7 

electrical power. A trade-off must be performed 

between allowable power excursions and the amount of 

damping added to the drive train mode by the 

controller. 

Finally, independent blade pitch control was introduced 

to regulate turbine speed and attenuate the effects of 

spatial variations of wind speed over the rotor disk 

(such as wind shear). A new disturbance was added to 

the DAC model to describe the dominant component of 

wind shear, the 1P component that has spatial variation 

over the rotor disk. A large reduction in blade-tip flap 

deflections was shown using this method. In addition, 

these control inputs stabilized the first flap mode of 

each blade, the first drive train torsion mode, and the 

tower first fore-aft mode. 

Independent blade pitch control required the rotor first 

asymmetric flap mode displacement to be measured. 

For the controls designed using rotor collective pitch 

control, blade flap displacement was not needed 

because the rotor first symmetric flap mode was 

observable in the generator speed measurement. When 

blade independent pitch is used, the rotor first 

asymmetric mode is contained in the linear model for 

control design. It was found that this mode is not 

observable by measuring only generator speed. The 

best controller performance occurred when the flap 

deflection of both blades was measured and 

transformed to obtain a measurement of the rotor first 

asymmetric mode. The improved control results were 

due to improved observability when measuring rotor 1st

first asymmetric flap. 

FUTURE WORK

Directions for future work include the implementation 

and field-testing of these controls in the CART. 

Important issues may include effects of measurement 

noise and the need to convert these control algorithms 

into digital form. The effects of turbine property 

uncertainties must also be accounted for. 

Further studies need to be conducted to investigate 

controls for very flexible machines. As machines 

become much more flexible than the CART, periodic 

control design methods probably will be important. 

Other important issues include the attenuation of more 

complex turbulent wind inflow structures, which may 

require additional model complexity as well as turbine 

measurements. 

REFERENCES

[1] Hinrichsen, E.N., 1984, “Controls for Variable Pitch 

Wind Turbine Generators,” I.E.E.E. Transactions on 

Power Apparatus and Systems, Vol. 103, pp. 886-892. 

[2] Barton, R.S., Bowler C.E.J., and Piwko, R.J., 1979, 

“Control and Stabilization of the NASA/DOE MOD-1 

Two Megawatt Wind Turbine Generator,” American 

Chemical Society, 1979, pp. 325-330. 

[3] Mattson, S.E., 1984, Modeling and Control of Large 

Horizontal Axis Wind Power Plants. Ph.D Thesis. 

Lund, Sweden: Department of Automatic Control, Lund 

Institute of Technology. 

[4] Liebst, B.S., 1983, “Pitch Control System for Large 

Scale Wind Turbines.” J. Energy, 7 (2), pp. 182-192. 

 [5] Stol, K., Rigney, B., and Balas, M., 2000, 

“Disturbance Accommodating Control of a VariableSpeed Turbine Using a Symbolic Dynamics Structural 

Model.” Proceeding of the 2000 ASME Wind Energy 

Symposium, Reno, Nevada, 10-13 January, 2000. 

[6] Wright, A.D. and Balas, M. 2002, “Design of StateSpace-Based Control Algorithms for Wind Turbine 

Speed Regulation.” Proceeding of the 2002 ASME 

Wind Energy Symposium, Reno, Nevada, 14-17 

January, 2002. 

[7] Stol, K. and Balas, M. 2002, “Periodic Disturbance 

Accommodating Control for Speed Regulation of Wind 

Turbines.” Proceeding of the 2002 ASME Wind Energy 

Symposium, Reno, Nevada, 14-17 January, 2002. 

[8] Stol, K., 2001, “Dynamics Modeling and Periodic 

Control of Horizontal-Axis Wind Turbines.” PhD 

Thesis. University of Colorado: Department of 

Aerospace Engineering Sciences. 

[9] Jonkman, J.M., and Buhl, M.L., 2003, FAST User’s 

Guide Version 4.31. NREL Report NREL/EL-500-

29798. National Renewable Energy Laboratory, 

Golden, Colorado. 

[10] Wright, A.D., 2003, Control Design for Flexible 

Wind Turbines. Ph.D. Thesis. University of Colorado: 

Department of Aerospace Engineering Sciences. 
8 

[11] Balas, M.J., Lee, Y.J., and Kendall, L., 1998, 

“Disturbance Tracking Control Theory with 

Application to Horizontal Axis Wind Turbines.” 

Proceeding of the 1998 ASME Wind Energy 

Symposium, Reno, Nevada, 12-15 January, 1998. 

[12] Wright, A.D. and Balas, M. 2003, “Design of 

Modern Controls for the Controlled Advanced Research 

Turbine.” Proceeding of the 2003 ASME Wind Energy 

Symposium, Reno, Nevada, 6-9 January, 2003. 

Figure 1. The Controls Advanced Research

Turbine (CART). 
9 

Figure 5. Plot of FAST simulated CART

generator speed using DAC controller

designed from 3-state model for different

pole locations. 

Figure 7. FAST simulated CART towertop fore-aft deflection with turbulent

inflow using DAC control designed from

5-state and 7-state models. 

Figure 9. Plot of FAST simulated CART

generator speed when generator torque

control is used to add drive-train damping.

38

39

40

41

42

43

44

45

20 30 40 50 60 70

Time (sec)

Generator Speed (RPM)

Low Drive-train Damping

High Drive-train Damping

Figure 8. FAST-simulated pitch rates using

DAC control designed from 7-state model

with turbulent inflow. 

40

41

42

43

44

45

20 30 40 50 60 70

Time (sec)

Generator Speed (RPM)

7-state Model

3-state Model

Figure 6. Plot of FAST simulated CART

generator speed using DAC controller

designed from 3-state model for different

pole locations. 

-0.10

-0.05

0.00

0.05

0.10

20 60 100 140 180 220 260 300

Time (sec)

Tower-top Displacement (m)

5-state model

7-state model

Limits

-20

-10

0

10

20

30

20 60 100 140 180 220 260 300

Time (sec)

Pitch Rate (deg/s)

7-state model

38

39

40

41

42

43

44

45

20 60 100 140 180 220 260 300

Time (sec)

Generator Speed (RPM)

Pitch and Generator

Torque Control

Figure 10. Plot of FAST simulated CART

pitch rates, showing reduced pitch rates

when generator torque control is used to

add drive-train damping. 

Limits

-20

-10

0

10

20

30

20 60 100 140 180 220 260 300

Time (sec)

Pitch Rate (deg/s)

Drive-train Damping Pitch

Drive-train damping Generator Torque
10 

Figure 11. Plot of FAST simulated CART

electrical power, showing effects of using

generator torque control. 

500

550

600

650

700

20 60 100 140 180 220 260 300

Time (sec)

Electrical Power (kW)

Drive-train Damping-Generator

Drive-train Damping-Pitch

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

43 44 45 46 47 48 49 50

Time (sec)

Blade 1 Flap Deflection (m.)

Old DAC New DAC

Figure 13. FAST simulated blade flap

displacement excited by wind shear for old

and new DAC designed from 5-state

model. 

Figure 12. FAST simulated generator

speed and blade pitch, using DAC

designed from 5-state model. 

38

39

40

41

42

43

44

45

20 30 40 50 60 70

Time (sec)

Generator Speed 

(RPM)

4

8

12

16

20

24

28

32

Pitch (deg)

Generator Speed

Blade Pitch

Figure 15. Power spectral density of FAST

simulated tower-top fore-aft displacement,

using DAC designed from 9-state model

excited by turbulence. 

1.0E-07

1.0E-05

1.0E-03

1.0E-01

1.0E+01

0123

Frequency (Hz)

Blade-tip Flap Deflection 

(m^2/Hz)

No Shear Attenuation

Shear Attenuation

38

39

40

41

42

43

44

45

20 60 100 140 180 220 260 300

Time (sec)

Generator Speed (RPM)

Figure 14. FAST simulated generator

speed, using DAC designed from 9-state

model, excited by turbulence. 

1.0E-08

1.0E-06

1.0E-04

1.0E-02

0123

Frequency (Hz)

Tower-top Fore-aft 

Deflection (m^2/Hz)

Undamped Tower

Damped Tower

Figure 16. Power spectral density of FAST

simulated blade-tip flap Displacement,

Using DAC designed from 9-state model

excited by turbulence. 
11 

Figure 17. Power Spectral Density of

FAST simulated Shaft Torque, Using DAC

Designed from 9-state Model Excited by

Turbulence. 

Figure 18. Blade Pitch Rates for Original

Gains and Reduced Gains Cases Using

DAC Designed from 9-state Model. 

Pitch Rate 

Limit

-30

-20

-10

0

10

20

30

20 60 100 140 180 220 260 300

Time (sec)

Blade Pitch Rate (deg/s)

Original Gains

Reduced Gains

1.E-05

1.E-03

1.E-01

1.E+01

1.E+03

1.E+05

3456

Frequency (Hz)

Low-speed Shaft Torque 

((kN-m)^2/Hz)

Low drive-train damping

High drive-train damping
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Conference paper 

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Design of Controls to Attenuate Loads in the Controls Advanced Research Turbine: 

Preprint 

6. AUTHOR(S) 

Alan D. Wright, Mark J. Balas 

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WER43301 

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National Renewable Energy Laboratory 

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13. ABSTRACT (Maximum 200 words)

Designing wind turbines to maximize energy production and increase fatigue life is a major goal of the wind industry. To 

achieve this goal, we must design wind turbines to extract maximum energy and reduce component and system loads. This 

paper applies modern state-space control design methods to a two-bladed teetering-hub upwind machine located at the 

National Wind Technology Center∗. The design objective is to regulate turbine speed in region 3 (above rated wind speed) 

and enhance damping in several low-damped flexible modes of the turbine. The controls approach is based on the 

Disturbance Accommodating Control (DAC) method and provides accountability for wind-speed disturbances. First, controls 

are designed using the single control input rotor collective pitch to stabilize the first drive-train torsion as well as the tower first 

fore-aft bending modes. Generator torque is then incorporated as an additional control input. This reduces some of the 

demand placed on the rotor collective pitch control system and enhances first drive train torsion mode damping. Individual 

blade pitch control is then used to attenuate wind disturbances having spatial variation over the rotor and effectively reduces

blade flap deflections caused by wind shear. 

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wind turbine design methods; wind turbine control; wind turbine speed regulation 16. PRICE CODE

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