A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Designing and Testing Controls 

to Mitigate Tower Dynamic 

Loads in the Controls Advanced 

Research Turbine 

Preprint 

A.D. Wright and L.J. Fingersh 

National Renewable Energy Laboratory 

K.A. Stol 

Mechanical Engineering Department 

University of Auckland 

Auckland, New Zealand 

To be presented at the 45th AIAA Aerospace Sciences Meeting and 

Exhibit, Wind Energy Symposium 

Reno, Nevada 

January 8–11, 2007 

Conference Paper 

NREL/CP-500-40932 

January 2007 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
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Designing and Testing Controls to Mitigate Tower Dynamic 

Loads in the Controls Advanced Research Turbine 

Alan D. Wright* †, Lee J. Fingersh‡

National Renewable Energy Laboratory 

Golden, Colorado 80401 

Karl A. Stol§

Department of Mechanical Engineering 

The University of Auckland 

Private bag 92019 

Auckland, New Zealand 

One goal of the U.S. Wind Energy Program is to design fatigue-resistant wind turbines. 

Control systems must be developed to mitigate structural loads to reduce costs and make 

wind energy a significant part of the U.S. electricity demand. Control systems that minimize 

fatigue loads without sacrificing turbine power while maintaining stable closed-loop 

behavior in the presence of turbulent wind inflow are critical for new large turbine designs. 

Classical design methods such as Proportional-Integral-Derivative control are used to 

design typical commercial wind turbine controllers. These methods are based on a single 

input and single output. As wind turbines become larger and more flexible, the potential for 

the control system to destabilize weakly damped flexible turbine modes increases. With 

modern advanced control design methods, multiple control objectives can be met with a 

single control loop, which leads to a stable closed-loop system. 

At the National Renewable Energy Laboratory we are designing, implementing, and 

testing advanced controls to maximize energy extraction and reduce structural dynamic 

loads. These control designs are based on a linear model of the turbine that is generated by 

specialized modeling software. In this paper we show the design, implementation and testing 

of a control algorithm to actively damp the tower’s fundamental side-side and fore-aft mode. 

This control algorithm combines generator torque and blade pitch control in a single control 

loop. 

I. Introduction 

Typical variable-speed wind turbines have different regions of operation. In below rated wind speeds (Region 2) 

blade pitch is held constant, and generator torque control is used to vary the speed of the turbine to maximize energy 

capture. In above-rated wind speeds (Region 3), generator torque is held constant, and blade-pitch control is used to 

limit aerodynamic power to maintain constant turbine speed. Another important goal of turbine control is to reduce 

structural dynamic loads in both operating regions. 

 

*

 Employees of the Midwest Research Institute under Contract No. DE-AC36-99GO10337 with the U.S. Dept. of 

Energy have authored this work. The United States Government retains, and the publisher, by accepting the article 

for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, 

worldwide license to publish or reproduce the published form of this work, or allow others to do so, for the United 

States Government purposes. †

 Senior Engineer, National Wind Technology Center, 1617 Cole Blvd., Mailstop 3811, AIAA member 

‡

 Senior Engineer, National Wind Technology Center, 1617 Cole Blvd., Mailstop 3811, AIAA nonmember 

§

 Lecturer, AIAA member

 

1
One method of reducing dynamic loads is to design controls to provide active damping of turbine components. 

Designing controls that reduce blade, tower, and drive-train dynamic loads—and thereby extend fatigue life—is 

particularly important. In Region 3, classical control design methods have been used to design controllers to add 

damping to the tower’s first fore-aft (f-a) mode with blade pitch.1,2 These methods use a notch filter to provide an 

additional pitch input in such a way as to increase the damping in that mode. Similar methods could also be used to 

actively damp the tower’s first side-side (s-s) mode with generator torque control, although this method is not 

discussed in the literature. A disadvantage of classical control methods is that multiple control loops must be used to 

simultaneously dampen several flexible turbine modes. If these controls are not designed with great care, these 

control loops interfere with each other and cause the turbine to become unstable. The potential to destabilize the 

turbine grows as turbines become larger and more flexible, and the degree of coupling between flexible modes 

increases. Using all the available turbine actuators in a single control loop to maximize load-alleviating potential 

would be advantageous. Advanced multi-input multi-output (MIMO) multivariable control design methods, such as 

those based on state-space models, can be used to meet these multiple control objectives and use all the available 

actuators and control inputs in a single control loop. 

In previous papers, we have shown the design of state-space controllers to regulate turbine speed in Region 3 and 

enhance damping of flexible turbine modes.3−6

 Those papers showed the advantages of using full-state feedback to 

place turbine plant poles to enhance transient response and increase stability. When limited turbine measurements 

are available, state estimation must be used. Without state estimation, every state contained in the linearized 

controller design model must be measured, which is not practical for most commercial turbines. Successful use of 

state estimation is based on a few turbine measurements such as generator speed and tower-top acceleration.In other 

work, state-space methods were used to design controls that maximize energy extraction and reduce fatigue loads in 

Region 2 using pitch control.7

 In that study, individual pitch control was shown through simulation to reduce blade 

fatigue damage equivalent loads (DELs) by 11%. 

Actual field implementations of these turbine controls have verified the capability of these state estimating 

controls to mitigate loads by using blade pitch as the control actuator.8−10 In addition, actual tests have shown that 

generator torque is an effective control actuator that actively damps the first drive-train torsion mode in Regions 2 

and 3.11 Other field implementations of controls on the Controls Advanced Research Turbine (CART) have used 

adaptive control techniques to improve Region 2 energy capture compared to the simpler Region 2 torque controls.12 

The tower bending modes also benefit from active damping. We are interested in the potential benefits of using 

the generator torque and blade pitch actuators together to actively damp the tower s-s and f-a bending modes. This 

control should be performed in a single control loop because of the large amount of coupling between these two 

modes. These controllers will require an additional measurement, such as tower-top acceleration or tower strain, to 

ensure that states representing the tower motion are estimated properly. 

In this paper we show the design, implementation and tests of a MIMO controller to add damping to the tower’s 

first f-a and first s-s bending modes, as well as the first drive-train torsion mode. The controller uses perturbations in 

generator torque and blade pitch to apply this active damping. Typical control inputs are generator speed and towertop f-a and s-s acceleration (although we started with tower strain data). We restrict attention in this paper to design 

of control for Region 2. The controller is based on full state feedback but uses state estimation based on these 

measurements. 

We describe steps taken in the control design process. We show implementation and tests of this controller on 

the CART at the National Wind Technology Center. We describe lessons learned from implementing these controls 

in the CART. We show load alleviating potential of this controller by comparing results to a baseline controller with 

no tower damping capability. We finally state conclusions and outline future work. 

II. CART Configuration 

The CART (Fig. 1) is a two-bladed, teetered, upwind, active-yaw wind turbine. This machine is used as a test 

bed to study aspects of wind turbine control technology for medium- to large-scale machines.13 

 

2
The CART is variable speed, and each blade is capable of being independently pitched with its own 

electromechanical servo. The pitch system can pitch the blades up to 18 degrees per second (deg/s) with pitch 

accelerations up to 150 degrees per second per second. The squirrel cage induction generator with full power 

electronics can control torque from minus rating (motoring) to plus rating (generating) at any speed. The torque 

control loop has a very high rated bandwidth of 500 radians per second (rad/s). 

Rated electrical power (600 kilowatts at a low-speed shaft 

[LSS] speed of 41.7 revolutions per minute [rpm]) is 

maintained in Region 3 in a conventional variable-speed 

approach. Power electronics are used to command constant 

torque from the generator and full-span blade pitch controls 

the rotor speed. 

The machine is equipped with a full complement of 

instruments that gather meteorological data at four heights. 

Blade-root flap and edge-strain gages, tower-bending gages, 

and low-speed shaft (LSS) and high-speed shaft (HSS) torque 

transducers gather load data. Accelerometers in the nacelle 

measure the tower’s f-a and s-s motion. Absolute position 

encoders gather data on pitch, yaw, teeter, LSS, and HSS 

positions. These data are sampled at 100 Hz. The custombuilt control system collects these data and controls the 

turbine at a control loop cycle rate of 100 Hz. This system is 

personal computer based and very flexible. 

For the baseline turbine controls, the generator torque is 

varied in Region 2 according to the expression 

Figure 1. The controls advanced research 

turbine (CART). 

 , (1) 2 T kw =

where T is generator torque, k is a constant, and w is rotor speed.13 In this region, blade pitch is constant, and the 

constant is chosen to maintain turbine operation at the tip-speed ratio (tsr) corresponding to maximum power 

coefficient Cp. This provides a simple way to design a generator torque controller for Region 2. 

k

Within the normal operating speeds of the CART, 

loads transmitted from the 2-bladed rotor (at two times 

the rotor rotation rate [2P]) to the tower can drive the 

tower at its first natural frequency. To alleviate this 

problem, we developed a strategy for the baseline 

controller to avoid operating it at a rotor speed that can 

cause this resonance.13 The tower resonance avoidance 

strategy employs the simple torque controller found in 

Fig. 2. This curve is designed to multiply the standard 

control law (depicted as the horizontal line) to give a 

final torque demanded vector. As rotor speed 

increases, the controller is designed to prevent the 

rotor speed from reaching the resonance point until 

aerodynamic torque is sufficient to allow the rotor to 

run through the resonance rapidly. When speed is 

decreasing, the opposite strategy is employed. Speed is 

kept above the resonance point until aerodynamic torque has fallen sufficiently to allow the machine to apply shaft 

torque of sufficient magnitude to rapidly slow the machine through the resonance point. 

Speed vector 

Torque vector 

Figure 2. Tower resonance avoidance strategy. 

Demanded torque with speed hysteresis is used to 

avoid operation at or near the resonance point. 

One challenge of wind turbine control design is meeting multiple control objectives. While maximizing energy 

in Region 2 and maintaining constant turbine speed in Region 3, we must also reduce dynamic loads to obtain longer 

 

3
turbine lifetimes and reduce the cost of energy. If controls are not carefully designed to account for the flexible 

modes of the wind turbine, we can actually increase dynamic loads by destabilizing low-damped modes. We now 

describe the design of state-space controls for Region 2 that allow us to meet the primary objectives in this region 

and enhance the damping in the tower’s first f-a and s-s modes, as well as the first drive-train torsion mode. 

III. State-Space Control Design 

We restrict our attention in this paper to control design in Region 2. The main goal in Region 2 is to control 

generator torque so the power is maximized. A second objective for our control is to mitigate tower-bending loads. 

We will use generator torque and blade pitch to add damping to the tower’s first s-s and f-a modes as well as the first 

drive-train torsion mode to reduce loads. 

In this paper, we perform overall generator torque control to maximize power by directly applying the torque 

expression in Eq. (1), which describes the baseline generator torque controller for the CART. Normally, pitch is held 

constant in this region. We apply the state-space controls as perturbations of torque and pitch on top of these values 

to add damping to the tower’s first f-a and s-s modes, as well as the first drive-train torsion mode. Only the tower 

and drive-train damping is performed with the state-space controller; the overall generator torque control to maintain 

optimum Cp is performed as in Eq. (1). These performance objectives drive the complexity of the state-space 

controller and the selection of turbine sensors for feedback. 

A linear time-invariant state-space model of the CART is described by 

x Ax Bu

y Cx Du

= +

= +

& (2) 

where u is the vector of control inputs: perturbed generator torque and perturbed collective blade pitch rate.9 x and 

y are the state vector and measurement vector respectively, described below. The state matrices (A, B, C, and D) 

are found by linearizing a FAST aero-elastic model of an operating point in Region 2.14 This operating point is 

defined by a rotor speed of 27.1 rpm and hub-height wind speed of 8.0 meters per second (m/s). In this context, 

“perturbed” refers to small deviations from the turbine linearization point. 

Twelve state variables in x are chosen to characterize the structural dynamics of interest. These state variables 

include 

st

st

st

st

st

perturbed tower 1 f-a bending deflection

perturbed tower 1 s-s bending deflection

perturbed drive-train 1 torsional deflection

perturbed tower 1 f-a bending velocity

perturbed tower 1 s-s bending 

 

x = st

velocity

perturbed generator speed

perturbed drive-train 1 torsional velocity

tower-top acceleration filter state 1

tower-top acceleration filter state 2

perturbed blade pitch angle

perturbed blade pitch 

 

rate

time integrated perturbed blade pitch angle 

⎡ ⎤

⎢ ⎥

⎢

⎢

⎢ ⎥

⎣ ⎦

⎥

⎥

 (3) 

We will further describe the tower-top acceleration filter states in the next section. 

 

4
The control input u consists of: 

perturbed generator torque

perturbed blade pitch rate u ⎡ = ⎢

⎣ ⎦

⎤

⎥

 (4) 

Five measurements in y are selected to ensure observability for state estimation. The measurements correspond 

to the output of physical sensors on the CART, including 

perturbed generator speed

perturbed blade pitch angle 

time-integrated perturbed blade pitch angle

perturbed tower-top fore-aft acceleration

perturbed tower-top side-side acceleration

y

⎡ ⎤

⎢ ⎥

⎢

= ⎢

⎢ ⎥

⎣ ⎦

⎥

⎥

 (5) 

Based on the state-space CART model, a state estimator is formed: 

ˆ ( )ˆ ++−= yKuBxKCAx& (6) 

where xˆ is the estimate of plant states. Full-state feedback is used to produce controller commands for generator 

torque and pitch rate. 

u Gx = ˆ (7) 

The control gains G and K are calculated using the linear quadratic regulation (LQR) method in MATLAB.15

This approach for gain calculation allows us to trade off state regulation with actuator effort. To add tower damping 

in particular, the states that correspond to tower-top rates of deflection are weighted most heavily. 

The block diagram in Fig. 3 illustrates how the state-space controller is implemented in simulation and with the 

actual CART machine. The conventional variable speed controller is still present to provide nominal generator 

torque commands for Region 2 control, based on Eq. (1). The state-space controller augments this generator torque 

signal to achieve the desired tower damping. 

Conventional 

Variable-Speed 

Controller CART 

State Estimator G Blade Pitch Rate

Added Gen. 

Torque

Nominal Generator 

Torque 

Total Gen. 

Torque y

y

y xˆ u

State-Space Controller 

Figure 3. State-space controller implementation. 

We designed, implemented, and tested simpler controllers to simplify implementation and work up in steps 

before we implemented and tested this “full-up” MIMO controller. We now describe these implementation steps. 

 

5
IV. Implementation Steps and Lessons Learned 

We performed several intermediate steps before we designed, implemented, and tested the full-up MIMO 

controller. One step was to design and implement separate generator torque and blade pitch controllers. We felt that 

designing and implementing simpler controllers would help uncover implementation issues before we progressed to 

the MIMO controller. We also wanted to assess the required generator torque and blade pitch perturbations 

necessary to actively damp the tower’s s-s and f-a motions. 

One problem we encountered was the use of accelerometer data as input to our controllers. Accelerometers are 

often plagued with noise problems, which introduce another degree of complexity to the control design and 

implementation process. Before employing tower-top acceleration as the control input, we decided to first use straingages close to the tower base as the inputs to the control algorithms. Figure 4 shows the tower and nacelle 

instrumentation that we used in the course of this study. The tower strain gages were mounted in the east/west and 

north/south directions and were fixed to the tower above the foundation. 

The first controller we designed and 

implemented on the CART used only the 

generator as a transducer to add damping to 

the tower first s-s mode, reading tower strain 

gage data (using tower bending moment as 

the control input). Even though we did not 

use pitch as an actuator to add damping to the 

tower’s first f-a mode, we included this mode 

in the linear model for control design. 

Including the tower 1st f-a mode was 

necessary so that the generator torque 

controller would have information regarding 

the first f-a mode to eliminate the possibility 

of destabilizing this mode. 

Nacelle Top View 

Accelerometer 

Locations. 

The solid one is 

the one used in 

this study 

(Port). 

Wind 

Fore-Aft 

acceleration 

Side-Side 

acceleration 

Wind 

Accelerometer 

Locations. 

Port and 

Starboard are 

on the bottom, 

and the center 

one is on top of 

the main 

bearing. 

Top of main 

bearing 

North-South 

and 

East-West 

strain gauge 

locations. 

Figure 4. Location of tower strain gages and 

accelerometers used for control inputs. 

The state-space model for this control 

consisted of states representing the tower first 

s-s, first f-a, first drive-train torsion mode, 

and generator speed. For this linear model, 

the state vector and control input were: 

1

2

3

st

st

st

st

4

5

x perturbed tower 1 f-a bending deflection

x perturbed tower 1 s-s bending deflection

x perturbed drive-train 1 torsional deflection

x perturbed tower 1 f-a bending velocity

x perturbed t

= 

= 

= 

= 

= 

x =

st

6

st

7

ower 1 s-s bending velocity

x perturbed generator speed

x perturbed drive-train 1 torsional velocity

= 

= 

⎡ ⎤ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢⎣ ⎥⎦

: perturbed generator torque Tgen u = δ . (8) 

 

6
The inputs to the state estimator controller were: 

perturbed generator speed

perturbed tower-base fore-aft bending moment

perturbed tower-base side-side bending moment

y

⎡ ⎤

⎢ = ⎢ ⎥

⎣ ⎦

⎥ (9) 

The strain gages are attached to the tower (fixed frame), so a transformation involving nacelle yaw position was 

used to transform these measurements to the f-a and s-s directions (in the nacelle frame), before inputting to the 

controller. 

We designed this controller from a linear turbine model generated by FAST14at the turbine operating point: 

Wind speed = 8 m/s 

Rotor speed = 27.1 rpm 

Pitch angle = -1 degree (normal Region 2 “run-pitch”). 

The goal of state-space control design is to use full state feedback to place the poles of the plant to obtain the 

desired stability and transient response. Without full state feedback, the eigenvalues of the matrix at this operating 

point have the values shown in Table 1 as the open-loop poles. We used LQR to design this controller.15 With full 

state feedback, and selection of appropriate LQR weights, the closed-loop poles are shown in Table 1. The largest 

weightings were placed on the states that corresponded to the tower’s first s-s mode as well as the drive-train first 

torsion mode. This resulted in increased damping, as can be seen from the real parts of these poles (moved farther to 

the left in the complex plane). We placed only small weighting on the states that corresponded to the tower’s first f-a 

mode as well as the generator speed state. 

Table 1. Open-loop and Closed-loop Pole Locations 

 Open-loop poles Closed-loop poles 

drive-train 1st torsion −.003 ± 22.47i (rad/s) −1.09 ± 22.47i (rad/s)

tower 1st f-a mode −0.034 ± 5.50i (rad/s) −0.034 ± 5.50i (rad/s)

tower 1st s-s mode −0.001 ± 5.54i (rad/s) −0.197 ± 5.54i (rad/s)

generator speed −0.03 −0.05

We first simulated turbine behavior with this controller to assess its tower-damping capability and the required 

generator torque fluctuations for this damping. Figure 5 shows the open-loop and closed-loop tower’s s-s bending 

moments from this simulation, as well as rotor speed. In this simulation, step winds were used as the input, so that 

the rotor speed would start below and pass through the 2-per-revolution (2P) excitation of the tower’s first s-s mode, 

at about 26.5 rpm (at this rotor speed [2.78 rad/s], 2P = 5.56 rad/s, which corresponds to the tower 1st s-s mode). A 

dramatic reduction in the tower’s s-s bending moments is seen with tower damping control (closed loop) compared 

to the case without tower damping control (open loop). Increasing the real part of the poles that correspond to the 

tower’s first s-s mode from −0.001 (open-loop) to −0.197 (closed-loop) provides this load reduction. Figure 6 shows 

the simulated generator torque for open-loop and closed-loop cases. For the closed-loop case, torque perturbations 

are superimposed on top of the overall torque variation. These perturbations correspond to the commanded generator 

torque that actively damps the tower’s first s-s mode. No perturbations are seen for the open-loop case. The largest 

generator torque perturbations (approximately 500 Nm) occur for rotor speeds of approximately 26.5 rpm, when the 

rotor provides 2P excitation of the tower. 

 

7
-1500

-1000

-500

0

500

1000

1500

20 30 40 50 60 70 80

Time (sec.)

Tower Side-side Bending

Moment (kNm)

25

30

35

40

Rotor-speed (RPM)

Open-loop

Closed-loop

Rotorspeed

Figure 5. Simulation of tower side-side bending 

moments in open- and closed-loop showing load 

attenuation in closed-loop. 

500

1000

1500

2000

20 30 40 50 60 70 80

Time (sec.)

Generator Torque (Nm)

20

25

30

35

Rotor-speed (RPM)

Closed-loop

Open-loop

Rotorspeed

Figure 6. Simulation of generator torque in openand closed-loop for the case shown in Fig. 5. 

We implemented and tested the tower damping controller on the CART. We tried to compare results from this 

controller to a baseline controller with no tower damping. A direct comparison of results is difficult because the 

controllers are tested at different times. We attempted to choose datasets that had similar statistics with excitation of 

the tower modes at 2P for both datasets. Figure 7 shows the tower’s s-s bending moments from these two 

controllers. These are not identical datasets; the operating conditions and wind speeds are different for the baseline 

case compared to the state-space case. Still, the state-space case shows a significant reduction in cyclic loads 

compared to the baseline case. This is a direct result of using full-state feedback (as previously seen in simulation) to 

add damping to the tower’s first s-s mode. Figure 8 shows commanded generator torque for the two cases, showing 

the degree of torque fluctuations at the tower’s first s-s frequency for the state-space case. Overall we observed 

higher torque fluctuations in the actual test of this controller than in simulation, possibly because the turbine in the 

real case is impacted by turbulent wind fluctuations (which have spatial variations across the rotor disk). In 

simulation, only step winds were applied. In addition, the actual system could have a smaller amount of damping 

than the simulation model, which would result in greater torque variations. 

Tower side-side Bending Moment

-500

0

500

1000

1500

2000

192 196 200 204 208

time (sec)

Bending moment (kNm)

new control

baseline control

Figure 7. Measured tower side-side bending 

moment from the new control compared to the 

baseline control in the region of tower resonance. 

Generator Torque

500

700

900

1100

192 196 200 204 208

time (sec)

Torque (Nm)

new control

baseline control

Figure 8. Measured generator torque from the 

new control compared to the baseline control in 

the region of tower resonance. 

After successfully implementing and testing this controller, we decided to change the control input from tower 

bending moment to tower-top acceleration. This change eliminated the need for a coordinate transformation and is 

more representative of sensors used in the wind turbine industry. The linear model for control design had the same 

states as in Eq. (8), but the control input y is now 

 

8
perturbed generator speed

perturbed tower-top fore-aft acceleration

perturbed tower-top side-side acceleration

y

⎡ ⎤

⎢ ⎥ =

⎣ ⎦

 (10) 

The controller was designed at the same linearization point as the last controller. After we implemented and 

tested this controller on the CART, we discovered that the commanded generator torque reflected the high frequency 

vibrations contained in the tower-top accelerometer data. Figure 9 shows measured tower s-s acceleration data and 

commanded generator torque which reflect these high frequency vibrations. 

To resolve this problem, we had to first filter the tower acceleration data before using it as an input to the 

controller. Even a filter that attenuates high-frequency vibrations can change the phase of a signal at low 

frequencies. We were concerned with this phase shift, because it would cause the commanded generator torque to be 

applied at the wrong phase and destabilize the tower’s s-s motion. This phase effect must be accounted for in the 

controller so torque commands will be applied at the correct phase. One way to do this is to represent a model of the 

filter in the linear model used for control design. In this way, the controller has the information it needs about the 

filter to apply torque fluctuations at the correct phase angle to stabilize the tower’s s-s motion. 

We selected a second-order filter with the following transfer function: 

2

0.03 + 30.0 ( ) + 15.0 + 30.0s F s

s s = (11) 

Generator Torque

0

400

800

1200

1600

2000

35 37 39 41 43 45

Time (sec)

Torque (Nm)

-2.0

-1.0

0.0

1.0

2.0

3.0

Acceleration 

(m/s^2)

generator torque

acceleration

Figure 10. Measured tower side-side 

acceleration and generator torque AFTER 

filtering acceleration. 

Generator Torque

200

400

600

800

1000

1200

1400

440 442 444 446 448 450

Time (sec)

Torque (Nm)

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

Acceleration 

(m/s^2)

acceleration

generator torque

Figure 9. Measured tower side-side acceleration 

and generator torque BEFORE filtering 

acceleration. 

This filter provided the characteristics necessary to attenuate accelerometer response at high frequency. We 

added two extra states to the state vector to add the dynamics of this second-order filter to the control model. In 

addition, the state matrices in Eq. (2) were amended with the necessary values to describe the dynamics of this filter. 

We then implemented and tested a revised controller designed from this linear model on the CART. Figure 10 

shows measured generator torque and tower s-s acceleration with this new controller. Now the commanded 

generator torque is smooth and applies the correct phase to actively damp the tower’s s-s motion, due to correct 

filtering of the accelerometer signal. 

Further tests were performed with this controller in Region 2 operation. For rotor speeds up to 38 rpm the 

controller behaves as expected and adds damping to the tower’s first s-s mode as designed. For rotor speeds of 38 to 

39 rpm, the controller appears to excite unmodeled dynamics. Figure 11 shows measured generator torque and rotor 

speed for a case when this excitation occurs. The rotor speed begins at 39 rpm and decreases to 37 rpm toward the 

 

9
end of the record. As the rotor speed passes through 38.5 rpm, a significant increase in amplitude occurs at a 

frequency of approximately 1.3 Hertz (Hz.). As the rotor speed decreases below 38.5 rpm, the amplitudes decrease. 

Figure 12 shows the measured tower’s first f-a and s-s acceleration. Clearly these accelerometers are measuring 

excitation at 38.5 rpm and the generator torque fluctuations reflect this excitation, because tower-top accelerometer 

data are being input to the controller. 

Generator Torque

500

1500

2500

3500

4500

5500

385 390 395 400 405

Time (sec)

Torque (Nm)

35

36

37

38

39

40

Rotorspeed

(rpm)

generator torque

rotorspeed

Figure 11. Measured generator torque for 

turbine operation at 37 to 39 rpm with new 

controller. 

Tower Acceleration

-3.0

-2.0

-1.0

0.0

1.0

385 390 395 400 405

Time (sec)

Acceleration (m/s^2)

side-side acceleration

fore-aft acceleration

Figure 12. Measured tower side-side and fore-aft 

acceleration for the case shown in Fig. 10. 

We needed to determine if this excitation was caused by the new state-space tower damping controller. We 

examined datasets from the baseline controller (which has no tower damping) and found similar excitation as that 

shown in Figure 12. This showed us that the state-space controller was not the cause of this excitation. 

Next we wanted to review code simulations to see if they revealed excitation at this rotor speed. We reviewed the 

FAST simulations with this controller for rotor speeds of 38 to 39 rpm. These simulations failed to show any 

excitation like that seen in the measured data. Clearly this is a case of excitation of un-modeled dynamics. It is 

important to design controllers to account for such un-modeled dynamics. 

A solution to correct control design would be to further filter the tower-top acceleration data to reduce the 

accelerometer response at the natural frequency that corresponds to this excitation before it is input to the controller. 

This would prevent the generator torque controller from responding at this frequency. It is not easy to design a filter 

that retains the proper information at the tower’s first s-s and f-a natural frequencies (at about 5.5 rad/s), yet 

attenuates response at the frequency corresponding to a 2P excitation at 38.5 rpm (8.0 rad/s). We will investigate the 

proper filter design to accomplish these objectives in future work. For this paper, we simply designed a second 

Generator Torque

500

1500

2500

3500

4500

5500

520 525 530 535 540 545 550

Time (sec)

Torque (Nm)

35

36

37

38

39

40

Rotorspeed 

(rpm)

generator torque

rotorspeed

Figure 13. Measured generator torque for turbine 

operation at 37 to 39 rpm with revised new 

controller. 

Tower Acceleration

-3.0

-2.0

-1.0

0.0

1.0

525 530 535 540 545

Time (sec)

Acceleration (m/s^2)

side-side acceleration

fore-aft acceleration

Figure 14. Measured tower side-side and fore-aft 

acceleration for the case shown in Fig. 13. 

 

10
controller with reduced tower damping gains and scheduled it (based on rotor speed) to be active for rotor-speeds of 

38 to 39 rpm. 

We implemented and tested this revised controller. Figures 13 and 14 show measured generator torque and tower 

acceleration from this new controller at 38.5 rpm. Even though the excitation again shows up in the tower 

acceleration data, the generator torque perturbations are greatly reduced, compared to the previous controller. This 

reduction is due directly to the reduction of tower damping gains in this controller. 

Another issue we resolved in implementation of these controls was the non-zero mean value of the tower-top s-s 

and f-a acceleration data, seen in Figures 12 and 14. We found it necessary to subtract these mean values before 

inputting this data to the controller. The cause of these non-zero mean values is still being investigated. 

The next step before implementing the MIMO controller was to implement and test a simple blade pitch 

controller for the tower’s f-a damping in Region 2. Implementation and testing of this controller was straightforward 

and validated our idea that blade collective pitch is an effective actuator for actively damping the tower’s f-a motion 

in Region 2. After this last step, we were ready to implement the MIMO controller. 

V. Comparisons of MIMO State-Space Controller to Baseline Results 

We collected data on the CART while the Region 2 MIMO state-space controller was being tested. We then 

compared the test results to cases from the baseline control in Region 2 for similar dataset statistics. Because these 

controllers were tested at different times, a direct results comparison is difficult. Our objective was to show trends 

such as load mitigation. We examined datasets and extracted sections of data in which turbine operating parameters 

(such as wind speed and direction and yaw error) were similar for the baseline and state-space control cases. The 

results of our comparison are preliminary, and we need many more hours of operation for valid statistical 

comparisons between the controllers. 

Table 2 shows the statistics of datasets in three cases. In each case there is a dataset for: 1) the baseline controller 

without the tower avoidance scheme, 2) the baseline controller with the tower avoidance scheme (shown in Figure 

2), and 3) the state-space MIMO controller. The different cases were intended to reflect a variety of mean rotor 

speeds and wind speeds, although in retrospect there was not a large change in these values from one case to the 

next. Listed in this table are the mean and standard deviation (std) of the wind speed (at a height of 36.6 m) and rotor 

speed (LSS rpm). In each case we tried to select datasets with similar statistics. 

Table 2. Wind and Rotor-Speed Statistics for the Three Data-sets 

Dataset Wind speed (m/s) Rotor speed (rpm) 

Case 1 (300 seconds) mean std mean std 

 Baseline (no tower avoidance) 8.46 1.62 28.9 4.39 

Baseline (with tower avoidance) 8.28 0.93 28.6 2.99 

State-space 8.23 1.61 29.2 6.15 

Case 2 (500 seconds) mean std mean std 

Baseline (no tower avoidance) 8.19 1.33 27.3 3.53 

Baseline (with tower avoidance) 7.31 1.20 26.0 4.15 

State-space 7.45 1.12 26.8 3.81 

Case 3 (300 seconds) mean std mean std 

Baseline (no tower avoidance) 9.42 1.19 30.1 3.48 

Baseline (with tower avoidance) 9.29 1.27 32.3 3.46 

State-space 8.78 1.28 31.4 4.50 

 

11
For each case we calculated the fatigue damage equivalent load (DEL) for the tower’s s-s and f-a bending 

moments, and the blade flapwise and edgewise bending moments.16 Figures 15, 16 and 17 show plots of normalized 

DELs for these three cases, with the results normalized to the baseline (no tower avoidance) dataset for each case. 

Case I

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Tower s-s

bending DEL

(kNm)

Tower f-a

bending DEL

(kNm)

Blade flapbending DEL

(kNm)

Blade edgebending DEL

(kNm)

Load Category

Normalized Value

Baseline-no tower

avoidance

Baseline-with tower

avoidance

State-space

Figure 15. Measured CART data for Case 1. 

Case 2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Tower s-s

bending DEL

(kNm)

Tower f-a

bending DEL

(kNm)

Blade flapbending DEL

(kNm)

Blade edgebending DEL

(kNm)

Load Category

Normalized Value

Baseline-no tower

avoidance

Base-with tower

avoidance

State-space

Figure 16. Measured CART data for Case 2. 

Case 3 

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Tower s-s

bending DEL

(kNm)

Tower f-a

bending DEL

(kNm)

Blade flapbending DEL

(kNm)

Blade edgebending DEL

(kNm)

Load Category

Normalized Value

Baseline-no tower

avoidance

Baseline-with tower

avoidance

State-space

Figure 17. Measured CART data for Case 3. 

Looking at each case, a large reduction in fatigue 

DEL for the tower’s s-s and f-a bending moments is 

evident for the baseline with tower avoidance and 

state-space controllers compared to the baseline 

without tower avoidance. The baseline with the tower 

avoidance scheme seems to be about as effective as the 

state-space controller for mitigating the tower’s s-s 

bending moment. The results for all three cases seem 

to indicate that the state-space controller mitigates the 

tower’s f-a bending moment more effectively than the 

baseline with tower avoidance. We should note that 

the tower avoidance scheme could be included in the 

state-space controller but was not included for these 

studies. 

Another important point is to see if the state-space controller results in higher fatigue DELs for other loads 

throughout the system compared to the baseline controllers. The blade flapwise- and edgewise-bending-moment 

DELs indicate there is no significant increase in DELs for the state-space controller compared to the baseline 

controllers (although for Case 3 the flapwise DEL for the state-space controller is slightly higher than the flapwise 

DEL for the baseline without tower avoidance). In future studies we will investigate the impact of this state-space 

controller on the fatigue DELs for the drive-train and gearbox. 

We also calculated the maximum pitch rates for each case for the state-space controller (the pitch rates for the 

baseline controller with and without tower avoidance were zero, since pitch is held constant in Region 2). We also 

calculated the root mean square (RMS) of the pitch current to further compare actuator usage. Table 3 shows these 

values. The pitch servos must not exceed a pitch rate of 18 degrees per second (deg/s) or a current of 35 Amps 

RMS. As we see from Table 3, the pitch servos were operating well below these limits. 

 

12
Table 3. Maximum Pitch Rates for the State-Space Controller 

Dataset Pitch Rate (deg/s) RMS Pitch Current (Amps) 

Case 1 5.64 12.69 

Case 2 6.64 12.19 

Case 3 6.89 15.73 

VI. Conclusions 

In this paper, we have shown the design, implementation, and testing of a MIMO controller to actively damp 

tower s-s and f-a motion by using generator torque and blade pitch in Region 2. Generator torque effectively damps 

tower s-s motion; blade pitch effectively damps tower f-a motion. When combined together in a MIMO controller, 

they actively damp both motions simultaneously without destabilizing the turbine. 

We described the steps taken to implement this MIMO controller. We resolved issues of accelerometer response 

at high vibration frequencies. We included filter states in the control model so the controller could compensate for 

the phase lag produced when the tower-top accelerometer data is filtered before using it as an input to the controller. 

Another issue was the excitation of unmodeled dynamics at 38.5 rpm rotor speed. We resolved this problem by 

designing a second controller with reduced tower damping gains and scheduling this controller to operate between 

38 and 39 rpm. In future work we will design a filter to correctly attenuate accelerometer response at this frequency 

without affecting response at the tower first s-s and f-a natural frequencies. 

Results from the MIMO controller showed large reductions in tower s-s and f-a bending moments compared to 

the baseline controller without the tower avoidance scheme. The MIMO controller produced similar tower s-s load 

alleviation but greater tower f-a load alleviation compared to the baseline controller with tower avoidance. The 

results of this study indicate that the MIMO controller did not increase the blade flapwise and edgewise loads 

relative to the baseline controller. 

Another issue that arises in this study is the effect of the tower avoidance strategy (for the baseline controller) on 

Region 2 energy capture. This strategy causes the turbine to operate at a tsr that does not correspond to optimum Cp, 

with a corresponding decrease in energy capture. Further tests are required to ascertain whether the state-space 

controller (which does not have the tower avoidance strategy) results in greater Region 2 energy capture than the 

baseline controller with tower avoidance. If it does, the state-space controller would hold the advantage, because it 

alleviates tower loads at about the same level as the baseline with tower avoidance, but results in greater energy 

capture. 

Future Work 

Directions for future work include further field testing of the controls already implemented in the CART. We 

need many more hours of test data to show statistically that loads are reduced compared to baseline control results. 

We will investigate the impact of this state-space controller on the fatigue DELs for the drive-train and gearbox. 

Developing the proper filter to mitigate tower-top accelerometer response in the frequency range corresponding to 

unmodeled dynamics is important. This filtering must be done without affecting information needed by the 

controller at the tower first s-s and f-a natural frequencies. Implementation and testing of independent pitch control 

to mitigate spatially varying wind disturbances across the rotor disk will be designed and tested. Controls will also 

be designed and implemented on a three-bladed version of the CART. 

 

13
References 

1 th Bossanyi, E. A., “Developments in Closed Loop Controller Design for Wind Turbines,” 19 ASME Wind Energy 

Conference, Reno, NV, 2000, pp. 64-74. 

2

Burton, T., Sharpe, D., Jenkins, N., and Bossanyi, E., 2001, Wind Energy Handbook, John Wiley & Sons, LTD, New York, 

pp. 488−489. 

3

Wright, A. D., and Balas, M., “Design of State-Space-Based Control Algorithms for Wind Turbine Speed Regulation,” 

ASME J. Solar Energy Engineering, Vol. 125, No. 4, November 2003, pp. 386-395. 

4

 Wright, A. D., and Balas, M., “Design of Controls to Attenuate Loads in the Controls Advanced Research Turbine,” ASME 

J. Solar Energy Engineering, Vol. 126, No. 4, November 2004, pp. 1083-1091. 

5

Wright, A. D., “Control Design for Flexible Wind Turbines,” Ph.D. Dissertation, Department of Aerospace Engineering 

Sciences, University of Colorado, Boulder, CO, 2003. 

6

Stol, K., and Balas, M., “Periodic Disturbance Accommodating Control for Blade Load Mitigation in Wind Turbines,” 

ASME J. Solar Energy Engineering, Vol. 125, No. 4, November 2003, pp. 379-385. 

7

 Stol, K., “Disturbance Tracking Control and Blade Load Mitigation for Variable-Speed Wind Turbines,” ASME J. Solar 

Energy Engineering, Vol. 125, No. 4, November 2003, pp. 396-401. 

8

Stol, K., and Fingersh, L., “Wind Turbine Field Testing of State-Space Control Designs,” National Renewable Energy 

Laboratory, NREL/SR-500-35061, Golden, CO, 2004. 

9

Wright, A., Stol, K., and Fingersh, L., “Progress in Implementing and Testing State-Space Controls for the Controls 

Advanced Research Turbine,” 24th ASME Wind Energy Conference, Reno, NV, 2005, pp. 88-100. 

10Stol, K., Zhao, W., and Wright, A., “Individual Blade Pitch Control for the Controls Advanced Research Turbine (CART),” 

ASME J. Solar Energy Engineering, Vol. 128, No. 4, November 2006, pp. 498−505. 

11Wright, A., Fingersh, L., and Balas, M., “Testing State-Space Controls for the Controls Advanced Research Turbine,” 

ASME J. Solar Energy Engineering, Vol. 128, No. 4, November 2006, pp. 506−515. 

12Johnson, K. E., Fingersh, L., Balas, M., and Pao, L. Y., “Methods for Increasing Region 2 Power Capture on a Variable 

Speed HAWT,” ASME J. Solar Energy Engineering, Vol. 126, No. 4, November 2004, pp. 1092−1100. 

13Fingersh, L., and Johnson, K., “Baseline Results and Future Plans for the NREL Controls Advanced Research Turbine,” 

23rd ASME Wind Energy Conference, Reno, NV, 2004, pp. 87-93. 

14Jonkman, J. M., and Buhl, M. L., “FAST User’s Guide,” National Renewable Energy Laboratory, NREL/EL-500-38230, 

Golden, CO, 2005. 

15Kwakernaak, H., and Sivan, R., Linear Optimal Control Systems, Wiley Interscience, NY, 1972, pp. 207-237. 

16Sutherland, H. J., “On the Fatigue Analysis of Wind Turbines,” Sandia National Laboratories, SAND99-0089, 

Albuquerque, NM, 1999. 

 

14
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Designing and Testing Controls to Mitigate Tower Dynamic Loads in 

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