IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 26, NO. 2, JUNE 2011
627
Wave Prediction and Robust Control of Heaving
Wave Energy Devices for Irregular Waves
Marco P. Schoen, Senior Member, IEEE, Jørgen Hals, and Torgeir Moan
Abstract—This paper presents a comparison of different existing
and proposed wave prediction models applicable to control wave
energy converters (WECs) in irregular waves. The objective of the
control is to increase the energy conversion. The power absorbed
by a WEC is depending on the implemented control strategy. Uncertainties in the physical description of the system as well as in the
input from irregular waves provide challenges to the control algorithm. We present a hybrid robust fuzzy logic (FL) controller that
addresses the uncertainty in the model and the short-term tuning
of the converter. The proposed robust controller uses the energy
from the power take off as input, while the FL controller portion
is used for short-term tuning. The FL controller action is based on
a prediction of the incoming wave. Simulation results indicate that
the proposed hybrid control yields improved energy production,
while being robust to modeling errors.
Index Terms—Control systems, energy conversion, intelligent
systems, prediction methods.
I. INTRODUCTION
HE energy absorption of wave energy converters (WECs)
is influenced by the use of the knowledge of future wave
data in the control loop. The prediction of future wave data is not
a trivial undertaking, since real waves are irregular in nature. A
number of contributions have been made in forecasting irregular
waves. For example, Forsberg [1] introduced an autoregressive
(AR) with moving average (ARMA) model with fixed time horizon prediction, utilizing the maximum likelihood estimation to
describe ocean waves off the coast of Sweden. The algorithm
included the on-line recursive form with an adaptation constant
for slowly time-varying sea condition. Results of the proposed
algorithm included prediction of waves for long time horizon,
i.e., 30 s using a 0.5 s sample time, yielding predictions with
adequate results. Noise considerations were given only to the
extent that outliers are removed through the standard outlier
detection rule. Forsberg’s model requires an extensive model
order in order to capture the nature of irregular waves. Halliday
et al. [2] looked at using the fast fourier transform (FFT) to
generate prediction of waves over distance. The distance factor
T
Manuscript received May 29, 2010; revised September 1, 2010; accepted
December 1, 2010. Date of publication February 4, 2011; date of current version May 18, 2011. Paper no. TEC-00239-2010.
M. P. Schoen is with Idaho State University, Pocatello, ID 83209 USA
(e-mail: schomarc@ isu.edu).
J. Hals and T. Moan are with the Center for Ships and Ocean Structures
(CeSOS), Norwegian University of Science and Technology (NTNU), 7491
Trondheim, Norway (e-mail: jorgen.hals@ntnu.no; Torgeir.Moan@ntnu.no).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TEC.2010.2101075
is used to avoid perturbations of the buoy to the measurements.
This approach necessitates the use of directional data. The simulation results presented indicate reasonable success for long
distances. One of the drawbacks of using the approach of Halliday et al. is that the spectral estimates of the FFT-based models
are less sensitive to sharp peaks and dips compared with AR
models, [3]. In addition, the FFT is for strictly stationary systems, and cannot handle the temporal fluctuations, which is an
essential component in the characteristics of irregular waves.
WECs operating in irregular waves can be categorized into
passive- and active-tuning-based control. Relevant study has
been reported in the literature since the mid 1970s [4]. Tuning
can be achieved by adapting the WECs power take off’s (PTO)
stiffness and damping to match the incident wave frequency and
the radiation wave damping coefficient, respectively. Extensive
review of this is given by Falnes [5], which includes the discussion of optimal control and phase control for WECs. Latching
control attempts to achieve the same effect by halting the device’s motion in order to align the phase of the system with
the wave motion. Study on latching control has been reported
by Korde [6], [7]. Optimal latching control is discussed in [8],
where two analytical solutions are presented: one based on the
equation of motion and the other based on optimal command
theory. Using the future wave excitation, Babarit et al. in [9]
investigated three strategies for latching control. Though the future wave excitation was not computed (assumed to be known),
the study has value in terms that it showed the improvement of
the energy absorbed using the wave excitation for the latching
time computation. Optimality, in terms of energy, production of
WECs has been addressed in many instances in aforementioned
literature and elsewhere. Intelligent systems, such as genetic
algorithms (GAs) may present an avenue to avoid the local minima problem, and is to some limited extend explored in this
paper.
In this paper, we will propose a hybrid Kautz/AR predictive
model as well as a pure predictive Kautz model and compare
them to a predictive AR and ARMA algorithm. The outcome
of our investigation into predictive models will be utilized to
design predictive fuzzy logic (FL)/robust controllers. The prediction models are based on local wave height measurements and
assume an unperturbed wave. We also attempt to address some
of the robustness aspects in designing a controller for WECs.
The robustness issues are primarily focused on the uncertainty
of parameters describing the dynamics of the buoy and not on
improved performance. The performance issues are addressed
by a proposed simple FL controller, which is responsible for
the short-term control of the device in order to maximize the
energy production. The proposed controllers in this paper do
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not employ latching control technology, rather use the resistive
forces of the PTO to improve the performance of the WEC. The
paper is organized in two parts. First, we describe the pursuit
of finding predictive models that work well in a stochastic environment are of low model order (parametric models), and have
a sufficient accuracy that allows the algorithm to be used for
control of WECs operating in irregular waves. In the second
part, we propose a robust controller and combine this with a FL
controller.
output vector. Defining Xss = ΦTAR YAR , the updated parameter estimate is θ̂AR (k + 1|k) = P (k + 1) × Xss (k + 1). Introducing the modal matrix Q composed of the eigenvectors of
P = (ΦTAR ΦAR ) and diagonal eigenvalue matrix Λ of the correlation matrix P, the transformed filter weight vector results
into
II. WAVE PREDICTION MODELS
′
′
XSS (k + 1) = QΛQT × Qθ̂AR
(k + 1|k) = QΛθ̂AR
(k + 1|k)
′
θ̂AR
(k + 1|k) = QT θ̂AR (k + 1|k).
(5)
Transforming Xss : Pθ̂AR (k + 1|k) = Xss (k + 1)
A. AR Predictive Model
Consider a simple discrete time finite impulse response (FIR)
AR model to characterize irregular waves
ŷ(k|k − 1) =
p
′
′
Xss
(k + 1) = Λθ̂AR
(k + 1|k).
aj y (k − j)
(1)
j=1
where the output y ∈ Rn o x1 is the wave height with no = 1 and
is measured up to time t = k − 1, k is the discrete time index,
p is the model order, aj ∈ Rn o ×n o are the influence coefficient
matrices, and ˆ denotes the predicted value. One of the main
problems in fitting such a time series to the gathered data is the
determination of the model order. Based on the Wold decomposition theorem, an AR model can fit a variety of data characteristics, if a sufficiently large model order is selected [10].
To construct a variable horizon prediction formulation on the
basis of (1) the following i-step-ahead predictor can be established (see [11])
yˆ(k + i|k − 1) =
p
(i)
aj y (k − j)
(2)
where we made use of the residual, which is defined by the
difference of the estimate and the true output, i.e., ε = y − yˆ.
The corresponding predictive coefficient matrices are:
(i)
(i−1)
x′i
(i−1)
aj + a j + 1 .
(3)
One can easily argue that by any standard, the predictive model
introduced above possesses a rather large model order. Hence,
the corresponding correlation matrix utilized in the estimation
algorithm induces a large computational burden during the operation of the filter, in particular when repeated estimations are
needed to update to the current wave conditions. This is true to
some degree also for the recursive least squares (RLSs) method,
even though no inversion of large matrices is required; the model
order dictates the number of computations needed. This property of large model order is a general characteristic of FIR filters
due to the missing feedback term in the filter structure. We can
investigate the contribution of each filter weight to the predicted
output by computing the transformed filter weights as follows:
starting with the formulation for the estimated filter parameter
matrices (least squares)
θ̂AR = (ΦTAR ΦAR )−1 ΦTAR YAR
(4)
where θ̂AR is a vector containing the estimated parameter matrices, ΦAR is the information matrix, and YAR is the respective
(6)
x′ss
Using
as the elements in
and λi as the corresponding
eigenvalues found on the diagonal of Λ, each individual transformed filter weight contribution is given by
(0)
a′ i
x′i
,
λi
=
for 1 ≤ i ≤ p.
(7)
B. ARMA Predictive Model
Adding to the AR formulation a MA part enhances the flexibility of the AR model [12]. Consider a typical ARMA model
in the output error formulation
yˆ(k|k − 1) =
p
aj y (k − j) +
p
bm ε(k − m)
(8)
m =0
j=1
j=1
aj = a1
′
Defining Xss
= ΦTAR Xss , which premultiplied with QT
yields
where aj and bj are the ARMA filter matrices. The MA associated with the bj matrices is nonlinear due to the prediction
error formulation. The model can be constructed through backsubstitution of the output into the original filter model formulation. An i-step ARMA predictor thus can be given as
yˆ(k + i|k − 1) =
p
(i)
aj y (k − j) +
p
b(i)
ε(k − m) (9)
m
m =1
j=1
where the predictive parameters for the past residual terms are
defined as
(i)
(i−1) (0)
aj
aj = a 1
(i−1)
+ aj + 1
for j = 1, 2, 3, . . . and ak = 0, for k > p.
The predictive parameters for the MA terms are defined as
(i−1)
b(i)
m = a1
(i−1)
b(0)
m + bm + 1
for m = 1, 2, 3, . . . and bk = 0, for k > p.
The estimation of these parameters can be done, for example,
by following the Hannan–Rissanen algorithm [13].
C. Orthogonal Basis Function (OBF) Prediction Model
One of the main reasons AR and ARMA models require large
model orders is that they are of the type FIR filter, and they do
not have a structure that allows for inclusion of prior knowledge
into the filter before the estimation occurs. AR models belong
SCHOEN et al.: WAVE PREDICTION AND ROBUST CONTROL OF HEAVING WAVE ENERGY DEVICES FOR IRREGULAR WAVES
629
to the class of orthogonal basis functions (OBFs), which are the
generalizations of FIR models. The basis function of FIR models
are delayed Dirac impulses expressed with the q-operator (delay
operator). In the pursuit of reducing the required model order
while still describing the future waveform adequately, we propose to choose orthonormal filters Li (q) such that (1) becomes
y (k + i|k − 1) =
p
c̃i Li (q)y (k).
(10)
i= 1
To keep the selected OBF model linear in the parameters, Li (q)
are chosen before the estimation of the filter coefficient matrices
c̃i occurs. Li (q)’s are chosen with the purpose of incorporating
prior knowledge of the system to be modeled. Since the waves
exhibit an underdamped dynamical characteristic, we choose
Kautz filters for the Li (q)’s. Kautz filters allow us to incorporate
conjugate complex pole pairs into the model. Considering a
given conjugate complex pole pair (α ± iβ), the Kautz filter
coefficients are computed by
c=
2α
1 + α2 + β 2
and d = −(α2 + β 2 ).
(11)
With this, the filter Li (q) can now be computed using the
following orthonormalization process:
(1 − c2 )(1 − d2 )
(12)
L1 (q, c, d) = 2
q + c(d − 1)q − d
(1 − d2 )(q − c)
L2 (q, c, d) = 2
(13)
q + c(d − 1)q − d
and the higher order filter parameters by using the recursive
formula as follows:
−dq 2 + c(d − 1)q + 1
L2i−1 (q, c, d) = L2(i−1)−1 (q, c, d) 2
q + c(d − 1)q − d
The limits on the coefficients c and d are given as
and
− 1 < d < 1.
In addition to having a purely predictive Kautz filter, one can
incorporate as well some simple AR terms, resulting into hybrid
Kautz/AR filters. The step-by-step procedure for constructing
and estimating a proposed predictive Kautz or hybrid Kautz/AR
filter is given in the following. A recursive formulation can be
developed using the RLS approach, which is left for future work.
Step 1: Collect a sufficient amount of wave height measurements y(k), with a fixed sampling time. Normalize the
wave height time series using y(k) = y(k)/ym ax , where ym ax =
sup{y(k)}.
⎡
⎢
⎢
ΦKautz (q, c, d) = ⎢
⎣
Influence of Kautz parameters on prediction.
Step 2: Perform a spectral analysis and identify the frequency
ω 1 with the largest contribution or magnitude in the spectrum.
Step 3: Compute the Kautz filter coefficients c and d based
on the identified frequency ω 1 ; also assume a damping ratio ξ.
The computation of the filter coefficients c and d require the
real and imaginary parts of the dominant root location, which
are computed as follows: α = ξω1 and β = sin(cos−1 ξ)ω1 .
Step 4: Estimate the model parameter matrices c̃i using
(i)
Θ̂Kautz = {ΦTKautz ΦKautz }−1 ΦTKautz YKautz (M − i)
where ΦKautz is the information matrix, ΦKautz (q, c, d) as
shown at the bottom of the page, where M is the data length, and
ΘKautz = [c̃1 , c̃2 , c̃3 , . . . , c̃p ]T YKautz
= [y(p + i + 1), . . . , y(M + i)]T .
−dq 2 + c(d − 1)q + 1
.
L2i (q, c, d) = L2(i−1) (q, c, d) 2
q + c(d − 1)q − d
−1 < c < 1
Fig. 1.
Step 5: Compute the future output using (10).
The aforementioned procedure for the computation of the
hybrid Kautz/AR filter involves two coefficients that need to
be selected. In the following, we clarify the reasoning for executing Step 2. For this, we use a set of simulations where the
average correlation coefficient is computed between the predicted wave height and the actual wave height. The simulations
are carried out with a hybrid model that contains four Kautz
filter terms and one AR term, i.e., the model order is five, where
p = 5 corresponds to L5 = q. The additive measurement noise
is characterized by a SNR of 1000 (0.1% noise variance), and
waves are computed based on the Pierson Moskowitz wave
spectrum [14], with a corresponding wind speed of 55.5 km/h
[30 knots, moderate Gale]. Fig. 1(a) gives the average and standard deviation (STD) for a varying c coefficient, corresponding
L1 (q, c, d)y (p)
L1 (q, c, d)y (p + 1)
..
.
L3 (q, c, d)y (p)
L3 (q, c, d)y (p + 1)
..
.
...
...
..
.
Lp (q, c, d)y (p)
Lp (q, c, d)y (p + 1)
..
.
L1 (q, c, d)y (M − 1)
L3 (q, c, d)y (M − 1)
...
Lp (q, c, d)y (M − 1)
⎤
⎥
⎥
⎥
⎦
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TABLE I
CORRELATION FOR DIFFERENT PREDICTIVE FILTERS UNDER VARYING
NOISE ENVIRONMENT
to the frequency of the Kautz filter. The statistics is based on a
set of 50 simulation runs. First, we observe that when c = 0, the
only portions of the filter that are nonzero are corresponding to
the AR coefficients; hence, the first entry can be considered as a
simple AR filter. The correlation of the AR filter is respectable,
but using c = 0, the simulations indicate that c is to be selected
around 0.9. Using the step-by-step procedure outlined earlier,
the average c value that would be selected by determining the
dominant frequency in the wave spectrum is 0.9979. The relationship of c to the Kautz filter is that it sets its dominant
frequency to the dominant frequency of the waves.
Considering the second parameter in the proposed wave prediction model, the damping, we are interested in observing the
influence of this parameter on the predicted time series. Utilizing the same simulation parameters as listed earlier, with a
Pierson–Moskowitz wave spectra corresponding to a wind speed
of 46.3 km/h [25 knots, strong breeze], the statistics provided
in Fig. 1(b) is generated. The parameter c is computed based on
the dominating wave frequency, which resulted in c = 0.9973.
Fig. 1(b) indicates that the damping term d has little sensitivity
to the quality of the prediction. Intuitively, we would imagine
that smaller damping fits the nature of the waves. This perception is validated ever so slightly by Fig. 1(b). No damping results
into the more accurate wave prediction results.
D. Comparative Simulation-Based Analysis of AR, ARMA,
Kautz/AR, and Kautz Predictive Filter Performance
Simulations with four predictive filters are carried out in order
to compare their performance. Table I was compiled using a sea
state of moderate gale, i.e., 55.5 km/h wind speed, a moving
prediction horizon of 20 data points corresponding to a 2-s
ahead prediction at every time instant, and a model order of 35
for the Kautz/AR hybrid filter, the AR, and the ARMA filter.
The proposed predictive Kautz filter is based on a model order
of 25 odd terms. Table I details the performance for different
additive measurement noise conditions (N) in% noise variance
and is based on 51 simulation runs.
From the table, we recognize that for the noise-free case,
all filters perform less well than when some small amount of
noise is included. This behavior can be traced back to the used
estimation algorithm.
Comparing the performance of the hybrid Kautz/AR filter
with the pure AR filter for the cases where noise is present,
one recognizes that not much improvement has been achieved
by including the Kautz elements to the AR filter. The comparison with the Kautz filter is to be made with caution, since it
has fewer filter weights (25) compared to the other filters (35
filter weights), which corresponds to the objective to reduce
the number of parameters used in a prediction model, while
maintaining accuracy. The employed ARMA filter has a similar
performance as the proposed Kautz filter for the cases where
noise is included. The standard deviation (STD) and average
(Ave) correlation coefficient reported in Table I indicate that
the AR and Kautz/AR filter seem to be less receptive to noise
than the ARMA and Kautz filters. Another characteristic noted
in the simulations is that the Kautz filter, and to some degree,
the ARMA filter offer a smoother prediction curve than the
AR-based filters. Correspondingly, from observing simulation
runs, the Kautz filter has a tendency to miss smaller peaks in
the forecast. A relatively simple analysis on the effectiveness to
include additional information into the filter in order to reduce
the number of filter weights is to look at the transformed filter
weights, see (7). Fig. 2 depicts the transformed filter weight
distribution of four filters. Comparing all discussed filters, the
proposed predictive Kautz filter requires fewer terms to describe
the prediction accurately.
III. BUOY MODEL
The chosen WEC model consists of a spherical heaving buoy
reacting against a fixed reference. The model includes only the
heave mode with vertical excursion y, while the water depth is
assumed to be infinite, and the usual linear approximation of
the hydrodynamic forces are used. Assuming that the gravity
force is balanced by the average buoyancy force, our system
can be modeled by the bond graph shown in Fig. 3 [15], [16].
The following forces are included: the wave excitation force Fe ,
the inertia forces due to buoy mass and added mass at infinite
frequency mb ÿ and m∞,b ÿ. Furthermore, the hydrostatic stiffness force Fc = −Cb y, the damping force due to wave radiation
Fr , and the load force from the power take-off machinery Fl is
accounted as well in the model.
As may be seen, the inertia force due to added mass at infinite frequency m∞,b has been separated from the rest of the
radiation force Fr , in accordance with the usual time-domain
representation [5]. Taking the hydrostatic stiffness force to be
linear is equivalent to assuming a constant water-plane area
Aω for the buoy. The buoy is modeled as a sphere with radius
r = 5[m]. The hydrostatic stiffness coefficient is then given by
Cb = ρgAω . As long as the incoming waves are small compared to the characteristic length of the buoy and the oscillation
amplitude is restricted to |y| ≤ 3 [m], this approximation keeps
force errors within ±6% [17].
The bond graph has two energy-storing elements in integral
causality, which indicates that the model has two state variables:
the linear momentum pb of the buoy, and its vertical position y.
Also, we see that the force of the added mass is in differential
causality, which will provide us with an implicit equation for
the inertia force.
SCHOEN et al.: WAVE PREDICTION AND ROBUST CONTROL OF HEAVING WAVE ENERGY DEVICES FOR IRREGULAR WAVES
631
Fig. 2. (a) Influence coefficients of the predictive ARMA filter. (b) Influence coefficients of the predictive AR filter. (c) Influence coefficients of the proposed
predictive Kautz/AR filter. (d) Transformed filter weight distribution for the proposed predictive Kautz filter.
The hydrodynamic radiation force can be given as Fr =
t
−∞ κ(t − τ )(pb /mb )dτ , [5]. Here, the impulse response function κ(t) is a memory function linking the current radiation
force to past and current values for the buoy velocity pb /mb .
The convolution integral Fr may be approximated by a finiteorder state-space model [18]
ψ̇(t) = Ak ψ(t) + Bk
Fig. 3. Bond graph for a heaving sphere showing the governing effects of
inertia, compliance, wave radiation, and excitation.
Deriving state equations from the bond graph yields
m∞,b
ṗb − Cb y − Fl − Fr + Fe
ṗb = −
mb
pb
.
ẏ =
mb
(15)
Fr (t) = Ck ψ(t)
(16)
which will require to find the additional state vector ψ. Rearranging (14), one arrives at
ṗb = −
(14)
pb
mb
mb
(−Cb y − Fl − Fr + Fe ).
mb + m∞,b
(17)
Defining the new state vector x = [ pb y ψ T ], one can now write
the entire system, including the radiation force approximation,
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IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 26, NO. 2, JUNE 2011
Fig. 4. Control system architecture. Dashed lines are associated with off-line
operations.
Fig. 5.
FIS interaction with the WEC.
as
⎡
−m b C b
m b +m ∞, b
0
⎢
ẋ = ⎣ m1
⎢
+⎣
0
Ak
−m b
m b +m ∞, b
mb
m b +m ∞, b
0
0
0
0
= Ãx + B̃
Fl
Fe
⎤
⎥
0 ⎦x
0
b
Bk
⎡
−Ck
.
⎤
⎥ Fl
⎦
Fe
(18)
In this form, Fl is the force from the PTO machinery on the
buoy. This PTO force is the force used to control the system and
is detailed in the following sections of this paper. For the studied
heaving sphere, we have used data from [19] and [20] to compute
the memory function κ(t). A reasonable approximation for the
convolution term can be achieved with a state model of order six
using Hankel singular value decomposition to reduce the model
order [18]. This gives a total of eight states for the heaving buoy
model, taking the wave excitation force and the machinery load
force as inputs to the system.
IV. FL CONTROL
In this section, we propose to use a FL controller with the
purpose to adapt the immediate PTO force Fl = −cẏ by controlling the damping coefficient c. The fuzzy controller includes
a number of rules that allow for reducing the buoy motion, if
the excursion becomes too large. The control output is computed based on the prediction of the future buoy motion, as a
result of current measured wave heights. The horizon for this
controller is of a rather short distance, i.e., 1 s. To accomplish
a fuzzy inference system (FIS) for WEC control, the following
system structure is proposed (see Fig. 4). In the figure, z denotes the wave height, y is the buoy motion, E is the energy,
c∗ is the optimal damping for a given sea state, and the control
parameter is the damping generated by the PTO. For the simulations, the wave energy device given by block WEC is defined by
the model given in Section III [18]. The system identification SI
in Fig. 4 provides a state-space model (resulting into the statespace matrices Ai , Bi , Ci , and Di ) to simulate the dynamics of
the buoy for given predicted wave motions. The SI is performed
off-line before the use of the controller. The prediction of the
wave height is accomplished by a discrete time predictive AR
formulation, which can be achieved by using (2).
The proposed FIS controller implementation includes as the
control variable the damping provided by the PTO. The Mamdani type FIS is defined for this control action by having three
inputs and one defuzzyfied output, as depicted in Fig. 5: the three
inputs are processed using a set of different input membership
functions.
For the Amplitude, the membership functions are categorized as “‘Small Difference,” “Some Difference,” “‘Large Difference,” and “‘Excessive Difference,” where Difference is defined as the discrepancy between |ŷ − z| and the maximum
excursion allowed.
The selected membership function for the amplitude input are
sigmoid functions for the “Small” (left) and “excessive difference,” (right), while the remaining membership functions are
based on the Bell membership function. A similar set of membership functions are created for the second input, the phase
difference between the buoy motion and the wave motion. The
phase is computed by establishing a time record of incoming
waves and the buoy’s motion. The “Small Phase” and “Large
Phase” are characterized with left sigmoid functions and right
sigmoid functions, respectively. The “Good Phase” case is modeled with a Bell membership function.
The last input, the velocity, representing the buoy’s vertical
velocity, is used for the computation of the amount of damping. The single membership function for the velocity is given
by a Polynomial-Z membership function. For the output, the
damping is constructed by the use of four different membership
SCHOEN et al.: WAVE PREDICTION AND ROBUST CONTROL OF HEAVING WAVE ENERGY DEVICES FOR IRREGULAR WAVES
Fig. 6.
Fig. 7.
Buoy motion and wave motion under control.
Fig. 8.
Buoy motion with and without control using optimal parameters.
633
FS output surface for two inputs.
functions, i.e., “Less Damping,” “Optimal Damping,” “More
Damping,” and “Halting.” The corresponding membership
functions are all Gaussian type membership function with the
exception of the “Less Damping” case for which a sigmoid
function is used, and the “Halting” case, where a PolynomialZ membership function is employed. The output damping is
then computed as a change from the nominal value of damping.
The nominal damping is equal to the optimal damping computed using a GA, which in turn is based on past input/output
data, with the objective to absorb the maximum energy. The
employed GA is based on a continuous number representation,
with an elitism-based selection scheme. For more details on the
used GA, see [21]. For the simulations, 100 generations are
used with an initial population size of 96 chromosomes, and a
steady-state population size of 48 chromosomes. The mutation
rate is set to 4%. The input functions are linked to the controller
output by a rule base of the form as follows:
1) if (Phase is Small_Phase), then (Damping is Less_
Damping);
2) if (Phase is Good_Phase), then (Damping is Optimal);
3) if (Phase is Large_Phase), then (Damping is More_
Damping);
4) if (Amplitude is Small_Difference), then (Damping is
More_Damping);
5) if (Amplitude is Some_Difference), then (Damping is
Optimal);
6) if (Amplitude is Large_Difference), then (Damping is
More_Damping);
7) if (Amplitude is Excessive_Difference), then (Damping is
Halting);
8) if (Velocity is zero), then (Damping is Halting).
The overall resulting transfer output surface is given by Fig. 6.
We recognize that the nominal amplitude difference and the
phase is normalized to 0 (desired state). The defuzzyfication
is done using the centroid of the output membership function. For the simulations, a Pierson–Moskowitz wave spectra of
46.3 km/h [25 knots, strong breeze] wind speed is utilized, while
predicting the future wave height 10 steps (1 s) ahead. Fig. 7
depicts the simulated buoy motion under the influence of the FL
controller, which is turned ON after 65 s (k = 650).
At the same time of switching the controller ON, the optimal
initial damping (and stiffness) is incorporated, which is used as
the nominal damping from which the controller deviates. Fig. 8
provides a depiction of the buoy’s motion when optimal damping is used without control compared to with control action. The
excursion limits are exceeded when no control is employed,
while the proposed controller keeps this excursion within the
desired limit of ±3[m]. The damping over time is depicted in
Fig. 9. The short-term forecast and reaction by the FL controller
is clearly manifested in the damping adaptation plot. The converted energy with and without the proposed FL controller is
depicted in Fig. 10 [the energy is computed according to (26)].
Here in both cases, the optimal parameters are used. The energy
graph indicates that the improvement is considerable. Although
the corresponding buoy motions as depicted in Fig. 8 shows
larger excursions for the uncontrolled case, the small change in
the phase allows for a larger energy accumulation. It is also to
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IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 26, NO. 2, JUNE 2011
Fig. 11. Rules with corresponding membership functions and firing strength
of output.
Fig. 9.
Damping computed using the FL controller.
Fig. 12.
Fig. 10. Energy generated with and without FL controller using optimal
parameters.
note that the membership functions of the inputs and the output
are generated using simple rules and intuitive assessment of the
consequences; hence, they are by no means optimal.
The FL inference system detailed earlier is expanded and a
second output is added to influence the stiffness of the PTO in
addition to the damping. The PTO force then takes the form
Fl = −cẏ − ky, where k is the stiffness coefficient to be controlled along with the damping coefficient c. In Fig. 4, the “Parameter” signal flow includes now also the stiffness in addition
to the damping. The FL controller has two output signals, an
addition of three rules, while maintaining the number of inputs.
The three inputs are processed using a set of input membership
functions. The output damping and stiffness are then computed
as a change from the nominal values, which are the optimal coefficients computed using a GA based on past input/output data.
The input functions are linked to the controller output by a rule
base as depicted in Fig. 11. The computed optimal damping and
Time history of buoy motion and wave motion.
the optimal stiffness, using the aforementioned GA, are taken
as the initial values when the controller is switched ON. After
that, the controller adapts the new damping and stiffness for the
immediate future only. Fig. 12 depicts the response of the buoy
over time. The controller is turned ON at time k = 650. The
immediate reaction of the device is to oscillate in resonance.
This oscillation always stays within the excursion limits of ±3
m. Fig. 13 depicts one output of the controller, the stiffness
(the damping plot is similar to Fig. 9). The converted energy
is correspondingly increasing on a steeper curve than when no
control is used (see Fig. 14). The optimized structural coefficients take on the following values: c∗ = (16.429e + 5) Ns/m
and k ∗ = (1.0742e + 5) N/m, respectively. Also included in
Fig. 14 is the comparison between the damping controller and
the damping and stiffness controller. Fig. 14 indicates that a
small improvement of the energy output is noticed due to the
help of adapting the stiffness of the PTO during the simulations.
The change is not significant, but indicates that an instantaneous
update of the control parameters can have some influence.
V. ROBUST CONTROL
The proposed control architecture involves the identification
of a generic WEC and the prediction of future wave height data.
SCHOEN et al.: WAVE PREDICTION AND ROBUST CONTROL OF HEAVING WAVE ENERGY DEVICES FOR IRREGULAR WAVES
635
Elsner and He (in [23]) refined this as
d (A, B) = min σn ([A − sI, B]) = min σ (s)
s∈C
Fig. 13.
s∈C
(20)
where σn (G) is the nth singular value of the (n × (n × m))
matrix G. This implies that the problem of finding the distance to uncontrollability is the problem of minimizing σ(s)
over the complex plane. It is shown in [23] that the function
f (s) = vnH (s)[ un (s) 0 ]T is the partial derivative of σ(s) with
respect to the components of s. The normalized left singular
vector un (s) and the normalized right vector vn (s) are the nth
column of U and V, respectively, which results from the singular value decomposition [A − sI, B] = UΣVH , and H denotes
the complex (Hermitian) transpose. In [23] the function f(s) is
used to find the minimum distance to the uncontrollable region
by use of an iterative Newton method. The disadvantage of this
method is that a good starting value for the iteration is needed
and that no guaranty is given that the global optimum is found.
We utilize an alternative approach by employing a simple GA.
The search area can be defined ( [23]) by
A + AT
A + AT
≤ x∗ ≤ λm ax
Ireal = λm in
2
2
A − AT
A − AT
Iim ag = λm in
≤ y ∗ ≤ λm ax
2i
2i
Stiffness (N/m) computed using the FL controller.
(21)
where s∗ = x∗ + iy ∗ defines the optimum point in the complex
s-plane. To implement a robustness measure in the controller
design, consider the closed-loop system given by (A + BF),
where F ∈ Rm ∗ n is the feedback gain matrix.
Assuming an uncertainty about the system reflected by ∆A
and ∆B, then we need to maximize the distance d
d(A + ∆A + (B + ∆B)F, S)
Fig. 14.
Comparison of Energy generated.
Both processes include inaccuracies and errors that can lead
to undesirable performance reduction of the WEC. Hence, we
are interested in designing a controller that has some sort of
robustness in terms of model inaccuracies. The objective of the
controller proposed in this section is to limit the loss of robustness due to the closed-loop arrangement while maximizing the
energy output of the WEC. The robustness is achieved with an
optimization algorithm that maximizes the distance to the unstable region.Consider a continuous time, linear time-invariant
system given by
x˙ = Ax + Bu
(19)
where A ∈ Rn ∗n and B ∈ Rn ∗m . The system (A,B) is control¯ B̄]) = n ∀s ∈ C.
lable, if rank([Ā − sI,
Paige (in [22]) defines the distance to uncontrollability as
the spectral norm distance of the pair (A,B) from the set of all
uncontrollable pairs
d(A, B) = min{||[E, H]|| : (A + E, B + H) uncontrollable}
(22)
where S ∈ Rn ∗n is the set of all unstable matrices.
Assuming that A + BF + (∆A + ∆BF) has at least one
eigenvalue with a positive real part, that is Re(λ) ≥ 0, we can
define the robustness criteria as follows: the distance between
the closed-loop system (A + BF) to the region of instability
must be larger than the uncertainty in the closed-loop system.
Expressed with aforementioned formulation (see [24])
d2 (A + BF, S) ≤ ||∆A + ∆BF||2
(23)
where we made use of the spectral norm. Following the proposition of Kenney and Laub in [24], we can assert the following,
For a closed-loop system (A + BF) with a system uncertainty of ∆A and ∆B, the spectral norm of the distance to the
set of all unstable matrices S is given by
d(A + BF, S) ≤ (1 + ||F||)d((A, B), S).
Using (3), the above equation is equivalent to
d(A + BF, S) = min σn ([Ac − λI])
Re( λ)≥0
where Ac = A + BF. Therefore, we propose to design the robust controller as follows: find F such that the minimum distance
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Fig. 15.
IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 26, NO. 2, JUNE 2011
Controller architecture for optimal robust FL predictive control. Dashed lines are associated with off-line operations.
to uncontrollability is maximized subjected to the constraint
given in (21). Expressed in matrix form
Max{Min{σn ([A + BF − sI, B])}}
subjected to
Min{σn ([A + BF − λI]) } ≤ (1 + ||F||)
+ Min{σn ([A − s∗ I, B])}
and Ireal m i n ≤ x∗ ≤ Ireal m a x and Iim ag m i n ≤ y ∗ ≤ Iim ag m a x .
The overall controller is achieved by combining the proposed
robust controller with the proposed FL controller. In this fashion, we can address the short-term fluctuations of the waves
by tuning the optimal damping force of the WEC device using
the FL controller, and the long-term fluctuations are addressed
by the GA, while inaccuracies in the model and the variations
of conditions are in part compensated by the proposed robust
controller. The resulting controller architecture/design is given
in Fig. 15. The implementation of this controller design is done
using a simple continuous GA (see [21]) that finds the appropriate minima for the defined cost function. The controller design
for the robust control portion is based on a linear quadratic (LQ)
controller. We have to pay special attention to the plant input,
since only one input will be used to control the WEC, while
both inputs are used to compute the control force.
Considering the input to the WEC as u = [ z Fl ]T , where FL
is the damping force, the state-space form of the system can be
expressed as follows:
K1
K1
ẋ = A − B
x and y = C − D
x
K2
K2
where we partitioned the feedback-gain matrix according to the
input u. The controller will not have any influence on the wave
force exerted onto the WEC; hence, K1 is set to zero and the
Fig. 16. Accumulated absorbed energy using robust controller and no
controller.
TABLE II
SIMULATION RESULTS FOR ACCUMULATED ABSORBED ENERGY FOR 200
SECONDS, WITH AND WITHOUT HYBRID ROBUST FL CONTROL
closed-loop system representation can be given as
ẋ = [A − B1 K2 ]x
y = [C − D2 K2 ]x
with B = [ B1 B2 ] and D = [ D1 D1 ]. Provided that u2 is
sufficient to reach all system states, the updated optimization
becomes: Find K2 such that
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637
where the output error model is given as
y(k) =
Fig. 17.
Buoy motion with and without robust FL controller.
J1 = Max{Min{σn ([A + B1 K2 − sI, B1 ])}}
(24)
subjected to
Min{σn ([A + B1 K2 − λI])} ≤ (1 + ||K2 ||)
+ Min{σn ([A − s∗ I, B1 ])}
and Ireal m i n ≤ x∗ ≤ Ireal m a x and Iim ag m i n ≤ y ∗ ≤ Iim ag m a x . In
the following, the introduced robust controller is implemented
in discrete time.
We use the traditional discrete time LQR controller design
for K2 , which is given as follows:
JLQ R =
xT (k)Qx(k) +
uT (k)Ru(k)
(25)
where Q and R are used as the variables in the GA in order to optimize the discrete time version of (24). The energy production
is given by the following cost function:
ẏ T (k)cPTO (k)ẏ(k).
(26)
JE =
The overall optimization can be expressed by: maximize the
minimum singular value of the closed-loop system (24) while
maximizing the energy output of the WEC (26) using as little
control energy as possible (25).
The identification of the model is accomplished once at the
start of the operation of the WEC and may be repeated after
some time to update the system model. The identification is
based on the actuation force and the wave as inputs, where the
actuation force is varied randomly with increasing amplitude
according to F (k) = {10 × 1.025k } × η, where η = N (1, 0).
The discrete time model can be given as an output error model
with the following polynomials:
B(q)
u(k) + e(k)
H(q)
(27)
and e(k) is the residual vector. The proposed controller is implemented in two phases to investigate each component, i.e.,
the robustness issue and the short-term interaction of the FL
controller. Fig. 16 depicts one simulation run for the robust controller portion in terms of energy absorbed. In the simulation,
the robustness is tested using a perturbation of the system model
characteristics, i.e., one of the parameters of the buoy model is
altered. In example, the WEC model assumes that the hydrostatic stiffness force is linear, which implies a constant waterplane area for the buoy. Since this is only an approximation, we
use this parameter input by varying it from its nominal value.
The controller is switched ON at t = 20 s, after completing the
identification task to extract the model.
For the simulation duration and selected sea state, the controller enables the system to convert more energy than the uncontrolled case (without altering the parameters on a continuous
base). Also to note is that the control action is powered from
the stored energy; hence, control forces aligned with the buoy
velocity reduce accumulated absorbed energy by the amount of
the control energy, while opposed control forces extract energy.
Perturbing the system as described earlier, the simulations are
repeated and the influence of the controller is compared to the
unperturbed case.
The resulting energy production loss due to the change (6%)
in the selected model parameter is 19% for the controlled case,
and 77% for the uncontrolled case. Combining the robust controller and the FL controller, as outlined in Fig. 15, the uncertainty influence can be quantified through simulations. The
optimal stiffness and damping values computed with a GA for a
given sea state are used as the nominal values, while perturbing
the resulting hydrostatic stiffness force as the uncertainty in the
WEC model and regulating the damping through the proposed
controllers. Table II summarizes the results for a varying perturbation of the hydrostatic stiffness force. Table II reveals the
influence of the proposed robust FL controller to changes in
absorbed energy by the WEC with respect to uncertainty in the
hydrostatic stiffness force. The controller has the effect to minimize the affect such an uncertainty may have in the operation
and ultimately the energy production of a WEC.
Fig. 17 depicts the comparison of the buoy motion for the case
of using the combined (hybrid) robust FL controller (turned ON
at k = 650) and no control for nonoptimized parameters (stiffness and damping). The small changes indicated by differences
in the amplitude and the phase between the controlled and uncontrolled case cause substantial differences in the energy conversion as indicated by Fig. 16 extrapolated for long durations
of operations.
B1 (q) = 0.05616q −1 − 0.04712q −2
B2 (q) = −4.523 × 10−8 q −1 + 4.518 × 10−8 q −2
H1 (q) = 1 − 1.988q −1 + 0.9893q −2
H2 (q) = 1 − 2q −1 + 1.002q −2
VI. CONCLUSION
The two proposed prediction algorithms (Kautz/AR and the
Kautz) indicate to perform in a competitive fashion when compared to existing prediction models. The reduced model or-
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IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 26, NO. 2, JUNE 2011
der of the proposed prediction filters will help lower the rather
high computational load imposed by conventional filters. For
the operation of WECs in irregular waves, two controllers are
proposed, each having a different objective. The FL controller
addresses the short-term control, while the robust controller attempts to minimize the effects of the fluctuations or errors of
the WEC model parameters on the energy absorption. The control architecture proposed requires the prediction of wave height
data as well as the optimization of the structural parameters of
the vibrational system, which is accomplished with a simple
GA. Identification of the model is achieved off-line and may be
repeated after some time when wave conditions have changed.
Simulation results for each controller are presented separately
before the combined robust FL controller is tested. The proposed
combined controller has the effect to limit the excursion of the
buoy motion to the predesigned limits, while maximizing the
energy output. In addition, uncertainties in the model have a
more limited effect on the energy production when the proposed
controller is used.
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Marco P. Schoen (SM’10) was born in 1965. He
received the B.S. degree in mechanical engineering
from the Swiss College of Engineering, in 1989, the
M.E. degree in mechanical engineering from Widener
University, Chester, PA, in 1993, and the Ph.D. degree in Engineering Mechanics from Old Dominion
University, Norfolk, VA, in 1997.
From 1997 to 1998, he was a Faculty Member
at Lake Superior State University, and from 1998 to
2001, he was a Faculty of the Mechanical Engineering program at Indiana Institute of Technology. Since
2001, he has been with Idaho State University, Pocatello, where he is currently
a Professor and Chair in the Department of Mechanical Engineering, and an Associate Director of the Measurement and Control Engineering Research Center
(MCERC). He has been an Associate Editor of the Journal of Dynamic Systems,
Measurement and Control. His research interests include controls and vibration
of biomedical and aerospace systems as well as energy-related problems.
Dr. Schoen has been the Past Chair of the Model Identification and Intelligent
Systems (MIIS) Technical Committee for the American Society of Mechanical
Engineers (ASME).
Jørgen Hals was born in 1974. He received the M.Sc.
degree in physics from the Norwegian University of
Science and Technology (NTNU), Trondheim, Norway, in 1999, and the Ph.D. degree from Norwegian
University of Science and Technology (NTNU), in
2010. His Ph.D. study was focused on renewable energy (wave energy conversion).
He was a Research Fellow at NTNU and a Researcher at SINTEF Energy Research, where his work
was focused on a wide range of energy technologies.
He is currently a Postdoctoral Fellow at the Center
for Ships and Ocean Structures (CeSOS), NTNU. His research interests include
mathematical modeling and optimal control of wave energy converters.
Torgeir Moan was born in 1944. He received the
M.Sc. and Ph.D. degrees, in 1968 and 1976, respectively, from the Department of Civil Engineering,
Norwegian University of Science and Technology
(NTNU), Trondheim, Norway.
Since 1977, he has been a Professor of marine
structures at NTNU, where he has been the Director
of the Research Center of Excellence (CeSOS), since
2002. He was a Visiting Professor at Massachusetts
Institute of Technology for one year, at University of
California, Berkeley for two years, and a part-time
Adjunct (Keppel) Professor at the National University of Singapore during
2003–2008. He has authored more than 400 scientific papers, delivered more
than 20 keynote, plenary lectures in international conferences and award lectures, as well as educated 50 doctoral candidates. He is an Editor of the Journal
of Marine Structures and is on the Editorial Board of eight other journals. His
research interests include stochastic dynamic modeling and reliability and analysis (relating to all kinds of marine structures), and facilities for offshore wind
and wave energy.
Dr. Moan has been an Elected Member of all the national academies of
science and letters in Norway and a Foreign Fellow of the Royal Academy
of Engineering, U.K., as well as a Fellow of several international professional
societies. He has received numerous research prizes.