The Second International Conference on Control, Instrumentation and Mechatronic Engineering (CIM09) Malacca, Malaysia, June 2-3, 2009

Wavelet Based Optimal Control Solution for Nonlinear Systems

Sasongko Pramono Hadi Dept. Electrical Engineering &

Information Tech. Gadjah Mada University Indonesia

sasongko@ugm.ac.id,

Soedjatmiko Dept. Electrical Engineering &

Information Tech. Gadjah Mada University Indonesia

djatmiko@mti.ugm.ac.id

Warindi Dept. Electrical

Engineering University of Mataram Indonesia

warindi_mtr@yahoo.co.id

Abstract

The main objective of the research is to show the advantages of wavelet based method from conventional ones to solve a nonlinear problem in a photovoltaic solar power generation. This non linearity comes from a case of limited power of solar radiation and expensive cost of solar panel, therefore maximization of absorbed power is become important.

To solve the problem, the system state space approach is used. The optimal control problem is transformed and reduced to a boundary value problem using Hamiltonian equation derivation. To increase computation speed, a wavelet analysis is used as time discretization adaptation on numerical solver that uses time discretization basis. The wavelet is employed as analysis tool at each discretization level, start from coarse to finer discretization. The temporary grid result at present level is then used for determining the next level discretization.

The final results of determining maximum power, prove that optimal control scheme can be used to solve control problem of solar power generation. The computation results also show that computation speed increases significantly by employing wavelet adaptation on the conventional solver without decreasing its accuracy.

Key words: optimal control, nonlinear system,

wavelet analysis 1. Introduction

In general nonlinear system is any problem with variables that can be represented as linear summation from independent components. Nonlinear system can be classified by natural nonlinear system and intended nonlinear system i.e. for special purposes. Example for this is optimal control. Research for control system

with nonlinear approach has been investigating more and more use to most real system is nonlinear (Ogata, 1996, and Wikipedia, 2008). .

In the other hand, most of nonlinear problem can not be solved with analytical method but should require numeris methods. Unfortunately numerical method has a drawback in longer computational time. Optimal control technique is a process for determining control signal and state trajectory in dynamical systems along certain time period for minimizing performance index. Then a numerical solver can be applied after the optimal control problem converted into a boundary value problems (BVP) i.e. differential equation problem with some constrained in initial, end, or the trajectory (Bonnard and Caillau, 2006 and Becerra, 2008) .

Current Algorithms for control grow along with signal processing algorithms. A relatively new algorithm called Wavelet appears in nonlinear signal recognation and processing. Some researches show that Wavelet based algorithm is suitable for signal processing that has non linearity (Pereyra and Mohlenkamp, 2004).

The objective of the research is to compare some conventional method with wavelet based method to solve an optimal control problem that found in a photovoltaic solar power generation control. This problem appears due to limited power of solar radiation and expensive cost of solar panel, therefore maximization of absorbed power become important. 2. Literature Review

Park and Scheeres (2003) shows that solving optimal control problem can be done through Boundary value problem (BVP). BVP can be derived from Hamiltonian equations.

In signal analysis Wavelet method is a relatively new compare to popular Fourier analysis. Gargour and

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The Second International Conference on Control, Instrumentation and Mechatronic Engineering (CIM09) Malacca, Malaysia, June 2-3, 2009 Ramachandran (1997) give basic Wavelet theory. The basic different between Wavelet and Fourier is that Wavelet is more suitable for non-periodic signal analysis, e.g. signal that have non linearity.

A research by Caillau and Noailles (2000) discusses BVP solution in general with help of wavelet computation in time discretization algorithm basis. The wavelet contribution is at time discretization shooting that used in multiple-shooting method. Each iteration discretization is repeated and adapted according to wavelet analysis in previous iteration solution.

Mahout and Boitier (2003) propose a model with state equation of a quadratic nonlinear solar power system. But they still use method of partial linearization to get the control signal. Some techniques to reach the maximum power in solar power generation system have largely discussed. According to Esram and Chapman (2006), there are hundreds of research or manuscripts about that from 1968 to 2005. Three techniques that mostly use is Hill-climbing or perturb and observe (PO), incremental conductance and fractional open-circuit voltage. PO is a method that gives specific reference voltage and observes current change at a solar array or module.

Most of the method is based on linear approximation. But there is a nonlinear method that has better accuracy due to most of the real cases is nonlinear in nature. Most nonlinear problem can not be solved analytically and mostly use numerical approach. The famous nonlinear system solver is based on newton method. It usually uses single shooting or multiple shooting schema. Some other methods also available, one of them is the collocation method. This method is currently available to solve ordinary differential equation, partial differential and integral equations. The basic idea is to choose limited dimension space from solution candidate (usually polynomials up to some level) and several points in a domain (namely collocation points) and chooses the solution that fulfill the given equation at the collocation points (Wikipedia, 2008).

Boundary Value Problems solver that base on time discretization i.e. multiple shooting newton method and collocation method need time slicing to smaller parts. Each small part could have different dynamic phenomena. Therefore a non uniform slicing or adaptation is likely to be appropriate method. This adaptive method can be done using wavelet.

2. System Modeling

Overall system e.g. solar cell, power converter, and load compose solar photovoltaic power generation that

is shown in figure1.

u

D1

Cs

rB

VBD2

Ce

L(ns/np)Rsh

PV array Battery

Figure 1. Model of solar photovoltaic power

generation system

Suppose iL and Vc are state variable and each is noted as x1 and x2, the state equation for boost converter can be written as:

x1V pL

uL

x2 (1)

x21

C s r Bx2 V B

uCs

x1 (2)

If optimal control is defined by solar power maximization Pp then

P p V p I p (3) The problem also has final boundary condition (at tf) as voltage and current at maximum power Vmpp and Impp. Hamiltonian equation, H is formulated as follows:

H 0.5u2 1V sL

uL

x 2 2uCs

x1V B x2Cs rB (4)

The condition at final time is 1 x1 k1 where k1 is determined from maximum power calculation model, therefore maximization is

V p x1 k1 (5) From equ. (5) then the complete performance index

become:

min J V p x1 k112 t0

t f

u2 dt (6)

The necessary condition for reaching maximum Pp in t0 to tf time are costate equation that contain objective function. The costate are:

1 H 1 2u

Cs (7)

2 H 2 1uL

2

Cs rB (8)

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The Second International Conference on Control, Instrumentation and Mechatronic Engineering (CIM09) Malacca, Malaysia, June 2-3, 2009 The final condition at tf are:

1 tf 1 V p (9) 2 tf 2 0

The sufficient condition for optimal control is satisfied if derivation of the Hamiltonian H to u is 0, dHdu

u 1x2L 2

x1C s

0 (10)

u 1x 2L 2

x1Cs (11)

The choosed solvers for BVP is collocation method and Newton single shooting and then in this research are called classical method. The algorithm efficiency is measured as time complexity or O (big oh). For detail about it can be seen in [2] and [16].

3. Numerical Experiments

The research material is optimal control of solar photovoltaic power generation system as proposed in Mahout and Boitier (2003). In this experiment the system parameters are as in table 1, 2, and 3.

Table 1. Photovoltaic panel parameters Number of cell in parallel, np 1

Number of series cell, ns 36

Short circuit current at 25C, Isc 3,8 A

Open circuit voltage, Voc 21,06 Volt

Diode quality factor, A 1,2

Boltzman const., k 1,38 x 10 –23

Temperature, T 25 ºC

Electron charge, q 1,6x10-19 C

Table 2. DC-DC converter parameters

Inductance, L 2,2 mH

Capacitance, Cs 30 µF

Table 3. Battery parameters Internal Resistance, rB 0,5 Ω

Battery voltage, VB 24 Volt Numerical experiments is used an application

software MATLAB® and Wavelab packet that can be downloaded freely from Stanford University website.

In this research we use Wavelab850, operating system Windows XP, and personal computer with Intel Pentium 4 processor and RAM 384 MB.

The global research or experiment flow is shown in figure 2.

Optimal control formulation

Mathematical models

Conventional solverWavelet based adaptation

Performance evaluation

Conventional Method choosing

Performance evaluation

Wavelet solver

Comparison and discusion

Figure 2. Research flow 4. Result and Discussion

Experiment with a change for maximum power from irradiance of 0.5 Sun to 1 sun is conducted for all method. From maximum power at initial condition t0 is found:

Pmpp 29,7576 Watt;

V mpp 16,8055 Volt; I mpp 1,7707 Ampere.

Final condition at tf is defined as:

Pmpp 60,4582 Watt;

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The Second International Conference on Control, Instrumentation and Mechatronic Engineering (CIM09) Malacca, Malaysia, June 2-3, 2009

V mpp 17,1111 Volt;

I mpp 3,5333 Amp.

From intial and final condition can be determined

x1 t 0 = 1,7707 A and x1 t f = 3,5333 A x2 t 0 = VB = 24 Volt (same as battery voltage).

The result of trajectory of state variable i.e. PV current (x1) and output voltage (x2) is shown in figure 3 and 4.

0 0.2 0.4 0.6 0.8 1

x 10-3

1.5

2

2.5

3

3.5

4

waktu (detik)

x 1 (A

mp)

0 0.2 0.4 0.6 0.8 1

x 10-3

24

24.2

24.4

24.6

24.8

25

waktu (detik)

x 2 (V

olt)

Figure 3. State variable trajectory

0 0.2 0.4 0.6 0.8 1

x 10-3

30

40

50

60

70

waktu (detik)

Daya

pan

el su

rya

(Wat

t)

0 0.2 0.4 0.6 0.8 1

x 10-3

0.45

0.5

0.55

0.6

waktu (detik)

kend

ali, u

0 0.2 0.4 0.6 0.8 1

x 10-3

0

20

40

60

waktu (detik)

Daya

bat

ere

(Wat

t)

Figure 4 Power output, batere and optimal

trajektory control The results show that all method regardless the

execution time finally can reach good maximum power

at tf as 60.4586 Watt and 48.6039 Watt for battery. But the different is computation time. Table 4 shows the result summary of measured execution time in Big O basis.

Table 4. Time complexity of some algorithms

Method Time complexity

Classic O n1,4854 Haarlet O n1,2617

Daubechies O n1,2390 Coiflet O n1,2606

It can be seen that all three wavelet adaptation is better in computational time. They also better form Newton based solver as shown in figure 5. IVP stand for Newton initial value problem, colloc is collocation method without adaptation.

100 200 300 400 500 600 700 800 900 1000

5

10

15

20

25

number of inputs

time

time complexity

IVPCollocHaarDaubCoifSym

Figure 5. Time complexity of some optimal

control algorithm

Measurement result is related with adaptive discretization. The classical method has equally space discretization. But adaptive discretization are not uniform as shown by figure 6 for example of the Haar wavelet discretization

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The Second International Conference on Control, Instrumentation and Mechatronic Engineering (CIM09) Malacca, Malaysia, June 2-3, 2009

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

x 10-3

0

0.5

1J=

5

N =

32

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

x 10-3

0

0.5

1

J= 6

N

= 4

0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

x 10-3

0

0.5

1

J= 7

N

= 6

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

x 10-3

0

0.5

1

J= 8

N

= 1

03

waktu (detik)

Figure 6. Adaptive discretization with Haarlet.

In classical method the number of discretization grid is 28 +1 = 257. But Haar wavelet can redudce grid number to 103 grid only, for Coiflet 100 and even Daubechies dispose 184 grid and left only 73 in 4 iteration. It can be summarized that the more reduce grid the quickest computation.

5. Summary

Optimal control of solar power generation can be solved through BVP with Hamiltonian and produce optimal trajectory as expected.

In the case of power maximization all method produce the same result of 60.4586 W. It can be seen that adaptation do not reduce optimal result

Solving optimal control with wavelet approach can increase the computation efficiency and get quickest result significantly

6. References [1] Angulo-Nunez, M.I., and Sira-Ramirez, H., 1998, Flatness in the Passivity Based Control of DC-DC Power Converters” , Proc. 37th IEEE Conf. Decision & Control, 4115, 4120 [2] Anonim, 2008, “An Intoduction to Computational Complexity, http://users.forthnet.gr/ath/kimon/CC acses date 16 Juli 2008 [3] Bonnard, B. and Caillau, J.B., 2006, “Introduction to Nonlinear Optimal Control - Advanced Topics in Control Systems Theory”, Lecture Notes Control & Inform. Sci. 328, 1, 60, Springer-Verlag, London.

[4] Buckheit, J. Chen, S, Donoho, D., Johnstone, I., and Scargle, J., 2005, “About Wavelab”, Software Manual, Stanford University [5] Becerra, V.M., 2008, “Optimal Control” , Scholarpedia, 3(1) , 5354; http: //www.Scholarpedia.com, tanggal akses 6 Februari 2008 [6] Caillau, J.B., and Noailles, J., 2000, “Wavelets for Adaptive Solution of Boundary Value Problems”, Proc.16th IMACS World Congress, 1, 6. [7] Clark, R.N., and Vick, B.D., 1997, “Performance Comparison of Tracking and Non-Tracking Solar Photovoltaic Water Pumping Systems” Proc. ASAE Intl. Meet. 974003 [8] Chaplais, F., and Petit, N., 2008, “Inversion in Indirect Optimal Control of Multivariable Systems”, Proc. ESAIM COCV,14, 294, 317 [9] Diehl, M., Bock, G.H., and Schlöder, J.P., 2005, “Real-Time Iterations for Nonlinear Optimal Feedback Control”, Proc. 44th IEEE Conf. Decision & Control. [10] Esram, T., and Chapman, P.L., 2007, “Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques”, IEEE Trans. Energy Conversion, 22, 439, 449 [11] Gargour, C.S. and Ramachanchan, V., 1997, “A Scheme for Teaching Wavelets at the Introductory Level”, Proc. 27th IEEE Education Conf., Teaching And Learning in an Era of Change, Vol.2, 1046, 1050. [12] Mahout, V., and Boitier, V., 2003, “Nonlinear Control of a Photovoltaic Converter”, Proc. 11th Mediterranean Control & Automation. [13] Ogata, K., 1996, Modern Control Engineering, 2nd ed., Prentice Hall Inc [14] Park, C. and Scheeres, D.J., 2003, “Solutions of the Optimal Feedback Control Problem Using Hamiltonian Dynamics and Generating Functions”, Proc 42nd IEEE Decision & Control, 2, 1222, 1227. [15] Pereyra, M.C. And Mohlenkamp, M.J., 2004, Wavelets, Their Friends and What They Can Do, Lecture Notes, University of Colorado [16] Perkowitz, M., 1999, “Time Complexity”, http://www.8.org/w8-papers/2bcustomizing/towards /node8. html tanggal akses 16 Juli 2008 [17] Raja. P, and Ashok. S, 2007, “Modified PAO Algorithm for Maximum Power Point Tracking of PV Systems”, newsletter IEEE Power Electronics Soc.

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The Second International Conference on Control, Instrumentation and Mechatronic Engineering (CIM09) Malacca, Malaysia, June 2-3, 2009 [18] Sera, D., Kerekes, T., Teodorescu, R., and Blaabjerg, F., 2005, “Improved MPPT algorithms for Rapidly Changing Environmental Conditions”, [19] Wikipedia, 2008, “Nonlinear System”, wikipedia, http://www.wikipedia.com, tanggal akses 6 Februari 2008

[20] Walker, G., 2001, “Evaluating MPPT Converter Topologies Using A Matlab PV Model”, IEE Australia Jour. of Electrical & Electronics Engineering, 21, 1, 49, 56.

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