fast steering mirror control using embedded self-learning fuzzy controller for free space optical...

Wireless Pers Commun (2014) 76:643–656DOI 10.1007/s11277-014-1731-1

Fast Steering Mirror Control Using EmbeddedSelf-Learning Fuzzy Controller for Free Space OpticalCommunication

Bilal Ahmad Alvi · Muhammad Asif · Fahad Ahmed Siddiqui ·Muhammad Safwan · Jawad Ali Bhatti

Published online: 29 March 2014© Springer Science+Business Media New York 2014

Abstract Coarse and Fine tracking in free space laser communication is very crucial. Thispaper presents architecture for laser beam acquisition, tracking and pointing mechanism. Thecentroid of the received image beam is calculated and then error is computed using referenceposition. Embedded self-learning fuzzy controller (SLFC) is used to drive the fast steeringmirror mechanism to point the laser beam on receiver detector. The SLFC is embedded inhardware using Arduino development board. The performance of the SLFC is comparedwith the standard PID control. It is shown that the proposed architecture successfully trackthe laser beam and the SLFC shows the robustness against model uncertainties and rejectdisturbances.

Keywords Free space optics · Neuro-fuzzy control · Image processing · Fast steeringmirror control

1 Introduction

In recent years, the dependence of our social life has been concentrated upon devices andapplications heavily reliant on access to Internet. These devices range from desktop PC,

B. A. Alvi (B) · M. Asif · F. A. Siddiqui · M. Safwan · J. A. BhattiElectronic Engineering Department, Sir Syed University of Engineering and Technology,Karachi, Pakistane-mail: [email protected]

M. Asife-mail: [email protected]

F. A. Siddiquie-mail: [email protected]

M. Safwane-mail: [email protected]

J. A. Bhattie-mail: [email protected]

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laptop to hand-held smart phones. Day by day applications are becoming bandwidth hungry.Especially the smart phone era is proving to be the era of Internet dependence as almostall the applications these days require some sort of Internet connectivity in order to provideproductivity. Most of the users are addicted to the habit of staying connected and frequentlyupdating their activities on social networks [8].

In order to satisfy user’s demands of broad band access, various devices of communicationnetwork needs to be linked to develop a ubiquitous network. To achieve universal platformconnectivity between wireless access system and optical fiber core network has been aroundand commonly known as radio over fiber (RoF) [4]. This platform is widely used in cellularphone and other broadband networks. This concept can further be utilized for free spacechannels by applying free space optical communication techniques combined with RoF.These systems make a use of traditional optical fiber network technology and devices suchas Erbium doped fiber amplifiers (EDFAs), single mode fiber (SMF) and DWDM technique.

All free space optic (FSO) systems operate at wavelength 1,550 nm and single mode fiberfrom the termination is directly connected [5]. The optical beam is radiated directly fromconnected fiber end to the atmosphere using FSO optics. The receiver accepts the transmittedoptical beam and can focus it directly into the core of fiber. Radio over free space optics(RoFSO) system may transmit light signals and it does not require any conversion fromoptical to electrical or vice versa at fiber connection part. This greatly enhances the systemthroughput.

This system can replace fiber from RoF system, to transmit optical signals that are confinedvarious kinds of wireless service signals without modulation through free-space. It also canbe an attractive means for RF signal transmission and may find significant applications inareas such as Metro network extension, Last mile access, Fiber Back Haul, Fiber backup andInterconnectivity of distributed antenna systems.

In order to achieve seamless connection with an optical fiber network avoiding any changesin the transmission signal formats, all-optical connection technology which combines a free-space optical propagated beam at 1,550 nm and single mode fiber (SMF) is essential [13].Signal transmission in the free space encounters a lot of noise and distortion due to varietyor reasons including scintillation noise, atmospheric pressure and ambient temperature [15].Even slight variation in these parameters can significantly distort the signal being sent overfree space.

However, there are many challenges to directly connect the free-space propagated beamdirectly to the SMF core. Some of the challenges related to radio over free space opticinclude incorporation of high speed and highly precise tracking mechanism [14]. In additionto effective beam tracking system, an efficient method (active tracking) for focusing the lightinto the SMF at the receiver is required to maintain alignment of the received optical signalto the SMF [6,9]. Further, it is required to develop tracking algorithm to rectify misalignmentissues [7]. The objective of this research is to develop a model of free space optical (FSO)communication system incorporating a self-learning fuzzy tracking system to improve theaccuracy of the atmospheric fluctuation suppression.

Traditional adaptive controller design technique uses high gain mechanism in feedbackloop to compensate uncertainties and disturbances whereas self-learning fuzzy controlleruses parallel and distributed learning mechanism to approximate the nonlinear function eitherusing fuzzy logic system or neural network [1]. The main advantage of fuzzy controller isthat it doesn’t require any mathematical model and linguistic information can be directlyincorporated into the controller. So, fuzzy controller has attracted numerous attention and anumber of fuzzy based controllers have been proposed using fuzzy–fuzzy [10], neuro-fuzzy[11], and wavelet-fuzzy [3]. The main drawback of these techniques is that they are com-

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plex and requires a lot of computational power. This implies that it is difficult to implementthese techniques on off-the-shelf microprocessor or microcontroller based system. The mainmotivation of this work is to implement a computationally effective self-learning fuzzy con-troller which provide robustness against the model uncertainties and disturbances and can beimplemented using off-the-shelf controller boards.

This paper proposed a fast steering mirror control for free space optical communicationusing self-learning fuzzy control. The system use two axis fast steering mirror (FSM) andimage base sensor to localize the optical beam. The error calculated based on image sensor isthen use to actuate the fine pointing mechanism for beam alignment using self-learning fuzzylogic. The advantage of using the self-learning fuzzy control algorithm is that it provides therobustness in the system by adapting to the inverse dynamics of the system.

Fuzzy controller is based on rule base created by using the expert opinion and consideredless dependent on actual system model. It can deal with variations in the system model with thereal world system. The self-learning fuzzy controller is based on fuzzy relational model thatis trained online in order to estimate the inverse dynamics of the plant. The main contributionsof this work are listed as: (1) Proposed an architecture for laser beam acquisition, tracking andpointing mechanism, (2) beam image profile is detected using image processing algorithm(3) a self-learning fuzzy controller is designed and implemented on Arduino mega 2,560.

The breakup of the further work is structured as follows: Sect. 2 discusses the architectureof the tracking system. Sect. 3 presents the beam image profile and centroid detection. Sect. 4discusses the self-learning fuzzy tracking system. The simulation results and discussions arepresented in Sect. 5. Finally Sect. 6 provides the conclusion.

2 Architecture of Tracking System

The block diagram of the complete tracking system is shown in Fig. 1. It includes faststeering mirror (FSM), CCD sensor, beam splitter, data receiver and fuzzy logic controller.Data receiver and CCD sensor are connected on a rigid platform and aligned to receive thelaser beam. The FSM is controlled using two DC motor actuators which is use to align abeam on data receiver. The CCD sensor array detects the position of beam in an image profile

Fig. 1 System model of FSO system

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using centroid detection. The error is calculated with respect to the reference position andfed into the fuzzy controller. Fuzzy controller calculates the actuator outputs to control theFSM dc motors.

3 Beam Image Profile and Centroid Detection

The initial and most important task in FSO communication is to track the error between thecurrent position and the reference point. In order to track this error a laser beam is usedbetween both nodes which falls on a CCD sensor array. One node transmits the laser beam,while on the other node, the received laser beam is projected on the CCD sensor array whichtransform it into the digital image profile. The beam image profile is represented by usingfollowing function

I (x, y, z) ∈ (0, 1) (1)

where x = 0, 1, . . . , M , y = 0, 1, . . . , N and z = 0, 1, 2 are the dimensions of RGB imageand I (x, y, z)represents the pixel intensity. Figure 2 shows the images captured using CCDcamera to detect the position of laser beam. It can be seen that images are noisy and it isimportant to do filtering before detecting the position. Therefore, the images are breakdowninto red, green and blue planes and then converted into the binary images using integralimage thresholding [2]. The resulting binary images of red, green and blue planes are shownin Figs. 3, 4 and 5 respectively.

Fig. 2 Images captured using CCD camera to detect the position of laser beam

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Fig. 3 Binary image using red image beam profile

IBinar y (x, y) = IB R (x, y)∧

IBG (x, y)∧

IB B (x, y) (2)

where IBinar y (x, y)is the filtered binary image, IB R (x, y) is the binary image obtained usingred plane only, IBG (x, y)is the binary image obtained using green plane only, IB B (x, y)isthe binary image obtained using blue plane only and

∧is the logical end operator. The

filtered images obtained using Eq. 2 is shown in Fig. 6. The centroid of the final images iscalculated using following function

centroid =(

μ1,0

μ0,0

μ0,1

μ0,0

)(3)

where μ0,0 represents the 0th moments and can be calculated using

μ0,0 =M∑

x=0

N∑

y=0

IBinar y (x, y) (4)

and μi,j is the 1st moments, i, j ∈ [0, 1] and i �= j . The 1st moments can be obtained using

μ1,0 =∑M

x=0∑N

y=0 x IBinar y (x, y)

μ0,0(5)

μ0,1 =∑M

x=0∑N

y=0 y IBinar y (x, y)

μ0,0(6)

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Fig. 4 Binary image using green image beam profile

Figure 7 shows the typical beam profile images with centroid which representsxandposition of the received laser beam on CCD sensor array.

4 Self-Learning Fuzzy Tracking Control

The centroid of laser beam detected using centroid detection is used to calculate the errorsignal which has to minimize using the self-learning fuzzy controller. The output of thedesigned controller has to control the motors which derive the FSM in order to adjust theazimuth and elevation angels of the mirror. This is mandatory for the freespace opticalcommunication, as without the proper alignment, two nodes cannot communicate properlyand the latency will increase sufficiently. The generalized model of proposed FSM mechanismis represented as

x (t) = f (x, t) + � f (x, t) + δ (t) + u(t) (7)

where x = [x1, x2, . . . xn]T ∈ � is the state vector and u (t) ∈ � is the control input ofsystem, δ f (x, t) represents system uncertainties and δ (t) is the bounded disturbance, i.e.,

‖δ (t)‖ ≤ ε

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Fig. 5 Binary image using blue image beam profile

where ε is the small positive constant. The control objective is the tracking the received beamprofile with respect to the reference such that

limt→∞ ‖e(t)‖ = lim

t→∞ ‖xd (t) − x(t)‖ → 0 (8)

where e(t) represents the tracking error, xd (t) is the target position, x (t) is the currentposition and ‖ · ‖ is the Euclidean norm of a vector.

The control objective is achieved by using self-learning fuzzy logic control (SLFC) track-ing system [12]. SLFC does not require a system model and accommodate nonlinearities andinput/output disturbances. SLFC uses an adaptive feed-forward fuzzy controller with onlinelearn mechanism to adapt the inverse plant dynamics and to compensate disturbances. APID control is used to provide the close-loop stability during online training of feed-forwardcontroller. In addition, it compensate for model mismatches as an exact inverse mapping isdifficult. The block diagram of SLFC is shown in Fig. 8 with desired inputs and outputs.The reference model is used to filter the desired changes in plant output in order to make thesetpoint trajectory achievable. The form of control law for FSM is as follows

u (t) = u f (t) + kpe (t) + ki ∫ e (t) dt + kdde(t)

dt(9)

A fuzzy relational model (FRM) is used to calculate the feed-forward control output u f (t).A FRM is a predefined set of linguistic rules, each of which has an associated rule confidencewhich is stored in a fuzzy relational array(R). The fuzzy relational array is define between

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Fig. 6 Filtered images of laser beam profile

the range 0–1,which indicates that confidence that associated fuzzy rule correctly describesthe fuzzy relation between inputs and the outputs of system being modeled. Assuming thatthe fuzzy set have 50 % overlap, the rule base of FRM can be described as

IFx(t) is Ai ANDu f (t − 1)is B j THENy(t)is X K (R[i, j, k])and

u f (t) = u f (t − 1) + γ e(t) (10)

where γ is the learning rate. The on-line learning mechanism is implemented using themodified RSK scheme [12]. RSK is one of efficient and computationally simple schemesfor estimating the rule confidences in the presence of sensor noise and disturbances. Therecursive form of RSK can be defined as

RA1,...,An (t) ={

R1 (t)R2 (t)

i f f A1,...,An(x (t)) �= 0otherwise

(11)

where

R1 (t) = f A1,...,An (x (t)) µB j (uD (t)) + λRA1,...,An (t − 1) F A1,...,An (t − 1) ,

R2 (t) = RA1,...,An (t − 1)

f A1,··· ,An (x (t)) = μA1 (x1) .μA2 (x2) . . . . .μAn(xn)

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Fig. 7 Filtered binary images with centroid detection

Fig. 8 Block diagram ofself-learning fuzzy control

and λ is the forgetting factor. The recursive F-array function F is defined as

F A1,...,An (t) ={

f A1,...,An (x (t)) + λF A1,...,An (t − 1)

F A1,··· ,An (t − 1)

i f f A1,··· ,An(x (t)) �= 0otherwise

(12)

The elements of the F-array are indicators of how strongly and how often each combinationof the inputs has occurred in the training data. Using the height defuzzification, the outputof the feed-forward controller, u f (t) is given by

u f (t) =∑q1

i1=1 · · · ∑qnin=1 f A1,...,An

[RA1,...,An,B1U1 + RA1,...,An,B2U2

]

∑q1i1=1 · · · ∑qn

in=1 f A1,...,An

[RA1,...,An,B1 + RA1,...,An,B2

] (13)

where U j , j ∈ [1, 2] is the position of the apex of the output set.

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5 Result and Discussion

The objective of aligning the FSM using the centroid detection method is implemented usingthe SLFC which is implemented using an Arduino Mega board. X-vision CCD camera isused to capture the laser beam images and then position of the laser beam on image plane isdetected using image processing algorithm on MATLAB. The self-learning fuzzy control isimplemented in Simulink and then embedded into Arduino mega 2,560 using target hardware.Arduino Mega is connected to physical hardware which drives the azimuth and elevationcontrol dc motor using PWM. The block diagram of the complete system is shown in Fig. 9.

The fuzzy relational model implemented with five by five triangular membership functionsto fuzzify the input with universe of discourse [−20 20] and apexes positioned at [−10 50 5 10]. The time constant of the reference model is set to 0.01 to ensure the FSM followthe desired trajectory with physical constraints. The PID controller, which controls the plantduring the learning cycle of SLFC, is tuned using the Ziegler-Nichols method which suggestthe initial gains as [25 10 5]. Therefore, the on-line learning rate γ is set using γ = K/T1

to ensure that the system remains stable during the initial learning phase, where K is theproportional gain and Ti is the integral time. The forgetting factor λ is set to 0.7 to optimizethe system memory. Figures 10 and 11 show the response of the self-learning controller forazimuth and elevation control motors respectively. It can be observed that PID controlleroutput is due to the fact that the fuzzy relational feed-forward controller is not able to modelthe entire dynamics. Overall, the self-learning controller is able to track the laser beam profilewhich is essential for FSO communication. It is also observed that both x and y axis controlshave slight variation due to fact that both control motors are supposedly similar in make,

Fig. 9 Block diagram of FSMphysical setup

Fig. 10 Self-learning fuzzy position control in X direction

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Fig. 11 Self-learning fuzzy position control in Y direction

Fig. 12 Time history of Rule Confidence R1–R9


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but all the physical systems are manufactured with non-linearity. This means the output ofboth motors varies slightly with respect to the manufacturing non-uniformity. The plots ofrule confidence is shown in Figs. 12, 13 and 14. It can be observed that the rule confidencecontinue to change whenever the associated rule is fire.

6 Conclusion

Coarse and fine tracking in free space laser communication is very crucial. In this paper,acquisition, tracking and pointing system for free space laser communication is proposedto rectify the misalignment issues in FSO. The system used CCD sensor array to detect thelaser beam in an image. Using centroid detection the laser beam located in an image. Theerror is computed with respect to the reference laser beam image. Self-learning fuzzy logiccontroller is used to derive the fast steering mirror mechanism to point the laser beam onreceiver detector for coarse and fine tacking. The fast steering mirror mechanism is derivedusing two dc motors. The adaptive fuzzy system is implemented in Arduino mega 2,560.The results shows that the proposed methodology is stabilize using fuzzy logic controller.PID controller, which is tuned using Ziegler–Nichols method, controls the plant while fuzzycontroller is learning the plant behavior. The output of fuzzy controller gradually increasesand start replacing the signal from the PID controller, and eventually taking over the controlcompletely.

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Bilal Ahmad Alvi is serving Sir Syed University of Engineering &Technology since May 2007 as Chairman, Department of ElectronicEngineering. He has more than 25 years of experience in the field ofElectronic Engineering and Optical Fiber Communication. He did hisM.S. and Ph.D. from the University of Salford, U.K. in the field ofOptical Fiber Communication and B.E. in Electronic Engineering fromNED University Karachi, Pakistan. He has worked on many importantprojects in Pakistan, Saudi Arabia and the U.S.A. He had also servedat the Pakistan Space and Upper Atmosphere Research Commission(SUPARCO) as Senior Engineer for more than 15 years. Dr. Alvi haspublished a number of research papers and chaired many local andInternational Conferences.

Muhammad Asif received B.S. degree in Biomedical engineeringfrom the Sir Syed University of Engineering and Technology, Karachi,Pakistan in 2003, and M.S. degree in Electrical and Electronic Engi-neering from the UniversitiSains Malaysia (USM) Malaysia, in 2007.Currently, he is pursuing his Ph.D. in Control Engineering from NUST,Pakistan. He is also working as faculty member and researcher in theDepartment of Electronic Engineering, Sir Syed University of Engi-neering and Technology. His research addresses the issues and prob-lems of industrial automation, navigation, mapping and design andimplementation of statistical control algorithm for autonomous robots.

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Fahad Ahmed Siddiqui is working as Assistant Professor in Elec-tronic Engineering Department at Sir Syed University of Engineer-ing and Technology. He is currently pursuing his Ph.D. in the fieldof control engineering. He did his M.S. in Electrical Engineering withPower Electronics from University of Bradford and B.E. in ElectronicsEngineering from Usman Institute of Technology. His research interestincludes power systems and control system design.

Muhammad Safwan received B.S. degree in Electronic Engineeringfrom Sir Syed University of Engineering and Technology, Karachi,Pakistan in 2012 and doing M.S. degree in Electrical Engineering fromGraduate School of Engineering Sciences and Information Technology,Hamdard University, Karachi, Pakistan. He is working as junior Lec-turer in Sir Syed University of Engineering and Technology, Karachi.He has a keen interest in the field of control engineering and adaptivesignal processing.

Jawad Ali Bhatti graduated from Sir Syed University of Engineer-ing and Technology in December 2002, Karachi, Pakistan. He com-pleted his M.S. from Hamdard University, Karachi, Pakistan in Sep-tember 2007. He is doing Ph.D. from Hamdard University, Karachi,Pakistan. Presently, he is working as Assistant Professor in Sir SyedUniversity of Engineering and Technology, Karachi, Pakistan. He hasresearch interest in image processing and object tracking.

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fast steering mirror control using embedded self-learning fuzzy controller for free space optical...

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