Neuro-Fuzzy System Technique for Obstructed ...

Neuro-Fuzzy System Technique for Obstructed Avoidance of Several Mobile Robot Hataitep WONGSUWARN Center of Innovative Mechatronic and Robotics (IMERs), Department of Mechanical Engineering, Kasetsart University (Kampangsean), Kampangsean, Nakhon Pathom, 73140, Thailand

ABSTRACT In this paper, navigation techniques for several mobile robot in presence of static and moving obstacles using fuzzy logic controller are investigated in a totally unknown environment. Fuzzy logic controller (FLC) based on Neuro-fuzzy system using difference membership functions are developed and used to navigate mobile robots. First a Neuro-Fuzzy controller (NFC) has been used with three types of membership function of five input members and three output members. Each robot has an array of infrared sensors for measuring the distances of obstacles around it. This task could be carried out specifying a set of fuzzy rules taking into account the different situations found by the mobile robots. . The approach is to extract a set of fuzzy rule set from a set of trajectories provided by human. For this purposes the input to all the NFC are left obstacle distance, right obstacle distance, front obstacle distance and target angle considered. The output from NFC is left wheel velocity and right wheel velocity of mobile robots is in use. The fuzzy rules help the robots to avoid obstacles and find targets. The robot considered for analysis is a three types of robot such as four-wheeled robot, six leg robot and boat robot. Three robots are same control method. The position and velocities of the robots dependent on two separate motors. For example, two motors are connected to two rear wheels separately in four wheeled robot. The direction and speeds of the wheels are being controlled by the motor controller interface. To verify the validity of the proposed scheme, some typical cases are simulated in which a robot is to move from a given current position to a desired goal position in various unknown environments. In all cases the robot is able to navigate its way towards the goal while avoiding obstacles successfully. These techniques have been demonstrated in various exercises, which depicts that the robots are able to avoid obstacles as well. Amongst the techniques developed, Neuro-Fuzzy Controller (NFLC) with having Gaussian membership function was found to be most efficient foe mobile robots navigation. Keywords: Computational intelligence, Neurofuzzy system, Obstacle avoidance

Mobile

robot,

1. INTRODUCTION The field of Agricultural mobile robot is advancing very rapidly and navigation is one of the main issues in this field [1,2]. The mobile robot is constructed as manipulator for auto sensing which capable to navigate in an unknown topology with moving and stationary obstacles. In the design of autonomous mobile robot, two important must consider carefully. The first of which is design of a nonlinear controller, and the second deals with the system reliability. It can be stated that human experience represented by a set of linguistic rules for a possible solution to this kind of control problem [3]. Soft computing techniques such as fuzzy logic, neural network and genetic algorithm are considered for expressing the subjective uncertainties in human mind. Humans use perceptions of time, distance, speed, shape, and other attributes of physical and mental objects. Perceptions are described by propositions

drawn from a natural language, in which the boundaries of perceived classes are fuzzy. Using the fuzzy logic framework, the attributes of human reasoning and decision-making can be formulated by a set of simple and intuitive IF (antecedent)— THEN (consequent) rules, coupled with easily understandable and natural linguistic representations[4]. Many researchers have used fuzzy logic techniques in mobile robot navigation. Examples of work relating to fuzzy logic for the navigation of mobile robot are described below: Seraji et al. [4] investigated navigation techniques for several mobile robots as many as one thousand robots using fuzzy logic in a totally unknown environment. Fuzzy logic techniques using different membership functions were developed and used to navigate mobile robots. The result was tested in a simulated environment and it was found that fuzzy logic controller with Gaussian membership function is most efficient for multiple mobile robots navigation. Toda et al. [5] have described a navigation method, which employs sonar-based mapping of crop rows and fuzzy logic control-based steering for a wheeled mobile robot in an agricultural environment. Montaner and RamirezSerrano [6] have designed fuzzy logic controller for mobile robot navigation. They have used their technique on an experimental mobile robot, which uses a set of seven ultrasonic sensors to perceive the environment. Lee and Wu [7] have proposed a fuzzy algorithm to navigate a mobile robot from a given initial configuration to a desired final configuration in an unknown environment filled with obstacles. They have shown the feasibility of their proposed method in simulation as well as in experimental mode. In fuzzy system, If-then rules may be required to define the expert for this field. Thus, several successful reactive navigation approaches, the neuro-fuzzy provide a capable to mimic human experts as in fuzzy logic and learning from previous experience capability as in neural networks [3]. In this paper navigation of multiple mobile robots in presence of static and moving obstacles using different types of membership function in Neuro-Fuzzy Controller (NFC) is discussed. This task could be carried out specifying a set of fuzzy rules taking into account the different situations found by the mobile robots. At first, the approach is to extract a set of fuzzy rule set from a set of trajectories provided by human. For this purposes the input to all the NFC are left obstacle distance, right obstacle distance, front obstacle distance and target angle considered. The output from NFC is left wheel velocity and right wheel velocity of mobile robots is in use. The fuzzy rules help the robots to avoid obstacles and find targets. The robot considered for analysis are three type of wheeled robots having two motor actuators. The speeds of the wheels are being controlled by the motor controller interface. Results are presented to demonstrate the performance of the proposed approach. The paper is organized as follows: Section 2 presents the background about Neuro-Fuzzy system (NFs) and 3 Robot

platforms. Section 3 presents the NFs model for controlling two actuators. A section 4 is devoted to experimental investigations and the evaluation of obstacle avoidance models from NFC. This section provides the basis for the selection of different variables used in the model, and the structure of model. The main conclusions of the work are presented in Section 5, with remarks on future directions.

y ( x) 

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(6)

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2. NEURAL NETWORK AND NEUROFUZZY APPROACHES FOR THE TIME SERIES PREDICTION 2.1 Neurofuzzy System (NFs) for Modeling and Identification Both neural networks and the fuzzy system imitate human reasoning process. In fuzzy systems, relationships are represented explicitly in forms of if-then rules. In neural networks, the relations are not explicitly given, but are coded in designed networks and parameters. Neurofuzzy systems combine the semantic transparency of rule-based fuzzy systems with the learning capability of neural networks. Depending on the structure of if-then rules, two main types of fuzzy models are distinguished as mamdani (or linguistic) and takagi-sugeno models [8]. The mamdani model is typically used in knowledgebased (expert) systems, while the takagi-sugeno model is used in data-driven systems In this paper, we consider only the Takagi - Sugeno-Kang (TSK) model. Takagi, Sugeno and Kang [9] formalized a systematic approach for generating fuzzy rules from an inputoutput data pairs. The fuzzy if-then rules, for the pure fuzzy inference system, are of the following form:

if x1 is A1 and x2 is A2 and x N is AN then y  f ( x) (4) Where x  [ x1 , x2 ,..., x N ]T , A1 , A2 ,  , AN fuzzy sets are in the antecedent, while y is a crisp function in the consequent part. The function is a polynomial function of input variables x1 , x 2 , x3 , , x N . The aggregated values of the membership function for the vector are assumed either in a form of the MIN operator or in the product form. The M fuzzy rules in the form Eq. (4) are N membership functions 1 ,  2 , 3 , ,  N . Each antecedent is followed by the consequent: N

yi  pi 0   pij x j

(5)

j 1

Where

p ij

are the adjustable coefficients, for

i  1,2,3, , M and j  1,2,3,  , N . The first-order TSK fuzzy model could be expressed in a similar fashion. Consider an example with two rules:

if x1 is A11 and x2 is A21 and then y1  p11 x1  p12 x2  p10 if x1 is A12 and x2 is A22 and then y 2  p21 x1  p22 x2  p20 Figure 1 shows a network representation of those two rules. The nodes in the first layer compute the membership degree of the inputs in the antecedent fuzzy sets. The product node ∏ in the second layer represent the antecedent connective (here the “and” operator). The normalization node N and the summation node ∑ realize the fuzzy-mean operator for which the corresponding network is given in Figure 1 Applying fuzzy singleton, a generalized bell function such as membership function and algebraic product aggregation of input variables, at the existence of M rules the Neurofuzzy TSK system output signal upon excitation by the vector, are described by

Fig.1 An example of a first-order TSK fuzzy model with two rules systems [9] The adjusted parameters of the system are nonlinear parameters of bell function ( c (j k ) , (j k ) , b (j k ) ), the fuzzifier functions and linear parameters (weight) of the TSK function for every j  1,2,, N and k  1,2, , M . In contrast to the mamdani fuzzy inference system, the TSK model generates a crisp output values instead of fuzzy ones. This network is simplified. Thus, the defuzzifier is not necessary. So, the learning of Neurofuzzy network, which adapts parameters of the bell shape membership functions ( c (jk ) ,  (jk ) , b (j k ) ) and consequent coefficients, p ij

can be done either in supervised or self-

organizing modes. In this study, we apply a hybrid method which is one-shot least-squares estimation of consequent parameters with iterative gradient-based optimization of membership functions. The important problem in the TSK network is to determine the number of rules that should be used in modeling data. More rules mean better representation of data processing, but increased of complexity of the network and a high cost of data processing. Therefore, the procedure for automatically determining number of rules is required. In our solution, each rule should be associated with one cluster of data. Fuzzy c-means is a supervised algorithm, because it is necessary to indicate how many clusters C to looks for. If C is not known beforehand, it is necessary to apply an unsupervised algorithm. Subtractive clustering is based on a measure of the density of data points in the feature space [9]. The idea is to find regions in the feature space with high densities of data points. The point with the highest number of neighbors is selected as center for a cluster. The data points within a prespecified, fuzzy radius are then removed (subtracted), and the algorithm looks for a new point having the highest number of neighbors. This process continues until all data points are examined. In conclusion, Figure 2 summarizes the Neurofuzzy Networks System (NFs). Construction process data called “training data sets” can be used to construct Neurofuzzy systems. We do not need prior knowledge called “knowledge-based (expert) systems”. In this way, the membership functions of input variables are designed by the subtractive clustering method. Fuzzy rules (including the associated parameters) are constructed from scratch by using numerical data. And the parameters of this

model (the membership functions, consequent parameters) are then fine-tuned by process data.

In Figure 5, a six-leg-wheel hybrid mobile robot was designed to move under uneven terrains. There are also six motors for driving the robot, six servos for the leg-wheels. From a geometrical point of view, a wheel usually has a circular rim and a rotational axis located at the centre of the rim. The rim contacts the ground and the rotational axis connects to the robot body at a point hereafter referred to as the “hip joint.” In general, the wheel rotates continuously and the ground-contact point of the wheel is located directly below the hip joint with a fixed distance.

Fig. 2 Constructing Neurofuzzy Networks The advantage of the TSK fuzzy system is to provide a compact system. Therefore, some classical system identification methods, such as parameter estimation and order determination algorithms, could be developed to get the fuzzy inference rules by using input/output data. Similar to neural networks, Neurofuzzy systems are universal approximations. Therefore, the TSK fuzzy inference systems are general for many complex nonlinear practical problems, such as nonlinear modeling and system identification. 2.2 Robot Platform The robot considered for analysis is a three types of robot such as four-wheeled robot (Figure 3.), six leg robot (Figure 4.) and boat robot(Figure 5.). Three robots are same control method. The position and velocities of the robots dependent on two separate left and right controller. Each robot has an array of infrared sensors for measuring the distances of obstacles around it. In Figure 4, the robot is car type drive. It is characterized by a pair of driving wheels and a separate pair of steering wheels. The translation and rotation are independent of each other. The first two of the four wheels can be used to steer and the next two to drive the robot.

Fig. 6 Boat robot based on 2 electrical motor driven propeller In Figure 6, the small autonomous surface vehicle is powered by two electrically driven propellers. The advantage of using such structure is that having two motors, it would provide more thrust and also it wouldn't need extra mechanical structure to turn. When one wants to turn to one side, such as turn to the right side, the right motor shuts down and the thrust of the left engine turns the boat right. Otherwise, turn to the left side, the left motor shuts down and the thrust of the right engine turns the boat left.

Fig. 7 Hardware structure of the proposed controlling system Fig. 4 Mobile robot based on four-wheel driven

Fig. 5 Six leg-wheel robot based on 2 side driven

Figure 7 shows the hardware structure of the proposed controlling system. The system consists of DC motors, IR sensors and Microcontroller ARM CortexM3. Using array of infrared (IR) sensors for measuring the distance of obstacles and locating the target i.e., front obstacle distance (FD), left obstacle distance (LD), right obstacle distance (RD) and detecting the bearing of target (HA). The status of IR sensor helps path planning and changing the direction of the vehicle. The developed Neuro-Fuzzy logic controller, embedded in microcontroller, controls the speeds of DC motors connected with rear direction of the agricultural vehicle to achieve the desired steer angle. Data transmission between the agricultural vehicle and the remote server computer is achieved by means of wireless communication tool. Online computer controlled steering system for the vehicle is described and a Neuro-Fuzzy controller is designed and trained to achieve steering control based on obstacle and boundary information of the agricultural environment.

3. METHODOLOGY FOR THE INTELLIGENCE NAVIGATION SYSTEM 3.1 Robot Controller Architecture The robots used here are imagined to be a rear wheel drive having two rear wheels, namely left and right rear wheel. Each robot has an array of sensors for measuring the distances around it and locating the target i.e., front obstacle distance (FD), left obstacle distance (LD), right obstacle distance (RD) and detecting the bearing of target (HA). The distance between the robots and obstacles act as repulsive forces for avoiding the obstacles, and the bearing of the target acts as an attractive force between robots and target. Neuro-Fuzzy Controller for Mobile Robot Navigation shown on Figure 8. Some of the fuzzy control rules are activated according to the information acquired by the robots using their sensors. In this research three types of membership functions are considered. First one is of three-membership function having trapezoidal members or triangular member or guassian member.

denoted as leftvelo (LV) and rightvelo (RV) respectively (Table 1). Similarly leftdist rightdist, and frontdist are defined for the distances left obstacle distance (LD), right obstacle distance (RD) and front obstacle distance (FD) respectively. Linguistic variables such as ‘‘pos’’ (positive) ‘‘zero’’ and ‘‘neg’’ (negative) are defined for the bearing of heading angle (HA) with respect to target. The term ‘‘no target consider’’ is used if there is no target in the environment. Linguistic variables like ‘‘fast’’; ‘‘medium’’ and ‘‘slow’’ are defined for left wheel velocity and right wheel velocity for three-membership function. Terms like ‘‘very slow’’, ‘‘slow’’, ‘‘medium’’, ‘‘fast’’, and ‘‘very fast’’ are considered for left wheel velocity and right wheel velocity for five-membership functions. Similarly linguistic variables such as ‘‘more pos’’ (more positive),‘‘pos’’ (positive) ‘‘zero’’, ‘‘neg’’ (negative) and ‘‘more neg’’ (more negative) are defined for the bearing of functions described above are shown in Figure. 10. VerySlow 1

Slow

Medium

Fast

VeryFast

Degree of membership

0.8

0.6

0.4

0.2

0 0

Fig. 8 Neuro-Fuzzy Controller for Mobile Robot Navigation In Input of Neuro-Fuzzy Controller, Linguistic variables such as ‘‘far’’, ‘‘medium’’ and ‘‘near’’ are taken for threemembership function. Five-membership function is considered with all of member. Here linguistic variables like ‘‘very near’’, ‘‘near’’, ‘‘medium’’, ‘‘far’’ and ‘‘very far’’ are considered. As shown on Figure 9., Gaussian membership function is considered with ‘‘very near’’, ‘‘near’’, ‘‘medium’’, ‘‘far’’ and ‘‘very far’’ as linguistic variables for navigation of several mobile robots. VeryNear 1

Near

Medium

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VeryFar


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Fig. 9 5-guassian Membership function of left rear distance are “VeryNear” “Near” “Madium” “Far”and VeryFar” The outputs of the activated rules are weighted by fuzzy reasoning and the velocities of the driving wheels of the robots are calculated. Left wheel velocity and right wheel velocity are

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Fig. 10 5 guassian Membership function of left wheel velocity are “VerySlow” “Slow” “Madium” “Fast” and VeryFast” Table 1. Obstacle avoidance for three-membership function Fuzzy Input Variable Output Variable Rlue No. LeftDist FrontDist RightDist LeftBackDist RightBackDist Heading LeftVelo RightVelo 1 2 3 4 5 6 7 8 9 10 11 .. ..

Near Near Near Near Near Near Near Near Near Med. .. .. ..

Near Near Near Med. Med. Med. Far Far Far Near .. .. ..

Near Med. Far Near Med. Far Near Med. Far Near .. .. ..

Far Far Far Far Far Far Far Far Far Far .. .. ..

Far Far Far Far Far Far Far Far Far Far .. .. ..

Zero Pos Pos Pos Pos Pos Pos Pos Pos Pos .. .. ..

Slow Med Fast Slow Fast Fast Slow Fast Fast Med. .. .. ..

Slow Slow Slow Slow Med. Slow Slow Med. Slow Fast .. .. ..

3.2 Neuro-Fuzzy mechanism for mobile robot navigation When the robot is very close to an obstacle, because of repulsive force developed between the robot and the obstacle the robot must change its speed and heading angle to avoid the obstacle. Some of the fuzzy rules used for obstacle avoidance by robots are listed in Tables 1. All the rules in those tables have been obtained heuristically using common sense. Some rules mentioned in Table 1 cater for extreme conditions when the obstacles have to be avoided as quickly as possible. This is for three-membership function. Rule 01,02 and 03 are mentioned in the Equation 6 describes if the left obstacle distance is ‘‘near’’, right obstacle distance is ‘‘far’’, front obstacle distance is ‘‘medium’’ and no target is around the robot, then the robot should turn to right side as soon as possible to avoid collision with the left obstacle. For the above condition the left wheel

velocity should increase fast and right wheel velocity should decrease slowly. Based on the subsets the Neuro-Fuzzy control rules are defined as follows:

VerySlow 1

Slow

Medium

Fast

40 50 60 LeftWheelVelocity

70

VeryFast

∧

R1 :

∧

ℎ

∧

∧

ℎ

∧ ∧

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∧

∧

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0.8

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0 0

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Fig. 12 5 guassian Membership function of left wheel velocity are “VerySlow” “Slow” “Madium” “Fast” and VeryFast”

(7) )

)

(8)

4. RESULTS AND DISCUSSIONS 4.1 Experimental method The proposed Neuro-Fuzzy technique has been implemented in implemented with three robots and different environment. In this work, the robot alternates among the following behaviours: Target reaching, Obstacle avoidance, Exploration, and Wall following, as shown on Figure 11. The fuzzy rules are designed to implement these behaviours. At Frist, the rule base is predesigned to avoid dead end situation. Once the robot has received a command to start searching for the target position, the robot will try to locate the target while avoiding any obstacle along its path. Secondly, the robot is set to explore its environment based on the time set and avoiding any obstacle on its path by training with remote control by operator. After learning, all output variables, such as left wheel, 5 memberships are changed as shown on Figure 12. Thirdly, an exercise has been carried out to compare the performances of the different types fuzzy controllers i.e., comparison between threemembership function, five-membership function, and Gaussian membership function.

1 Fig. 13 5 Example of experimental environment for Six leg-wheel robot based on 2 side driven Table 2. Performance successful avoidance of different technique for navigation of several mobile robot No.

Robot Type

1

Ideal Condition NFC

2

Three-membership NFC

3

Four-wheel driven Three-membership NFC

4

mobile robot

type of membership

% successful avoidance

-

10

trapezoidal

62

triangular

71


guassian

80

5

Five -membership NFC

trapezoidal

72

6


triangular

77

7


guassian

90

1

Ideal Condition NFC

-

15

2


trapezoidal

65

3

Six leg-wheel


triangular

78

4

mobile robot


guassian

85

5


trapezoidal

82

6


triangular

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7


guassian

95

1

Ideal Condition NFC

2


trapezoidal


triangular

52


guassian

62

5


trapezoidal

65

6


triangular

71

7


guassian

85

3 4

Fig.11 Environment robot motion with avoiding obstacles of different shapes and sizes.

Different technique

Boat robot

-

5 45

After experiment 20 times for one condition, a comparison of the performances of different techniques has been carried out and represented in Table 2. In traced by the robot using threemembership function, five-membership function and different type of membership function such as, trapezoidal, triangular,

guassian membership function respectively. Total path lengths using three-membership fuzzy, five-membership fuzzy and Gaussian membership fuzzy controllers are measured. Similarly percent successful avoid Obstruct taken to target using threemembership, five-membership based on serval membership fuzzy controllers are measured for the several of robots using statistical method. The path lengths and percent of successful obstructed avoidance are giving an objective measure of the performance of the different controllers. A comparison of the performances of different techniques has been carried out and represented in Table 2. In Ideal condition, the robot unknowns the environment. But, it shown navigation of several mobile robot to reach target with prior knowledge from expert with minimum percent of successful. In Table 2, apart from the path length and percent successful in Gaussian MF based controller, the completion time shows the suitability of Gaussian MF based controller over Triangular MF or trapezoidal MF controller for mobile robot control. 5. CONCLUSION This paper has described techniques for controlling the navigation of several mobile robots using different Neuro-Fuzzy logic controllers (NFC) in an unknown environment. Fuzzy rules for obstacle avoidance are derived from the experience of human reaction in the unknown environment. These rules are formed according to the different situation exemplified by the obstacle positions, the goal orientation and the direction of movement of the robot. In all cases the robot is able to navigate its way towards the goal while avoiding obstacles successfully. All techniques employ fuzzy rules and take into account the distances of the obstacles around the robots and the bearing of the target in order to compute the velocities of the driving wheels. It has been seen that, by using all the three types of Neuro-Fuzzy logic controller (NFC). The robots were able to avoid any obstacles (static and moving obstacles), escape from dead ends, and find targets in unknown environment. Using Neuro-Fuzzy logic controller (NFC) with Gaussian membership as many as twenty tests on all of mobile robots can navigate successfully neither colliding with each other nor colliding with obstacles present in an unknown environment. Comparisons of the performances among different techniques have been carried out. From the present analysis it is concluded that the Neuro-Fuzzy logic controller utilizing Gaussian membership is best among the three techniques for navigation of multiple mobile robots. The present research has got a tremendous application such as agricultural activity. This technique can again revised by fusing some other technique to neuro-fuzzy technique such as adaptive technique, genetic algorithm and etc. 8. ACKNOWLEDGMENT I would like to thank participants for their helpful comments and invaluable discussions with them. This piece of work was partly under in kind and financial support from Faculty of Engineering and Center of Innovative Mechatronics and Robotics (IMERs) at Department of Mechanical Engineering (Kampangsean), Faculty of Engineering (Kampangsean), Kasetsart University (Kampangsean), Nakhonpatom, Thailand.

9. REFERENCES [1] M. Kassler, “Agricultural Automation in the new Millennium”, Computers and Electronics in Agriculture”, Vol. 30, 2001, pp. 237 – 240. [2] Editorial board, “Agricultural Robotics”, Journal of Field Robotics, Vol. 26, No. 6-7, June – July, 2009, pp. 501-503. [3] A. AbuBaker, “A Novel Mobile Robot Navigation System Using Neuro-Fuzzy Rule-Based Optimization Technique”, Research Journal of Applied Secience, Engineering and Technology, Vol 4, No. 15, 2012, pp. 2577-2583. [4] S.K. Pradhan, D.R. Parhi and A. K. Panda, “ Fuzzy logic techniques for navigation of serval mobile robots”, Applied Soft Computing, Vol. 9, 2009, pp. 290-304. [5] M. Toda, O. Kitani and T. Okamoto, “Navigation method for a mobile robot via sonar-based crop row mapping and fuzzy logic control”, Journal of agricultural Engineering Research, Vol. 72, No. 4, 1999, pp. 299-309. [6] M.B. Montaner and R. Serrano, “Fuzzy knowledge-based controller design for autonomous robot navigation (robotics and computer vision)”, Expert Systems with applications, Vol. 14, No. 1-2, 1998, pp. 179-186. [7] T.L. Lee, C.J. Wu, “Fuzzy motion planning of mobile robots in unknown environments”, Journal of Intelligent and Robotic Systems, Vol. 37, No. 2, 2003, pp. 177–191. [8] T. Takagi, and M. Sugeno, “Fuzzy identification of systems and its application to modeling and control”, IEEE Transactions on. SMC, Vol. 5, 1985, pp. 116-132. [9] Babuska, A. R., “Neuro-fuzzy methods for modeling and identification”, Recent Advances in intelligent Paradigms and Application, 2002, pp. 161–186.