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Outline Abstract Introduction Motion Fuzzy Controller Structure GA-PSO Fuzzy Controller Design Method Simulation Results Conclusions
Citation preview
Application of the GA-PSO with the Fuzzy controller to the robot
soccer
Department of Electrical Engineering, Southern Taiwan University,
Tainan, R.O.C Juing-Shian Chiou, Ming-Yuan Shieh Shih-Wen Cheng,
Kuo-Yang Wang, Yu-Chia Hu Outline Abstract Introduction Motion
Fuzzy Controller Structure
GA-PSO Fuzzy Controller Design Method Simulation Results
Conclusions Abstract In this paper we proposed the method of GA-PSO
to adjust the rule of the fuzzy system with robot soccer. The
experimental scenarios involved a five-versus-five soccer
simulation and the MATLAB simulation. Introduction(1/2) We use the
robot system as our test platform since it can be used to fully
implement a multi-agent system. In this paper, we choose
five-versus-five simulation platform. (Figure1) Figure 1. The
Five-versus-Five simulation platform Introduction(2/2) To achieve
an optimal design for a soccer robot, we use Genetic algorithms-
particle swarm optimization (GA-PSO) to adjustment fuzzy membership
function thus reaches the optimal result. Motion Fuzzy Controller
Structure(1/7)
In this part, we start design the fuzzy logic controller aimed at
producing the velocities of the robot right and left wheel. We set
two input parameters of the fuzzy logic controller are distance and
angle . The former is the distance between the robot and the goal.
The latteris the direction of with on the straight line path to the
goal. Both are shown in Figure 2. Figure 2. the relation ofand
Motion Fuzzy Controller Structure(2/7)
We set the values of variable , ,,,,,, and design two fuzzy
controllers to control the velocity of the right and left wheels to
move the robot. The fuzzy rules on which were based these fuzzy
controllers are described in tables 1 and 2, and can be described
according to the following equations: Then (1) (2) Motion Fuzzy
Controller Structure(3/7)
Table 1. Fuzzy rule base of the left- wheel velocity fuzzy
controller Table 2. Fuzzy rule base of the right- wheel velocity
controller Motion Fuzzy Controller Structure(4/7)
The following term sets were used to describe the fuzzy sets of
each input and output fuzzy variables: (3) (4) Motion Fuzzy
Controller Structure(5/7)
As show in figure 3, the triangle membership function and the
singleton membership function are used to describe the fuzzy sets
of input variables and output variables. (a) (b) Figure 3.
Membership function:(a) the fuzzy sets for; (b) the fuzzy sets for
Motion Fuzzy Controller Structure(6/7)
Based on the weighted average method, the final output of these
fuzzy controllers can be described by means of equation (5) and (6)
Where and were determined according to Equations (7) and (8). (5)
(6) (7) (8) Motion Fuzzy Controller Structure(7/7)
When the input data of andare given,and can be determined by using
Equations (5) and (6) Thus, the left-wheel velocity and the
right-wheel velocity can be obtained. GA-PSO Fuzzy Controller
Design Method(1/11)
The procedure of GA-PSO algorithm can be described as follows: Step
1: Initialize the PSO algorithm by setting , the maximum number of
generation (G), the number of particles (N), and four parameter
values of and GA-PSO Fuzzy Controller Design Method(2/11)
Step 2: Generate the initial position vector and the initial
velocity vector of N particles randomly by (12) and (13) GA-PSO
Fuzzy Controller Design Method(3/11)
Step 3: Calculate the fitness value of each particle in the g-th
generation by Step 4: Determine and for each particle by (15) and
(16) GA-PSO Fuzzy Controller Design Method(4/11)
Step 5: Find an index q of the particle with the highest fitness by
(17) and determine and by (18) and (19) GA-PSO Fuzzy Controller
Design Method(5/11)
Step 6: If g=G, then go to Step 12, Otherwise, go to Step 7. Step
7: Update the velocity vector of each particle by (20) is a weight
value and defined by (21) GA-PSO Fuzzy Controller Design
Method(6/11)
Step 8: With fixed-length chromosomes that the problem is variable
domains, select the number of chromosome population is , the
crossover rate is , the mutation rate is Step 9: The definition of
adaptive function to measure the problem domain on a single
chromosome of the performance or adaptability. Adaptive function is
built on the reproductive process, the basis for selecting pairs of
chromosomes. Step 10: The size of a randomly generated initial
population of chromosomes GA-PSO Fuzzy Controller Design
Method(7/11)
Step 11: Calculating the adaptability of each chromosomes. Step 12:
In the current population, select a pair of chromosomes Parental
chromosomes are selected and their adaptability related to the
rate. Adaptive chromosomes are selected with high rate is higher
than the low adaptability of the chromosomes. Step 13: Through the
implementation of genetic operators-crossover and mutation of a
pair of offspring chromosomes. GA-PSO Fuzzy Controller Design
Method(8/11)
Step 14: The offspring chromosomes into new populations. Step 15:
Repeat step 13, unit the new chromosome population size is equal to
the size of initial population Step 16: With the new (offspring)
chromosome populations to replace to the initial (parent)
chromosome populations. Step 17: Back to step 12, repeat this
process until you meet the conditions for ending to stop. GA-PSO
Fuzzy Controller Design Method(9/11)
Step 18: Check the velocity constraint by (22) Step 19: Update the
position vector of each particle by (23) GA-PSO Fuzzy Controller
Design Method(10/11)
Step 20: Bound the updated position vector of each particle in the
searching range by (24) Step 21: Let g=g+1 and go to Step 3. GA-PSO
Fuzzy Controller Design Method(11/11)
Step 22: Determine the corresponding fuzzy controller based on the
position of the particle with the best fitness value In the above
reasoning, we will use the FIRA simulator to confirm the results of
our reasoning. Simulation Results(1/2)
The membership functions of and, as determined by the proposed
GA-PSO based method, are presented in figure 4. (a)(b)(c) Figure 4.
membership functions of (a) (b) (c) , as determined by the proposed
GA-PSO method Simulation Results(2/2)
The figure 5 are the orbit of the soccer robot when controller by
the proposed GA-PSO method and simulation with a FIRA simulation.
Figure5. The soccer robot moving Conclusions The final results
showed that, although the GA-PSO's convergence time is not the time
than the PSO-based fast, and as we join the GA algorithm , after
the results obtained will be closer to its optimal solution. In the
future, we need to explore ways to let a faster convergence time
for change Thanks for your attention !