28-08-2014, 02:31 PM
Subway Train Braking System: A Fuzzy Based Hardware Approach
[attachment=67244]
Abstract:
Problem statement: Automated subway train-braking system require perfection, efficiency
and fast response. In order to cope with this concerns, an appropriate algorithm need to be developed
which need to be implemented in hardware for faster response. Approach: In this research, the FPGA
realization of fuzzy based subway train braking system has been presented on an Alter FLEX10K
device to provide an accurate and increased speed of convergence of the network. The fuzzy based
subway train braking system is comprised of fusilier, inference, rule selector and defuzzifier modules.
Sixteen rules are identified for the rule selector module. After determining the membership functions
and its fuzzy variables, the Max-Min Composition method and Madman-Min implication operator are
used for the inference module and the Centre of Gravity method is used for the defuzzification module.
Each module is modeled individually using behavioral VHDL. The layers are then connected using
structural VHDL. Two 8-bit and one 8-bit unsigned digital signals are used for input and output
respectively. Six ROMs are defined in order to decrease the chances of processing and increasing the
throughput of the system. Results: Functional simulations were commenced to verify the functionality
of the individual modules and the system as well. We have validated the hardware implementation of
the proposed approach through comparison, verification and analysis. The design has utilized 2372
units of LC with a system frequency of 139.8MHz. Conclusion: In this research, the FPGA realization
of fuzzy brake system of subway train has been successfully implemented with minimum usage of
logic cells. The validation study with C model shows that the hardware model is appropriate and the
hardware approach shows faster and accurate response with full automatic control.
INTRODUCTION
Fuzzy techniques are becoming an attractive
approach to handle uncertain, imprecise, or unmodeled
data in solving control and intelligent decision-making
problems (Zadeh, 1972; Al-Odienat, 2008). This is
mainly due to their inherent ability to describe a
complex system by means of a simple set of intuitive
and ambiguous behavioral rules. The application of
fuzzy technologies into real-time control problems
demands the development of efficient hardware
implementations of fuzzy inference mechanisms.
In this paper, fuzzy logic utilities for synthesis of
control system are discussed in the context of an
application to the subway train braking system. A fuzzy
based subway train braking system has the advantage
that it allows the intuitive nature of accurate prediction
of brake pressure to be easily modeled using the
number of linguistic terms, the respective membership
functions for each linguistic variable, the inference
mechanism, the rules, the fuzzification and
defuzzification methods (Victor and Dourado, 1997;
Zachary, 2010). Due to the relative computational
simplicity of fuzzy rule-based system, intelligent
decision can be made in real-time, thus allowing an
accurate prediction of brake pressure.
MATERIALS AND METHODS
FPGA realization of subway train braking system using
fuzzy technique is introduced to implement a dedicated
hardware for faster and accurate response with full
automatic control. The subway train braking system,
which determines the amount of brake pressure with
given two inputs: distance and speed. The fuzzy based
subway train braking system is comprised of four major
blocks: the fuzzifier, the rule selector, the inference and
the defuzzifier. Three membership functions ‘distance’
and ‘speed’ as input and ‘brake’ as output are chosen.
For ‘distance’, the range of 500m with fuzzy variables
of Very Close, Close, Far and Very Far are chosen.
RESULTS
Simulation: In order to evaluate the performance of the
VHDL model of subway train braking system, a set of
input values of distance and speed are fed into the
model. Singleton output values from the fuzzifier and
the rule selector are taken to analyze the degree of
fulfillment and the brake values are taken for
verification of the overall output. The input values are
converted into its binary form before being entered into
the test-bench of the VHDL model. The generated
simulated waveform is shown in Fig. 2 and the
converted binary values of hexadecimal output are
tabulated in Table 3.
The same input, which is a real value, is fed into a
C model in order to compare the result. The C model
was set as benchmark against the VHDL model. The
corresponding output is shown in Table 4.
DISCUSSION
To verify the efficiency and perfectness, we have
also developed the C model of our proposed design.
In the comparative analysis, the output results of the C
model are compared with the output of VHDL
model. The percentage of error was calculated as
shown in equation (1)
CONCLUSION
The FPGA realization of fuzzy based subway train
braking system has been proposed and its potentialities,
as indicated by the good prediction of brake values,
have been presented. By simulating and synthesizing
with the values of distance and speed, the proposed
approach has been successfully designed, implemented
and tested. It is found that the error is almost negligible
when the VHDL simulation result has been compared
with the results from C model, which shows that
hardware realization train braking system is
appropriate. Moreover, hardware solution is faster and
robust than software solution.
[attachment=67244]
Abstract:
Problem statement: Automated subway train-braking system require perfection, efficiency
and fast response. In order to cope with this concerns, an appropriate algorithm need to be developed
which need to be implemented in hardware for faster response. Approach: In this research, the FPGA
realization of fuzzy based subway train braking system has been presented on an Alter FLEX10K
device to provide an accurate and increased speed of convergence of the network. The fuzzy based
subway train braking system is comprised of fusilier, inference, rule selector and defuzzifier modules.
Sixteen rules are identified for the rule selector module. After determining the membership functions
and its fuzzy variables, the Max-Min Composition method and Madman-Min implication operator are
used for the inference module and the Centre of Gravity method is used for the defuzzification module.
Each module is modeled individually using behavioral VHDL. The layers are then connected using
structural VHDL. Two 8-bit and one 8-bit unsigned digital signals are used for input and output
respectively. Six ROMs are defined in order to decrease the chances of processing and increasing the
throughput of the system. Results: Functional simulations were commenced to verify the functionality
of the individual modules and the system as well. We have validated the hardware implementation of
the proposed approach through comparison, verification and analysis. The design has utilized 2372
units of LC with a system frequency of 139.8MHz. Conclusion: In this research, the FPGA realization
of fuzzy brake system of subway train has been successfully implemented with minimum usage of
logic cells. The validation study with C model shows that the hardware model is appropriate and the
hardware approach shows faster and accurate response with full automatic control.
INTRODUCTION
Fuzzy techniques are becoming an attractive
approach to handle uncertain, imprecise, or unmodeled
data in solving control and intelligent decision-making
problems (Zadeh, 1972; Al-Odienat, 2008). This is
mainly due to their inherent ability to describe a
complex system by means of a simple set of intuitive
and ambiguous behavioral rules. The application of
fuzzy technologies into real-time control problems
demands the development of efficient hardware
implementations of fuzzy inference mechanisms.
In this paper, fuzzy logic utilities for synthesis of
control system are discussed in the context of an
application to the subway train braking system. A fuzzy
based subway train braking system has the advantage
that it allows the intuitive nature of accurate prediction
of brake pressure to be easily modeled using the
number of linguistic terms, the respective membership
functions for each linguistic variable, the inference
mechanism, the rules, the fuzzification and
defuzzification methods (Victor and Dourado, 1997;
Zachary, 2010). Due to the relative computational
simplicity of fuzzy rule-based system, intelligent
decision can be made in real-time, thus allowing an
accurate prediction of brake pressure.
MATERIALS AND METHODS
FPGA realization of subway train braking system using
fuzzy technique is introduced to implement a dedicated
hardware for faster and accurate response with full
automatic control. The subway train braking system,
which determines the amount of brake pressure with
given two inputs: distance and speed. The fuzzy based
subway train braking system is comprised of four major
blocks: the fuzzifier, the rule selector, the inference and
the defuzzifier. Three membership functions ‘distance’
and ‘speed’ as input and ‘brake’ as output are chosen.
For ‘distance’, the range of 500m with fuzzy variables
of Very Close, Close, Far and Very Far are chosen.
RESULTS
Simulation: In order to evaluate the performance of the
VHDL model of subway train braking system, a set of
input values of distance and speed are fed into the
model. Singleton output values from the fuzzifier and
the rule selector are taken to analyze the degree of
fulfillment and the brake values are taken for
verification of the overall output. The input values are
converted into its binary form before being entered into
the test-bench of the VHDL model. The generated
simulated waveform is shown in Fig. 2 and the
converted binary values of hexadecimal output are
tabulated in Table 3.
The same input, which is a real value, is fed into a
C model in order to compare the result. The C model
was set as benchmark against the VHDL model. The
corresponding output is shown in Table 4.
DISCUSSION
To verify the efficiency and perfectness, we have
also developed the C model of our proposed design.
In the comparative analysis, the output results of the C
model are compared with the output of VHDL
model. The percentage of error was calculated as
shown in equation (1)
CONCLUSION
The FPGA realization of fuzzy based subway train
braking system has been proposed and its potentialities,
as indicated by the good prediction of brake values,
have been presented. By simulating and synthesizing
with the values of distance and speed, the proposed
approach has been successfully designed, implemented
and tested. It is found that the error is almost negligible
when the VHDL simulation result has been compared
with the results from C model, which shows that
hardware realization train braking system is
appropriate. Moreover, hardware solution is faster and
robust than software solution.