FPGA implementation of fuzzy logic elevator group controller with ...

2010 5th International Conference on Industrial and Information Systems, ICIIS 2010, Jul 29 - Aug 01, 2010, India

FPGA Implementation of Fuzzy Logic Elevator Group Controller With Traffic Based System Rajesh Kumar Patjoshi

Kamala Kanta Mohapatra

Electronics and Communication Engineering, National Institute of Technology, Rourkela, India Email : [email protected]

Electronics and Communication Engineering, National Institute of Technology, Rourkela, India Email : [email protected]

Abstract - This paper present an implementation of fuzzy logic controller (FLC) on a field programmable gate array (FPGA) system for managing the traffic of two or more elevator group control system .The proposed design responds to a new hall call by determining the most suitable car based on an evaluation function computed for each car. The car with the lowest value of evaluation function is selected to serve the hall call. This proposed approach is based on an algorithm that consists of two parameters, first parameter is the estimated arrival time to the floor where the hall call occurs, second parameter is the floor priority cofficients (Ki). Simulation is carried out by considering three inputs i.e. average waiting time (AWT), power consumption (PC), and floor traffic (FT). During the hours of low passenger traffic, high fuzzy control values resulted in the cars to be positioned at the floors to which high priority values are assigned. When the passenger traffic is high, the low fuzzy control values reduce the importance of priority of floors, rather the estimated arrival time is to be minimized.

possible assignments for M cars is M -N! – (2N)!. A total of all possible states M-N!- (2N!) –C (N,M) has to be considered. If P hall calls come, the controller has to consider MP cases to make a decision for the most suitable car. All these cases cannot be evaluated within a limited time for decision making. In addition there are several uncertainties such as the time of hall call, the number of people who make the calls, the destination of the passengers who make the hall call, the number, physical conditions and age of passengers, etc. With the excessive number of states he uncertainty factors, it is not possible to achieve an optimal decision [2]. To overcome all these difficulties, fuzzy approach have been extensively used for hall call assignments in elevator groups. Numerous studies have already been published since mid 1990s [3]- [6]. The method described in [6] was based on the area and weight method for describing the hall call assignment. The main problem with this method is that, the floor priority for the heavy traffic floor is not assigned. To overcome this method, we introduce a floor priority coefficient (Ki) in the evaluation function for determining the most suitable car whenever a new hall call comes from the heavy traffic and low traffic floor. We have used the mamdani type fuzzy inference techniques .The system is further implemented in the FPGA for making the control circuit simpler and that would also provide flexibility for an IC fabrication if desired. This new approach allows the system to give high priorities to the most important floors (heavy traffic floor). The floor priority coefficient (Ki) takes a value between 0 and 1 for each floor. For the floors that have low priority a small value is assigned. For the floors with higher priority, a high value is assigned. This FPGA based fuzzy approach is aimed at controlling the elevator group during low and high passenger traffic hours. During busy traffic hours, the controller focuses on achieving a high system performance by assigning the cars to maintain the average waiting time, power consumption and floor traffic at a certain value. II. EVALUATION FUNCTION FOR ELEVATOR GROUP CONTROLLER The most important task of an elevator group control system is selecting a suitable elevator based on the traffic contition.The selection is made in order to minimize the average waiting time of passengers,power consumption and the floor priority coefficient (Ki) .The evaluation function is

Keywords – FEGCS, Floor priority, Average Waiting Time (AWT), Power Consumption (PC), Floor Traffic (FT).

I. INTRODUCTION The Elevator Group Control System(EGCS) is a control system that manages systematically three or more elevators in a group to improve the services for passengers and reduces the cost such as power consumption. Most of the elevators group control system have used to hall call assignment method which assigsns elevator in response to a passenger’s call. This system systematically manages one or more elevators in high-rise buildings to efficiently transport passengers from one floor to another [1]. The fundamental operational principle of an EGCS is to assign the most suitable car to the floor, where a new call comes from, with the highest possible performance. The overall performance of an EGCS is measured by several criteria such as low average waiting time of passengers, low power consumption of the of the elevator system, the smallest number of passengers waiting at each floor. Unfortunately, it is difficult to satisfy all of these criteria at the same time. This because as the number of floors (N) and the number of elevator groups (M) increase, the system complexity increases dramatically. For instance, if there are two call buttons (up and down) at each floor, the number of

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2010 5th International Conference on Industrial and Information Systems, ICIIS 2010, Jul 29 - Aug 01, 2010, India

used to select the most suitable car for the hall call at any time under changing conditions. To consider the effect of uncertainty factors in deciding the most suitable car, we define the evaluation function as summation of two terms: φi = TAR,i + KiT (1)

In the implementation of this algorithm, the times of hall calls are stored and systematically updated every operation period (Top). In simulations, this period is selected as 5 minutes (300 seconds). If the number of hall calls within the last operation period is H, then the power consumption is calculated from PC =

Where TAVR,i is the estimated arrival time of the car to the floor where the call comes from. When calculating the arrival time of the cabin to the floor of call, the time of entering the people, the acceleration time, and breaking time, the time required to change the floors and the distance to the destination have to be known. The second term consists of the product of two values. The coefficient Ki is the floor priority coefficient of the with floor. The variable T is obtained as a result of fuzzy logic algorithm described in the following subsections.

AWT PC FT

Crisp input

Fuzzify inference Mandani

FPGA Control Outputr

MD

LR

VL

Fig. 2 The linguistic terms and the membership functions of the futures used for performance criteria. Power Consumption (PC), Average Waiting Time (AWT) & Floor Traffic (FT)

Where PCi is the power consumption for the ith hall call and Pmax is the maximum power consumption of the elevator group by an assumption that all elevators are operating continuously during the operation period. The power consumption feature is represented by 5 linguistic terms including VS (very small), SM (small), MD (medium), LR (large), and VL (very large). These linguistic terms and their membership functions are shown in Fig. 2. The average waiting time is calculated from 1 H (3) AWT = ∑ TAVR,i H i =1

T, T2

Defuzzification Module

SM

Universe of Discourse

Tn Fuzzification Module

(2)

Pmax

VS

III. FUZZY ELEVATOR GROUP CONTROLLER A conventional FLC is designed on a simple concept .It receives an input,performs some sort of processing and then generates an output. Basically the FLC is divided into four components. Figure-1 shows the diagram of the basic Structure of Proposed FPGA based fuzzy logic elevator group controller with traffic based system. It consists of Fuzzification Module, Rule Base, Fuzzy Interface Engine, Defuzzification Module. Rule Base

∑ iH=1 PCi

Elevator group Control System with traffic based system

Where the TAVRi is calculated by the following formula :

Crisp output

TAVRi =

Fig. 1. Basic Structure of the proposed Fuzzy Elevator Group Controller with traffic based System

∑ Tstop (i ) + ∑ Tdrive( k )

stop

(4)

drive

Tstop (i ) = Tspeed_down + T get_on/off(i) + Tspeed_up

(5)

In the above formula, we divide the path of the elevator into stop and drive. Stop means where hall calls and car calls are assigned, drive means floor where no calls near the floors.

A. Fuzzification In evaluating the performance of the elevator group control system, different criteria can be considered. In this paper, the following three features of the elevator group system are used

The linguistic terms and their membership functions are shown in Fig. 2. Finally for the floor traffic is calculated from H FT = i (6) H

1) Power Consumption (PC) is the percentage ratio of the power consumed by the elevators in service during the operation period of Top, to the maximum possible power consumed by all elevators during that period. 2) Average Waiting Time (AWT) is the average of all times pass until an assigned car arrives to the service floor where a passenger presses a hall call button. 3) Floor Traffic (FT) is the percentage of hall calls from a floor during an operation period of Top to the total number of hall calls occurred during that period.

Where Hi is the number of hall calls occur at the ith floor during Top. The linguistic terms and their membership functions of this feature are shown in Fig. 2.

B. Rule based and Inference The fuzzy control variable T has a major effect on the evaluation functions for each elevator car. Whenever a new hall call is requested by the passengers, a new value of the

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2010 5th International Conference on Industrial and Information Systems, ICIIS 2010, Jul 29 - Aug 01, 2010, India

fuzzy value T is determined as an output from a given of linguistic variables describing the state of the elevator traffic. We implemented the fuzzy inference system using three linguistic variables including PC, AWT, and FT as a logical input to the logical operations defined by a set of “if-then” rules. Mamdani's fuzzy inference method [8] has been used to determine the logical fuzzy output. For a three input, singleoutput fuzzy inference system considered here includes five membership functions for each input variable. Therefore, it is possible to define 125 rules to describe the all possible states of the elevator traffic under consideration. When forming the table of rules, the experience of the system manager and possible traffic conditions of the elevator group system play an important role to make a good decision [9]. For example, when the power consumption is very low, the average waiting time is very short and the floor traffic is very low, a high value T should be obtained to emphasize the importance of the floor priority coefficient. The rule for such a case can be constructed by the following statement:

The function of the defuzzificaition is to convert the fuzzy output value of the control system into the corresponding crisp value of the membership function shown in fig.3. This is achieved using the weighted average defuzzification method. This defuzzification operation requires several multipliers and a divider. Behavioural modelling in VHDL supports multiplication and division but these operations are complicated to realise in the systhesis and implementation stages.As most synthesis tools do not support the division operator ,modification is required at subsequent stages.A binary division algorithm is implemented after the defuzzification is the main modification in the process. IV. SIMULATION RESULT AND FPGA IMPLEMENTATION A. Simulation Details The fuzzy elevator group control algorithm developed in this study is tested for various operation conditions by simulations. The major objective of these test cases is to show the effectiveness of the fuzzy output value T when determining the most suitable car for the given traffic conditions and the floor priority coefficients. For all test case, we considered a 10 floor building with a group of three elevators. When computing the estimated arrival time TAVR the following assumptions are made.

if (PC is VS) and (AWT is VS) and (FT is VS), then (T is VL). In contrast to the previous case, the fuzzy output value has to be reduced when the power consumption is very high, the average waiting time is very long and the floor traffic is very high, the rule is constructed by:

10 9 8 7 6 5 4 3 2 1

if (PC is VL) and (AWT is VL) and (FT is VL), then (T is VS). Some other rules are :1) if (PC is VS) and (AWT is LR) and (FT is VL), then (T is VS). 2) if (PC is VS) and (AWT is MD) and (FT is LR), then (T is SM).

2) The time required to travel between two floors takes 15 s.

Parameter for heavy traffic

C. Defuzzification

1) AWT = 60 Sec 2) PC = 100% 3) FT = 100%

SM MD LR VR

Single ton Membership Function

Based on the above data the fuzzy controller can calculate the fuzzy control value T=1, that is shown in simulation result of fig. 5. The operating conditions for the test case during a very traffic condition is depicted in Fig. 4. When the hall call comes from the 7th floor in the upward direction (indicated by Δ), the car located at the 2nd, 3rd and 4th floors respectively.The rightmost column in this figure indicates the direction of the hall call and the floor priority

Universe of Discourse 5

Hall Call Car-3 Priority Coef. 10 9 8 7 Δ 6 5 4 1.0 3 0.1 2 0.0 1

1) The time required to start and stop the car takes 12s.

4) if (PC is MD) and (AWT is MD) and (FT is MD), then (T is MD).

0

Car-2 10 9 8 7 6 5 4 3 2 1

Fig. 4 The state of the elevator group in Case-1

3) if (PC is SM) and (AWT is MD) and (FT is LR), then (T is MD).

VS

Car-1 10 9 8 7 6 5 4 3 2 1

10

15

20

Fig. 3 : Output Fuzzy set

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2010 5th International Conference on Industrial and Information Systems, ICIIS 2010, Jul 29 - Aug 01, 2010, India

coefficients. The estimated reaching times for each car are shown in Table 1. The fuzzy value for this case is obtained as T=1 as shown in the simulation result of fig. 5,resulting the following value for the evaluation functions are shown in Table 1.Based on the caculated value of evaluation function φ3, Car-3is selected as the most suitable car for this hall call, although the floor where Car-3 is located at has the highest priority coefficient. Since the elevator traffic is very busy, the floor priority looses its significance. Instead, the Car-3 is selected as the most suitable car to increase system performance since it is the closest one to the 7th floor.

Fig. 6

10 9 8 7 6 5 4 3 2 1

TABLE –I VALUE OF ESTIMATED REACHING TIME AND EVALUATION FUNCTION FOR FIRST CASE

Heavy traffic Low traffic

TAVR,1 18s

TAVR,2 16s

TAVR,3 15s

φ1 18s

φ2 16.15

φ3 16

T 1

18s

16s

15s

17.5

17

21

7

Parameter for low traffic are –

Simulation result for fuzzy control variable T for first case (Lower Traffic)

Car-1 10 9 8 7 6 5 4 3 2 1

Car-2 10 9 8 7 6 5 4 3 2 1

Hall Call Car-3 Priority Coef. 10 9 8 Δ 7 6 5 4 0.5 3 2 0.2 1 0.1

Fig. 7 The state of the elevator group in Case-2

1) AWT = 10 s 2) PC = 20% and 3) FT = 20% Based on the above data the fuzzy controller can calculate the fuzzy control value T = 7. This is shown in the Simulation result of the fig. 6. From the Simulation result, it is observed that the fuzzy output will increases to T = 7, the resulting evaluation function value which are shown in Table-1. From these values, Car-2 is the most suitable car. Since Car-3 is located at a floor with the highest priority coefficient, it is not selected for this hall call. Car-2 is closer to the hall call, being the best selection for system performance.

Based on the above data the fuzzy controller can calculate the fuzzy control value T = 16 which is shown in the Simulation Result of Fig. 8. The estimated reaching time of each car are shown in Table-II. The fuzzy value for this case is obtained as T=16 from the simulation result, resulting the following value of evaluation function are calculated which are shown in Table II. based on the calculated value, the Car-1 is selected as the most suitable car.

Another interesting case can be analyzed when a hall call occurs at the 8th floor to the upward direction given in Fig. 7.

Fig. 8 Simulation result for fuzzy control variable T for second case (Low Traffic)

For heavy traffic, the parameters the condition are : Fig. 5 Simulation result for fuzzy control variable T for first case (Heavy Traffic)

1) AWT = 10s 2) PC = 15 % 3) FT = 15%

The parameters in this case are For the lower traffic condition : 1) AWT = 10s 2) PC = 5 % 3) FT = 5%

Based on the above data fuzzy controller can calculate the fuzzy control value T = 12 this is shown in the simulation result of fig. 9.

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2010 5th International Conference on Industrial and Information Systems, ICIIS 2010, Jul 29 - Aug 01, 2010, India

process provide two outputs i.e. Div.A and Div. B. Based on this output, the binary Division can produce the fuzzy value T.

B. FPGA Implementation : The design of fuzzy elevator group controller with floor priority system is implemented using VHDL. Synthesis process has performed using Xilinx tools [11]. Each elements of the FLC is designed and carefully optimised for synthesis. Four VHDL components FUZZIFY, INFER, DEFUZZ AND DIVIDER make up the core of the fuzzy controller. The nature of the components’ connection and the functionality of the processes are described by an upper hierarchy VHDL code Controler.vhd. These components are wired to each other to form the complete control system. These VHDL codes are synthesized for converting into RTL view of the FLC architecture as shown in figure 11. The Technology mapping has chosen in this project from Spartan 3E (xc3s500E) with FG320 package and a speed grade of -4. The synthesized schematic is also simulated to ensure the synthesized design functions. Table-III shows the device utilisation summary of the FPGA based fuzzy logic controller for inteligent control of elevator group system.

Fig. 9 Simulation result for fuzzy control variable T for second case (Heavy Traffic)

The estimated reaching time of each car are shown in Table - II. The fuzzy value for this case is obtained as T = 12 from the simulation result, resulting the following value of evaluation function are calculated which are shown in Table II. based on the calculated value, the car 3 is selected as the most suitable car.

TABLE - III DEVICE UTILISATION SUMMARY No. of slices 481 out of 4656 10% No. of slices flip flop 241 out of 9312 2% No. of 4 input LUTS 874 out of 9312 9% Number of IOs 38 No. of Bonds IOBs 38 out of 232 16% No. of GCLK 1 out of 24 4% Timing Summary : Speed Grade: -4 Minimum period: 17.485ns (Maximum Frequency: 57.192MHz) Minimum input arrival time before clock: 16.826ns Maximum output required time after clock: 4.532ns Maximum combinational path delay: No path found

Fig.10 Simulation result and RTL Schematic for defuzzification TABLE – II VALUE OF ESTIMATED REACHING TIME AND EVALUATION FUNCTION FOR SECOND CASE.

Low traffic Heavy traffic

TAVR,1 20s

TAVR,2 19.5s

TAVR,3 16s

φ1 22

φ2 22.5

φ3 23.8

T 16

20s

19.5s

16s

21.5

20

19.8

12

The simulation result and the RTL schematic view of defuzzification process are shown in fig. 10. Defuzzification

Fig. 11 RTL Schematic for FLC Architecture

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2010 5th International Conference on Industrial and Information Systems, ICIIS 2010, Jul 29 - Aug 01, 2010, India

[5]

V. CONCLUSION This paper presents an approach for the implementation of a fuzzy elevator group controller with floor priority system on FPGA using VHDL. The controller for elevator group control system is implemented on a Xilinx Spartan-3E FPGA. The implementation of the fuzzy logic controller is very straight forward by coding each component of the fuzzy inference system in VHDL according to the design specifications. This floor priority algorithm can change accoridng to the traffic condition and gives flexible control to the heavy traffic floor by suitably updating the floor priority coffecient. The performance parameter such as power consumption (PC), Average Wating Time (AWT) and floor traffic (FT) can be controlled to remain in certain limit by suitably updating the floor priority coefficient (Ki) for changing passenger traffic.

[6]

[7]

[8] [9] [10]

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