Modeling and Optimization of Elevator Group Control ... - IEEE Xplore

IEEE International Conference on Computer, Communication and Control (IC4-2015).

Modeling and Optimization of Elevator Group Control System for High Rise Commercial Building Sudeep Mohaney1 Sourabh Mehto2 Manish Shah3 Electrical and Electronics Engineering VITS, Indore Indore, India 1 [email protected] [email protected] 3 [email protected] Abstract-The paper presents a unique and optimal modeling of elevator system to achieve excellence in energy efficient & low cost operation of elevators. The traffic control and time management has been crucial issue in group elevator control. Here an attempt is made to optimize this using soft computing techniques i.e. ANN and Fuzzy logic; a comparison between them for optimal choice. Keywords- Soft Computing Techniques, ANN, Fuzzy Logic, EGCS

I. INTRODUCTION In modern life the time duration of official segment is restricted to 7-8 hours a day and is same at every place. Consequence to this the height of buildings is also increasing rapidly as the cities have limited area for expansion and is possible in vertical direction only. Then travelling time and waiting time in these buildings should also be reduced for elevators. In this paper modeling of elevator is shown which is used to design an energy efficient and fast elevator. A single elevator is not sufficient in high rise building for handling of huge mass so here we proposes a multiple elevator with group system to control large traffic and conjunction free services of building. Receive all hall call this will but without a proper control these elevators will not work efficiently so an EGCS is required to minimize the waiting time of passenger. Here an attempt is made to control and optimize it with soft computing techniques. Among these techniques the proposed work has carried out with ANN and Fuzzy logic II. MODELING OF ELEVATOR Here the modeling for vertical elevator has done for a medium level multistory commercial building for Indian city. To design an efficient elevator required parameters are given belowThese are the forces which will be apply on the elevator ሺ‡™–‘ሻ ൌ  ൈ ƒ

(1)

Mass of the elevator and counterweight are considered while considering weight 75 kg/passenger. So overall mass is ൌ ሺ’ ൈ ͹ͷሻ ൅ ሺ‡ሻ െ ሺ…™ሻ

(2)

Here “Np” is number of passenger, “Me” is mass of elevator and “Mcw” is mass of counterweight.

Now Power is calculated by the equation given (3)

ሺƒ––ሻ ൌ ൈ ˜

Here “v” is maximum velocity of elevator and “P” is the power of the motor. Then speed of elevator will become tangential velocity. Then angular velocity of the drum will be୴

ଷ଴

୰ୢ

஠

ɘ†ሺሻ ൌ ቀ ቁ ൈ ቀ ቁ

(4)

Here “ωd” is angular velocity of drum and “rd” is radius of drum and is selected by the user as per requirement. For a geared system elevator a gear box is required to adjust the speed. Angular velocity of drum is converted using gear system. Gear ratio is defined according to the requirement of the speed of elevator which is kept constant during the calculation. Now speed of the motor isቀ

ɘ† ଷ଴  ቁ ൌ  ൈ ൬ ൰ൈቀ ቁ ஠ ”ƒ†Ȁ• 

(5)

Here “Nm” is speed of motor, and “GR” is gear ratio. Now the load torque on the motor isሺǤ ሻ ൌ ቀ

୊ൈ୰ୢ ୋୖ

ቁ

(6)

To calculate inertia on motor, load and drum. Total mass is required which include elevator, counterweight, passenger and cable. Then to calculate mass of cable the equation is…ƒ„Ž‡ …ƒ„Ž‡ ൌ ൬ ൰ ൈ ሺ…ƒ„Ž‡ሻ ൈ ሺŽ…ƒ„Ž‡ሻ ‰Ȁ

(7)

Here “Mcable” is mass of cable, “Wcable” is weight of cable in Kg/m, “Ncable” is number of cable used and “l cable” is length of cable. Then mass of the drum is† ൌ Ɏ ൈ ɏ ൈ ሺŽ†ሻ  ൈ ሺଶ୭୳୲ െ ଶ୧୬ ሻ

(8)

IEEE International Conference on Computer, Communication and Control (IC4-2015).

Here “Rout” and “Rin” is the radius of the drum, “ρ” is specific gravity and “l d” is the length of cable. Now total mass will be ൌ ሺ‡ሻ ൅ ሺ…™ሻ ൅ ሺ’ሻ ൅ ሺ…ƒ„Ž‡ሻ

(9)

The load inertia is the total mass that produces force tangential to the drum and is equivalent to point mass located at the drum radius rotating about the drum axis. Therefore total inertia will be ሺ‰ଶ ሻ ൌ  ൈ ”†ଶ

(10)

III. ELEVATOR GROUP CONTROL SYSTEM USING SOFT COMPUTING TECHNIQUES As we know in high rise building only one elevator is not sufficient to perform the entire task so the group of elevator will be installing to solve this problem. For every group of elevator a control system is required to control each elevator of the group. Then to make an efficient system fast service to every hall call is required, to perform this task an EGCS system is developed. The above system will decide the elevator will provide service to different hall call. EGCS is implemented using two heuristic methods Fuzzy logic and ANN. A. Implementation using Fuzzy Logic

Here “J” is moment of inertia.

Fuzzy logic implementation required input output variable and their membership function.

Now the inertia of the drum will be: ଵ

†ሺ‰ଶ ሻ ൌ ൈ ሺ†ሻ ൈ ሺଶ୭୳୲ ൅ ଶ୧୬ ሻ ଶ

(11)

Here “Jd” is moment of inertia of drum. Both the inertia will act as load inertia. Then load inertia will be the sum of inertia is in Kgm2. Then load inertia will be Žሺ‰ଶ ሻ ൌ ൅ †

(12)

Here “Jl” is inertia of load. Then motor inertia can also be calculated with the help of gear box and its gear ratio:

ሺ‰ଶ ሻ ൌ ቀ

୎୪ ୋୖమ

ቁ

(13)

Here “Jm” is inertia of motor. Inertia of the motor is used for the calculation of acceleration. ƒሺȀ• ଶ ሻ ൌ ቀ

ୟ୫ୟ୶ ୰ୢ

ቁ ൈ 

(14)

Here “am” is motor acceleration.

x

Input Variable

In this seven input variables are used. There are three input variable for direction of each elevator in a group of three elevators. Fourth input variable is direction of hall call. And last three input variables are of waiting time of three elevators. x

Output Variable

There are three output variable for priority of each elevator. For hall call request FUZZY will decide the priority of each elevator that which elevator among three will provide the service to that hall call. x

Membership Function

Membership functions are defined for each input and output variables. Membership function for each variable is shown in fig.1The range of membership function for direction of each elevator is from -1 to 1. -1 to 0.2 is for down direction and 0.2 to 1 for up direction. This membership function is also for direction of hall call.

This acceleration can be converted into accelerating torque using inertia which calculated as aboveƒ……ሺǤ ሻ ൌ ƒ ൈ 

(15)

Here Tacc is accelerating torque. Now the total torque will be ൌ ƒ…… ൅ 

(16)

Fig-1: Membership function for direction of each Elevator and Hall call

(17)

Fig-2 shows the membership function for waiting time of each elevator. It ranges from 0 to 60. This is categorized in very low, low, medium and high.

The HP rating of motor will beሺ୘ൈ୒୫ሻ

 ൌ ሺଽǤହସସൈ଻ସ଺ሻ

IEEE International Conference on Computer, Communication and Control (IC4-2015).

Fig-2: Membership function for waiting time of each Elevator

Fig-3 shows the membership function for the output variables which will decide the priority of each elevator. It has the range from 0 to 60 and has three category low, medium and high.

Fig-4: Rules

x

Direction

Travelling direction of car is also important as input for ANN. Direction of car is only can be up or down. For preparation of data set “1” is taken as up direction and “-1” is taken as down movement of car. x

Hall Call

Hall call is the position where passenger waits and requires the elevator on this floor. It can be 1 to 15 any floor. Fig-3: Membership function of output variable (Priority)

x

IF-THEN Rules

With the help of input variable if then rules are formed. if the direction of car1 is up, direction of car2 is up, direction of car3 is up, direction of hall call is up, waiting time of car1 is very low, waiting time of car2 is low and waiting time of car3 is medium then priority of all three car will be high medium and low respectively. Fig-4 shows rule window of fuzzy set. B. Implementation using ANN ANN is used for EGCS, for implementation of ANN input parameters and target should already prepared. In this implementation three elevator make the group of elevator. Number of floors are also fixed in this number of floor is taken as 15. For this application input parameters are as followsx

Position

This parameter tells about the current position of the car. Car may be stationary or moving but at the time of hall call the position of car is important. For three elevators three positions are taken as input.

x

Hall Call Direction

When any passenger demands any elevator he also decides the direction which he wants to travel. Up and down notation is same as direction of elevator. “1” is for up, “-1” is for down. x

Last Internal Hall Call

Last internal hall call shows the final destination of travelling passenger. As 2 or 3 passengers travelling in the elevator then there will be 2 or 3 internal hall call. Then last internal hall call is decided according to the passenger who is traveling largest distance. x

Number of stops in between position and hall call

To calculate waiting time of elevator it is important to calculate number of stops. If hall call comes first than last internal hall call then the stops will be calculated in between position and hall call. And if last internal hall call comes first than hall then the stops will be calculated in between position and last internal hall call. For every stop we assume that elevator takes 6 sec to move again. x

Number of floor between position and hall call

Again for calculation of waiting time number of floors is required. For every crossing of floor, elevator speed is

IEEE International Conference on Computer, Communication and Control (IC4-2015).

considered as 2 m/s then it takes 2 sec to cross a floor if height is taken as 4 m of every floor. x

Waiting Time

Waiting time is calculated with the help of number of stops and number of floors. In ANN waiting time is calculated same as in Fuzzy Logic with the help of equation. – ൌ ሺ͸ ൈ ୱ ሻ ൅ ሺʹ ൈ ୤ ሻ

(18)

Here “Ns” is stops of elevator and “Nf” is number of floors elevator is crossing in between hall cal and present position. After the calculation of all the input parameter one output parameter is also calculated this will work as target for ANN. The output parameter is: x

According to the input given elevator 2 has the highest priority and this will serve the hall call in the upward direction. B. Simulation of ANN Using above input output variables of ANN a network can be trained. The trained network is now converted into Simulink block. This block can be tested for randomly selected sample data from the input data set. And it should give there respected output. In this Simulink model different 14 inputs are given with the help of MUX and three outputs are taken out with the help of DEMUX. The Simulink model is shown in fig 6. Here blue block is trained neural network which gives required output. Inputs are same as used training of the network but these are selected randomly.

Output

On basis of waiting time output is calculated. One elevator will have least waiting time then this will be assign to serve the hall call and remaining to will serve their respective last internal hall call. IV. SIMULATION AND RESULT MATLAB is used to apply the FUZZY logic and ANN. A. Simulation of FUZZY After working on FUZZY tool in MATLAB a simulation can be used to run the logic. Fig-5 shows the simulation of FUZZY constant blocks is used to provide input to fuzzy controller. And display is used to see the result.

Fig-6: Simulation of ANN

1) Result of ANN TABLE I.

TARGET OUTPUT AND ERROR OF ANN Error=

Error in

Target

Output

Output-Target

%

9

9.128

0.128

1.4%

7

7.118

0.118

1.66%

12

12.07

0.07

0.58%

This table shows the target (which should come after simulation) and output (result after simulating). Here error is the difference in both of them. Fig-5: Simulation of FUZZY

1) Result of FUZZY Logic As the result display the priority of each elevator according to the waiting time. Which has the highest waiting time will get the lowest priority and vice versa and this elevator will serve the hall call.

Performance plot is plotted between mean squared error and epochs. This plot shows three graphs training, validation and test. It also denotes the best validation performance which is “6.4455” at 14th epoch. The performance plot is shown in fig7.

IEEE International Conference on Computer, Communication and Control (IC4-2015).

Fig-7: Performance

After the training of network at epoch 18 minimum gradient is obtained which is “8.65×10^-10”, Mu at 18th iteration is “1×10^-11” and validation check is “4” at 18th iteration. Training state is shown in fig 8.

Fig-9: Error Histogram

Fig-8: Training State

Fig-10: Regression Plot

Error histogram is a bar plot in between error and instances. In this error is calculated from subtracting output from target. At 18th epoch it got minimum error which is “0.265”. Error histogram is shown in fig 9. Fig 10 shows the regression plot of trained ANN. This result contains four graphs all are plotted for target and output. So all plots should be linear but due to error it deviates from the linear path which shown by dotted line. In these plots first is training plot second is validation plot third is test plot and fourth plot combine above three plots and give overall result. The best training result is “0.99893”, a best validation is “0.82573”, and best test “0.64932” and overall result is “0.87506”.

V. CONCLUSION From the above research work we can conclude that proposed algorithm & hence program written on Matlab environment for design an economic and efficient elevator system has excellent outcome and is capable to model any middle level elevator entirely The issue of traffic in high rise commercial building specially in peak hours is solved with EGCS using soft computing techniques Among these techniques for fast and accurate result ANN and Fuzzy is used. Both ANN and Fuzzy are fissile for actual implementation but while comparing both them ANN better result than Fuzzy. The economic consideration for cost of installation & commissioning shows that Fuzzy has easy, economical & for better solution while on the basis of performance ANN is unbeatable.

IEEE International Conference on Computer, Communication and Control (IC4-2015).

REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]

[13]

[14]

[15]

[16]

[17]

[18]

[19] [20]

[21]

George R. Strakosch and Robert S. Caporale, The Vertical transportation handbook, Wiley India Pvt. Ltd. Walter T. Grondzik, Alison G. Kwok, Ben Stein and John S. Reynolds, Mechanical and Electrical Equipment for Building, Wiley India Pvt. Ltd. Gina Carol Barney, Elevator Traffic Handbook Theory and Practice, Spon press London, 2003. Charles A. Gross, Electric machine, CRC Press Taylor & Francis Group. Bimal K. Bose, Power Electronics and AC Drives, Prentice Hall PTR 2002. Duane Hanelman & Bruce Little Field, Mastering MATLAB 7, Pearson Education in South Asia. Amos Gilat, MATLAB an Introduction with Application, Wiley India Pvt. Ltd. New Delhi. Dr. Shailendra Jain, Modeling & Simulation using MATLABSimulink, Wiley India Pvt. Ltd. New Delhi. S. N. Sivanandam and S. N. Deepa, Principles of Soft Computing, Wiley India Pvt. Ltd. New Delhi. S. Rajasekaran and G. A. Vijayalakshmi Pai, Neural Network, Fuzzy logic and Genetic Algorithm Synthesis and Application, PHI Learning Privet Limited, New Delhi. R. S. Khurmi and J. K. Gupta, Theory of Machines, Eurasia Publishing House, 2005. Jianchang Liu, Yiyang Liu “Ant Colony Algorithm and Fuzzy Neural Network based Intelligent Dispatching Algorithm of an Elevator Group Control System”, 2007 IEEE International Conference on Control and Automation Guangzhou, CHINA - May 30 to June 1, 2007. Yine Zhang, Yun Yi and Jian Zhong “The Application of the Fuzzy Neural Network Control in Elevator Intelligent Scheduling Simulation”, Third International Symposium on Information Science and Engineering 2010 IEEE. Jianzhe Tai, Suying Yang and Hong Tan “Dispatching Approach Optimization of Elevator Group Control System with Destination Floor Guidance using Fuzzy Neural Network”, proceedings of the 7th World Congress on Intelligent Control and Automation June 25 - 27, 2008, Chongqing, China 2008 IEEE. Cao Liting Zhang Zhaoli and Hou Jue “Dynamic Optimized Dispatching System for Elevator Group Based on Artificial Intelligent Theory”, The Eighth International Conference on Electronic Measurement and Instruments 2007 IEEE. Zhi-Ming Chen, Fei Luo, Yu-Ge Xu and Jian-Zhong Cao “Design and Implementation of Modern Elevator Group Control System”, Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, Dalian, 13-16 August 2006 IEEE. Zhenshan. Yang, Cheng. Shao, and Guizhi. Li. “Multi-Objective Optimization for EGCS Using Improved PSO Algorithm”, Proceedings of the 2007 American Control Conference Marriott Marquis Hotel at Times Square New York City, USA, July 11-13, 2007 IEEE. ChangBum Kim, Kyoung A. Seong, Hyung Lee-Kwang, Member, IEEE, and Jeong O. Kim “Design and Implementation of a Fuzzy Elevator Group Control System”, IEEE Transactions On Systems, Man, And Cybernetics—Part A: Systems And Humans, Vol. 28, No. 3, May 1998. C. Erdem Imrak “Artifical Neural Network Application in Duplex/Triplex Elevator Group Control System”, Strojniski vestnik – Journal of Mechanical Engineering 54(2008)2, 103 – 114. Yanwu Gu “Multi-Objective Optimization of Multi-Agent Elevator Group Control System Based on Real-Time Particle Swarm Optimization Algorithm”, Scientific Research Engineering, 2012, 4, 368-378. J. Fernández, P. Cortés, Member, IEEE, J. Muñuzuri, and J. Guadix “Dynamic Fuzzy Logic Elevator Group Control System with Relative

[22]

[23]

[24]

Waiting Time Consideration”, IEEE Transactions on Industrial Electronics, Vol. 61, No. 9, September 2014. Naoki Imasaki, Susumu Kubot, Shoji Nakait, Tatsuo Yoshitsugut, Jun-ichi Kiji and Tsunekazu Endo “Elevator Group Control System Tuned By A Fuzzy Neural Network Applied Method”, TOSHIBA Corporation, Systems & Software Engineering Laboratory 1995 IEEE Zhu Dewen, * Jiang I,i,Zhou Yuwen,Shan Guanghui and He Kai “Modern Elevator Group Supervisory Control System and Neural Networks Technique”, International Conference on Intelligent Processing System 1997 IEEE. LIUTundong, LIAO Xiaosheng, and ZENG Jianping “Research for Distributed Elevator Group Control Base on Fuzzy Neural Network”, Proceedings of the 29th Chinese Control Conference July 29-31, 2010, Beijing, China.