Implementation of Fuzzy Logic Control System on Rotary Car Parking System Prototype

Rotary car parking system (RCPS) is one of the effective parking models used in the metropolitan area because the mechanical parking system is designed vertically to conserve the land usage. This paper discussed the implementation of fuzzy logic with the Sugeno Inference Model on the RCPS miniature control system. The research started with kinematics analysis and a mathematical model was derived to determine the slot position and optimal power requirements for each condition. Furthermore, the Fuzzy Inference model used was the Sugeno Model, taking into account two variables: distance and angle. These two variables were selected because in the designed miniature RCPS there will be rotational changes of rotation and rotation in turn. Variable distance was divided into four clusters, such as Zero, Near, Medium and Far. While the angle variables were divided into four clusters as well, such as Zero, Small, Medium, and Big. The test results on a miniature RCPS consisting of six parking slots showed that fuzzy based control provided better results when compared to conventional systems. Step response on the control system without fuzzy control showed the rise time value of 0.58 seconds, peak time of 0.85 seconds, settling time of 0.89, percentage overshoot of 0.20%, and steady state error of 4.14%. While the fuzzy control system provided the rise time value of 0.54 seconds, settling time of 0.83 seconds, steady state error of 2.32%, with no overshoot.

[1]  Raviraj P. Shelke,et al.  Rotary Automated Car Parking System , 2019 .

[2]  Xiang-Gui Guo,et al.  Adaptive Fuzzy Fault-Tolerant Control for Multiple High Speed Trains , 2017, Multi-Agent Systems.

[3]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[4]  P. S. Londhe,et al.  Fuzzy Sliding Mode Control for Spatial Control of Large Nuclear Reactor , 2015, IEEE Transactions on Nuclear Science.

[5]  Philip A. Adewuyi Performance Evaluation of Mamdani-type and Sugeno-type Fuzzy Inference System Based Controllers for Computer Fan , 2012 .

[6]  Lotfi A. Zadeh,et al.  Fuzzy logic - a personal perspective , 2015, Fuzzy Sets Syst..

[7]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[8]  Chuen-Chien Lee FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I , 1990 .

[9]  Jishun Li,et al.  Optimization of an Intelligent Controller for Parallel Autonomous Parking , 2012 .

[10]  E. H. Mamdani,et al.  Prescriptive method for deriving control policy in a fuzzy-logic controller , 1975 .

[11]  Yufei Xu,et al.  Adaptive Fault-Tolerant Tracking Control of Near-Space Vehicle Using Takagi–Sugeno Fuzzy Models , 2010, IEEE Transactions on Fuzzy Systems.

[12]  Edwin Milton Calderon Mendoza,et al.  Design of Neuro-Fuzzy Controller for Control of Water Distribution in an Irrigation Main Canal , 2016, IEEE Latin America Transactions.

[13]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[14]  Teddy Surya Gunawan,et al.  Development of smart chicken poultry farm , 2018 .

[15]  Tie Wang Simulate Study of Automatic Parking System , 2013 .

[16]  Pablo Cortés,et al.  Dynamic Fuzzy Logic Elevator Group Control System With Relative Waiting Time Consideration , 2014, IEEE Transactions on Industrial Electronics.

[17]  Mu-King Tsay,et al.  Fuzzy Power Control for Downlink CDMA-Based LMDS Network , 2008, IEEE Transactions on Vehicular Technology.