Fuzzy sliding mode control for trajectory tracking of an electric powered wheelchair

Mohammed Mecifi; Abdelmadjid Boumediene; Djamila Boubekeur; Mohammed Mecifi; Abdelmadjid Boumediene; Djamila Boubekeur

doi:10.3934/electreng.2021010

AIMS Electronics and Electrical Engineering

2021, Volume 5, Issue 2: 176-193. doi: 10.3934/electreng.2021010

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Research article

Fuzzy sliding mode control for trajectory tracking of an electric powered wheelchair

1.
Electrical Engineering Department, Faculty of Technology, University of Tlemcen, Tlemcen, Algeria
2.
Oran1 University, Oran, Algeria

Received: 03 May 2021 Accepted: 06 July 2021 Published: 12 July 2021

Various controllers have been applied to control the dynamics of Electric Powered Wheelchair (EPW) for people whose walking are difficult or impossible, due to illness or disability. This paper deals with the nonlinear control of an electric wheelchair based on the hybridization between fuzzy logic and sliding mode control called Fuzzy Sliding Mode Control (FSMC). The EPW is powered by two Permanent Magnet Synchronous Motors (PMSM) due to some advantageous features, such as high efficiency, high torque to the current ratio, low noise and robustness. This research aims to present the dynamic modelling of both EPW motors with Lagrangian method in the first step, and the application of fuzzy sliding mode control in the second. This control technique was presented in order to consider the full dynamic model while alleviating the chattering phenomenon and to increase trajectory tracking performance of the EPW in the presence of disturbances. However, the reference trajectory used is that generated by the fifth-degree polynomial interpolation, which ensures a regular trajectory that is continuous in positions, velocities and accelerations. Finally, numerical simulations are presented to show the evolution of electrical and mechanical quantities in order to verify the effectiveness of the control strategy.
- electric powered wheelchair,
- fuzzy sliding mode control,
- dynamics modelling,
- permanent magnet synchronous motor,
- trajectory tracking
Citation: Mohammed Mecifi, Abdelmadjid Boumediene, Djamila Boubekeur. Fuzzy sliding mode control for trajectory tracking of an electric powered wheelchair[J]. AIMS Electronics and Electrical Engineering, 2021, 5(2): 176-193. doi: 10.3934/electreng.2021010

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Abstract

Various controllers have been applied to control the dynamics of Electric Powered Wheelchair (EPW) for people whose walking are difficult or impossible, due to illness or disability. This paper deals with the nonlinear control of an electric wheelchair based on the hybridization between fuzzy logic and sliding mode control called Fuzzy Sliding Mode Control (FSMC). The EPW is powered by two Permanent Magnet Synchronous Motors (PMSM) due to some advantageous features, such as high efficiency, high torque to the current ratio, low noise and robustness. This research aims to present the dynamic modelling of both EPW motors with Lagrangian method in the first step, and the application of fuzzy sliding mode control in the second. This control technique was presented in order to consider the full dynamic model while alleviating the chattering phenomenon and to increase trajectory tracking performance of the EPW in the presence of disturbances. However, the reference trajectory used is that generated by the fifth-degree polynomial interpolation, which ensures a regular trajectory that is continuous in positions, velocities and accelerations. Finally, numerical simulations are presented to show the evolution of electrical and mechanical quantities in order to verify the effectiveness of the control strategy.

References

[1]	Nguyen TN, Su SW, Nguyen HT (2011) Robust neuro-sliding mode multivariable control strategy for powered wheelchairs. IEEE T Neur Sys Reh 19: 105–111. doi: 10.1109/TNSRE.2010.2069104
[2]	Al_Okby MFR, Neubert S, Stoll N, et al. (2017) Low-cost Hybrid Wheelchair Controller for Quadriplegias and Paralysis Patients. Advances in Science Technology and Engineering Systems Journal 2: 687-694. doi: 10.25046/aj020388
[3]	Geonea I, Dumitru N, Dumitru V (2015) Design and Motion Analysis of a Powered Wheelchair. Applied Mechanics and Materials 772: 613-620. doi: 10.4028/www.scientific.net/AMM.772.613
[4]	Boubekeur D, Boumédiène A, Sari Z, et al. (2015) Modeling and Backstepping Control For Electric Powered Wheelchair. International Electrical and Computer Engineering Conference, Setif, Algeria.
[5]	Mecifi M, Boumédiène A, Boubekeur D (2019) A Fuzzy Logic Controller for Electric Powered Wheelchair Based on Lagrange Model. International Conference on Advanced Electrical Engineering, Algiers, Algeria.
[6]	Dou XX, Tong QM (2011) The Application of Permanent Magnet Electric Machine on Electrical Wheelchair. Advanced Materials Research 383: 7161-7165.
[7]	Boquete L, Garcia R, Berea R, et al. (1999) Neural control of the movements of a wheelchair. Journal of Intelligent and Robotic Systems 25: 213–226. doi: 10.1023/A:1008068322312
[8]	Ma J (2014) Design on Electric Power Wheel Control System. Advanced Materials Research 1070: 1668-1671.
[9]	Maatoug K, Njah M, Jallouli M (2019) Electric Wheelchair Trajectory Tracking Control based on Fuzzy Logic Controller. 19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, Sousse, Tunisia.
[10]	Kawasaki T, Yokoyama M, Tsuchiya M (2007) Sliding Mode Control for Electric Power Assist Systems. Journal of System Design and Dynamics 1: 159-167. doi: 10.1299/jsdd.1.159
[11]	Ayten KK, Dumlu A, Kaleli A (2017) Real-Time Trajectory Tracking Control for Electric-Powered Wheelchairs Using Model-Based Multivariable Sliding Mode Control. 5th International Symposium on Electrical and Electronics Engineering, Galati, Romania.
[12]	Bingül Z, Karahan O (2011) A Fuzzy Logic Controller tuned with PSO for 2 DOF robot trajectory control. Expert Syst Appl 38: 1017-1031. doi: 10.1016/j.eswa.2010.07.131
[13]	Soltanpour MR, Khooban MH, Soltani M (2014) Robust fuzzy sliding mode control for tracking the robot manipulator in joint space and in presence of uncertainties. Robotica 32: 433-446. doi: 10.1017/S0263574713000702
[14]	Nafia N, El Kari A, Ayad H, et al. (2018) A robust type-2 fuzzy sliding mode controller for disturbed MIMO nonlinear systems with unknown dynamics. Automatika 59: 194-207. doi: 10.1080/00051144.2018.1521568
[15]	Pang H, Yang J, Liang J, et al. (2018) On Enhanced Fuzzy Sliding-Mode Controller and Its Chattering Suppression for Vehicle Semi-Active Suspension System. WCX World Congress Experience, Detroit Michigan, United States.
[16]	Cao W, Lin C, Zhang L, et al. (2017) Fuzzy Sliding Mode Control of Networked Control Systems and Applications to Independent-Drive Electric Vehicles. IEEE International Conference on Industrial Technology, Toronto, Canada.
[17]	Yan TH, Wu B, He B, et al. (2016) A Novel Fuzzy Sliding-Mode Control for Discrete-Time Uncertain System. Mathl Probl Eng 2016: 1-9.
[18]	Bendaas I, Naceri F (2013) A New Method to Minimize the Chattering Phenomenon in Sliding Mode Control Based on Intelligent Control for Induction Motor Drives. Serbian Journal of Electrical Engineering 10: 231-246. doi: 10.2298/SJEE130108001B
[19]	Zhang H, Fang H, Zhang D, et al. (2020) Adaptive Fuzzy Sliding Mode Control for a 3-DOF Parallel Manipulator with Parameters Uncertainties. Complexity 2020: 1-16.
[20]	Cherifi D, Miloud Y (2020) Hybrid Control Using Adaptive Fuzzy Sliding Mode Control of Doubly Fed Induction Generator for Wind Energy Conversion System. Periodica Polytechnica Electrical Engineering and Computer Science 64: 374-381. doi: 10.3311/PPee.15508
[21]	Lei X, Jianqiao Z, Chuang L, et al. (2021) Fuzzy-logic-based adaptive event-triggered sliding mode control for spacecraft attitude tracking. Aerosp Sci Technol 108: 106394. doi: 10.1016/j.ast.2020.106394
[22]	Song BK, An JH, Choi SB (2017) A New Fuzzy Sliding Mode Controller with a Disturbance Estimator for Robust Vibration Control of a Semi-Active Vehicle Suspension System. Applied Sciences 7: 1053. doi: 10.3390/app7101053
[23]	Teng L, Bai S (2019) Fuzzy Sliding Mode Control of An Upper-Limb Exoskeleton Robot. IEEE International Conference on Cybernetics and Intelligent Systems and IEEE Conference on Robotics, Automation and Mechatronics, Bangkok, Thailand.
[24]	Soltanpour MR, Zaare S, Haghgoo M, et al. (2020) Free-Chattering Fuzzy Sliding Mode Control of Robot Manipulators with Joints Flexibility in Presence of Matched and Mismatched Uncertainties in Model Dynamic and Actuators. J Intell Robot Syst 100: 47-69. doi: 10.1007/s10846-020-01178-0
[25]	Wang Y, Xie X, Chadli M, et al. (2020) Sliding Mode Control of Fuzzy Singularly Perturbed Descriptor Systems. IEEE T Fuzzy Syst, China.
[26]	Dhaouadi R, Abu Hatab A (2013) Dynamic Modelling of Differential-Drive Mobile Robots using Lagrange and Newton-Euler Methodologies: A Unified Framework. Advances in Robotics and Automation 2: 1-7.
[27]	Aggarwal A (2013) Electronic differential in electric vehicles. International Journal of Scientific & Engineering Research 4: 1322-1326.
[28]	Ozkop E, Altas IH, Okumus HI, et al. (2015) A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV. Int J Electron 102: 1919-1942. doi: 10.1080/00207217.2015.1010183
[29]	Tsai MC, Wu KS, Hsueh PW (2009) Synchronized Motion Control for Power-Wheelchairs. Fourth International Conference on Innovative Computing, Information and Control, Kaohsiung, Taiwan.
[30]	Sareena A, Mathew RP (2019) Application of PID Controller and Nonlinear Sliding Mode Control on Two Link Robotic. International Journal of Engineering Research & Technology 8: 504-508.
[31]	Ben Jridi A, Chaker N, Aloui H, et al. (2016) Sliding Mode Control for Speed's Tracking of an Electrical Vehicle. Proceedings of the International Conference on Recent Advances in Electrical Systems, Sfax, Tunisia.
[32]	Hosseini SH, Tabatabaei M (2017) IPMSM velocity and current control using MTPA based adaptive fractional order sliding mode controller. Eng Sci Technol 20: 896-908.
[33]	Irfan S, Mehmood A, Raeeaq MT, et al. (2018) Advanced sliding control techniques for inverted Pendulum: Modelling and simulation. Eng Sci Technol 21: 753-759.
[34]	Londhe PS, Patre BM (2019) Adaptive fuzzy sliding mode control for robust trajectory tracking control of an autonomous underwater vehicle. Intel Serv Robot 12: 87–102. doi: 10.1007/s11370-018-0263-z
[35]	Dombre E, Khalil W (2007) Modeling, Performance Analysis and Control of Robot Manipulators. ISTE, London.
[36]	Qaid MM, Miskon MF, Bahar MB, et al. (2017) Comparative Study Between Quintic and Cubic Polynomial Equations Based Walking Trajectory of Exoskeleton System. International Journal of Mechanical & Mechatronics Engineering 17: 43-51.
[37]	Zhao X, Wang M, Liu N, et al. (2017) Trajectory Planning for 6-DOF Robotic Arm Based on Quintic Polynomial. 2nd International Conference on Control, Automation, and Artificial Intelligence, Sanya, China.

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