Jaya-Based Optimal Type-2 Fuzzy Sliding Mode Controller Design for a Cylindrical Robotic Arm

Mokhtari, Abolfazl; Alaviyan, Yasin

doi:10.22034/jast.2024.445621.1177

Jaya-Based Optimal Type-2 Fuzzy Sliding Mode Controller Design for a Cylindrical Robotic Arm

Document Type : Original Article

Authors

Abolfazl Mokhtari ¹

Yasin Alaviyan ²

¹ Department of Flight and Engineering , Imam Ali University, Tehran, Iran

² Department of Electrical Engineering , Shahid Beheshti University, Tehran, Iran

10.22034/jast.2024.445621.1177

Abstract

In this study an optimal Jaya-based interval type-2 fuzzy high-order sliding mode control (IT2-FSMC) for the trajectory tracking of a cylindrical robotic arm is presented. Sliding mode control (SMC) is offered for robustness against external disturbance and parameter uncertainty. The chattering phenomenon is a common issue of the classic sliding mode controls that can be harmful to the actuators. Therefore, the High-Order sliding mode control (HSMC) method is developed to improve control performance and reduce chattering effects. Type-2 fuzzy systems are added to the control system witch membership functions are intervals that can be very useful in handling uncertainties and minimizing their effects. To improve controlling performance, a Type-2 fuzzy system is combined with the HSMC controller called type-2 High-Order sliding mode control (T2HSMC). Moreover, to make the best choice for parameters of the fuzzy membership functions and sliding surface gains, the Jaya optimization algorithm is offered to optimally tune the mentioned parameters. Hence, the Jaya optimization algorithm is added to the control system and consequently, the proposed controller has been named Optimal type-2 High-Order sliding mode control (OT2HSMC). Also, by utilizing the Lyapunov stability theorem, the closed-loop system stability is proved. Finally, the simulation results for the cylindrical robotic arm present the advantage of the suggested controller against disturbance and parameter uncertainties, and the merits of the proposed controller in decreasing chattering effects are illustrated.

Keywords

Type2 fuzzy logic

High order Sliding mode control

Jaya algorithm

Cylindrical robotic arm

Subjects

Flight Mechanics Stability & Contorl

[1] R. Buckingham, A. Graham, “Dexterous manipulators for nuclear inspection and maintenance—case study,” in: 2010 1st International Conference on Applied Robotics for the Power Industry, IEEE, 2010, pp. 1-6.
[2] J. Sikorski, I. Dawson, A. Denasi, E.E. Hekman, S. Misra, “Introducing BigMag—A novel system for 3D magnetic actuation of flexible surgical manipulators,” in: 2017 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2017, pp. 3594-3599.
[3] M.W. Spong, M. Vidyasagar, Robot dynamics and control, John Wiley & Sons, 2008.
[4] H. Wang, “Adaptive control of robot manipulators with uncertain kinematics and dynamics,” IEEE Transactions on Automatic Control, 62(2) (2016) 948-954.
[5] A. Hussein, A. Basset. “Neural Network-Based Adaptive Control of Robotic Manipulator: Application to a Three-Links Cylindrical Robot.” Iraqi Journal for Electrical & Electronic Engineering 13, no. 1 (2017).
[6] L.V. Truong, S.D. Huang, V.T. Yen, P.V. Cuong, “Adaptive trajectory neural network tracking control for industrial robot manipulators with deadzone robust compensator,” International Journal of Control, Automation and Systems, 18(9) (2020) 2423-2434.
[7] M. Makarov, M. Grossard, P. Rodriguez-Ayerbe, D. Dumur, “Modeling and preview $ H_\infty $ control design for motion control of elastic-joint robots with uncertainties,” IEEE Transactions on Industrial Electronics, 63(10) (2016) 6429-6438.
[8] A. Boudallaa, M. Chennani, D. Belkhayat, and K. Rhofir. “Vector control of asynchronous motor of drive train using speed controller h∞.” Emerging Science Journal 6, no. 4 (2022): 834-850.
[9] J. Viola, L. Angel, “Identification, control, and robustness analysis of a robotic system using fractional control,” IEEE Latin America Transactions, 13(5) (2015) 1294-1302.
[10] S. Mobayen, “A novel global sliding mode control based on exponential reaching law for a class of underactuated systems with external disturbances,” Journal of Computational and Nonlinear Dynamics, 11(2) (2016).
[11] A.H. Khan, S. Li, “Sliding mode control with PID sliding surface for active vibration damping of pneumatically actuated soft robots,” IEEE Access, 8 (2020) 88793-88800.
[12] C.-J. Lin, T.-Y. Sie, W.-L. Chu, H.-T. Yau, C.-H. Ding, “Tracking control of pneumatic artificial muscle-activated robot arm based on sliding-mode control,” in: Actuators, MDPI, 2021, pp. 66.
[13] P. Stuart, and E. Lindo Secco. “Design of a biomimetic BLDC driven robotic arm for teleoperation & biomedical applications.” J Hum Earth Future. ISSN (2022): 2785-2997
[14] L. Liu, W.X. Zheng, S. Ding, “High-order sliding mode controller design subject to lower-triangular nonlinearity and its application to robotic system,” Journal of the Franklin Institute, 357(15) (2020) 10367-10386.
[15] B. Brahmi, M. Driscoll, M.H. Laraki, A. Brahmi, “Adaptive high-order sliding mode control based on quasi-time delay estimation for uncertain robot manipulator,” Control Theory and Technology, 18(3) (2020) 279-292.
[16] Y. Alaviyan, A.A. Afzalian, “Decentralized Fuzzy Sliding Mode Control Using Jaya Algorithm,” in: 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), IEEE, 2020, pp. 1-5.
[17] M. Aghaseyedabdollah, M. Abedi, M. Pourgholi, “Supervisory adaptive fuzzy sliding mode control with optimal Jaya-Based fuzzy PID sliding surface for a planer cable robot,” Soft Computing, 26(17) (2022) 8441-8458.
[18] K. Zheng, Y. Hu, B. Wu, “Intelligent fuzzy sliding mode control for complex robot system with disturbances,” European Journal of Control, 51 (2020) 95-109.
[19] C. Liu, G. Wen, Z. Zhao, R. Sedaghati, “Neural-network-based sliding-mode control of an uncertain robot using dynamic model approximated switching gain,” IEEE transactions on cybernetics, 51(5) (2020) 2339-2346.
[20] M.F. Hamza, H.J. Yap, I.A. Choudhury, H. Chiroma and T. Kumbasar. “A survey on advancement of hybrid type 2 fuzzy sliding mode control,” Neural Computing and Applications, 30(2) (2018) 331-353.
[21] S.M. Nodeh, M.H. Ghasemi, H. Mohammadi Daniali, “Robust Tuned Controller Based on Interval Type 2 Fuzzy Logic for Robotic Manipulators Exposed to Perturbations and Parametric Uncertainties.” J Control Autom Electr Syst, 30, (2019) 323–336.
[22] M.S. Qureshi, P. Swarnkar, S. Gupta, A supervisory online tuned fuzzy logic based sliding mode control for robotics: An application to surgical robots, Robotics and Autonomous Systems, 109 (2018) 68-85.
[23] Y. Alaviyan, MH. Aghaseyedabdollah, MH. Sadafi, and A. Yazdizade. “Design and manufacture a smart greenhouse with supervisory control of environmental parameters using a fuzzy inference controller.” In 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), pp. 1-6. IEEE, 2020.
[24] S. Zhe, S. Hu, H. Xie, H. Li, J. Zheng, and B. Chen. “Fuzzy adaptive recursive terminal sliding mode control for an agricultural omnidirectional mobile robot.” Computers and Electrical Engineering, 105 (2023): 108529.
[25] M. Aghaseyedabdollah, Y. Alaviyan, H. Azmi, A. Yazdizadeh, “Fuzzy Fractional Order Sliding Mode Controller Design for a Wind Turbine with DFIG,” in: 2021 29th Iranian Conference on Electrical Engineering (ICEE), IEEE , (2021) 637-642.
[26] X. Yin, L. Pan, S. Cai, “Robust adaptive fuzzy sliding mode trajectory tracking control for serial robotic manipulators, Robotics and Computer-Integrated Manufacturing,” 72 (2021) 101884.
[27] P.J. Gaidhane, M.J. Nigam, A. Kumar, P.M. Pradhan, “Design of interval type-2 fuzzy precompensated PID controller applied to two-DOF robotic manipulator with variable payload,” ISA transactions, 89 (2019) 169-185.
[28] J.M. Mendel, R.I. John, F. Liu, “Interval type-2 fuzzy logic systems made simple,” IEEE transactions on fuzzy systems, 14(6) (2006) 808-821.
[29] J. Liu, T. Zhao, S. Dian, “General type-2 fuzzy sliding mode control for motion balance adjusting of power-line inspection robot,” Soft Computing, 25(2) (2021) 1033-1047.
[30] H. Qin, H. Yang, Y. Sun, Y. Zhang, “Adaptive interval type-2 fuzzy fixed-time control for underwater walking robot with error constraints and actuator faults using prescribed performance terminal sliding-mode surfaces,” International Journal of Fuzzy Systems, 23(4) (2021) 1137-1149.
[31] S. Dian, J. Han, R. Guo. “Double Closed-Loop General Type-2 Fuzzy Sliding Model Control for Trajectory Tracking of Wheeled Mobile Robots.” Int. J. Fuzzy Syst. 21, (2019) 2032–2042.
[32] C. Lamini, S. Benhlima, A. Elbekri, “Genetic algorithm-based approach for autonomous mobile robot path planning,” Procedia Computer Science, 127 (2018) 180-189.
[33] K. Vanchinathan, K. R. Valluvan, C. Gnanavel, and C. Gokul. “Design methodology and experimental verification of intelligent speed controllers for sensorless permanent magnet brushless DC motor: intelligent speed controllers for electric motor.” International Transactions on Electrical Energy Systems, 31, no. 9 (2021): e12991.
[34] M. Aghaseyedabdollah, M. Abedi, and M. Pourgholi. “Supervisory adaptive interval type-2 fuzzy sliding mode control for planar cable-driven parallel robots using Grasshopper optimization.” Iranian Journal of Fuzzy Systems, vol. 19, no. 5 (2022).
[35] F.S. Gharehchopogh, H. Gholizadeh, “A comprehensive survey: Whale Optimization Algorithm and its applications, Swarm and Evolutionary Computation,” vol. 48 (2019) 1-24.
[36] K. Vanchinathan., K. R. Valluvan, C. Gnanavel, and C. Gokul. “Numerical simulation and experimental verification of fractional-order PIλ controller for solar PV fed sensorless brushless DC motor using whale optimization algorithm.” Electric Power Components and Systems 50, no. 1-2 (2022): 64-80.
[37] Z. Masomi, Zynab, M. Yaghoobi, and H. R. Kobravi. “Optimal fuzzy controller design for industrial gear system by gray wolf algorithm.” International Journal of Dynamics and Control, vol. 11, no. 4 (2023): 1856-1866.
[38] Y. Zhang, S. Cheng, Y. Shi, D.-w. Gong, X. Zhao, “Cost-sensitive feature selection using two-archive multi-objective artificial bee colony algorithm,” Expert Systems with Applications, vol. 137 (2019) 46-58.
[39] K. Vanchinathan, and N. Selvaganesan. “Adaptive fractional order PID controller tuning for brushless DC motor using artificial bee colony algorithm.” Results in Control and Optimization 4 (2021): 100032.
[40] H. Duong, Q. Nguyen, D. Tien Nguyen, and L. Nguyen. “PSO-based hybrid PID-FLC Sugeno control for excitation system of large synchronous motor.” Emerging Science Journal, vol. 6, no. 2 (2022): 201-216
[41] K. Vanchinathan, V. Karumanchetty, T. Ramasamy, and G. Chinnaraj. “Systematic design of multi-objective enhanced genetic algorithm optimized fractional order PID controller for sensorless brushless DC motor drive.” Circuit World ahead-of-print (2021).
[42] A. Dey, L.H. Son, A. Pal, H.V. Long, “Fuzzy minimum spanning tree with interval type 2 fuzzy arc length: formulation and a new genetic algorithm,” Soft Computing, vol. 24, no. 6 (2020) 3963-3974.
[43] R. Rao, “Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems,” International Journal of Industrial Engineering Computations, vol. 7, no. 1 (2016) 19-34.
[44] M. J.V. Hari, M. Patil, “Optimization of FUZZY controller by JAYA algorithm,” Int Res J Eng Technol (IRJET), vol. 4, no. 7 (2017) 2182-2186.
[45] M. Aghaseyedabdollah, Y. Alaviyan, A. Yazdizadeh, “IoT-Based Smart Greenhouse Design with an Intelligent Supervisory Fuzzy Optimized Controller,” in: 2021 7th International Conference on Web Research (ICWR), IEEE, 2021, pp. 311-317.
[46] C. Torres, J. de Jesús Rubio, C.F. Aguilar-Ibánez, J.H. Pérez-Cruz, “Stable optimal control applied to a cylindrical robotic arm,” Neural Computing and Applications, vol. 24, no. 3, (2014) 937-944.