Document Type : Research Articles

Authors

1 Department of Electrical and Robatic Engineering ,Shahrood University of Technology,Iran

2 Department of Electrical and Robatic Enbineering ,Shahrood University of Technology,Iran

Abstract

In this paper, a novel model-free control scheme is developed to enhance the tracking performance of robotic systems based on an adaptive dynamic sliding mode control and voltage control strategy. In the voltage control strategy, actuator dynamics have not been excluded. In other words, instead of the applied torques to the robot joints, motor voltages are computed by the control law. First, a dynamic sliding mode control is designed for the robotic system. Then, to enhance the tracking performance of the system, an adaptive mechanism is developed and integrated with the dynamic sliding mode control. Since the lumped uncertainty is unknown in practical applications, the uncertainty upper bound is necessary in the design of the dynamic sliding mode controller. Hence, the lumped uncertainty is estimated by an adaptive law. The stability of the closed-loop system is proved based on the Lyapunov stability theorem. The simulation results demonstrate the superior performance of the proposed adaptive dynamic sliding mode control strategy.

Keywords

Main Subjects

[1] Y. Su, , C. Zheng, and P. Mercorelli, “Robust approximate
fixed-time tracking control for uncertain robot
manipulators”, Mechanical Systems and Signal
Processing, Vol. 135, (2020) p.106379.

[2] R. Gholipour, M.M. Fateh, “Robust Control of Robotic
Manipulators in the Task-Space Using an Adaptive
Observer Based on Chebyshev Polynomials”, Journal of
Systems Science and Complexity, (2020).
https://doi.org/10.1007/s11424-020-8186-0.

[3] M. Bekrani, M. Heydari, and S.T. Behrooz, “An Adaptive
Control Method Based on Interval Fuzzy Sliding Mode for
Direct Matrix Converters”, International Journal of
Industrial Electronics, Control and Optimization, Vol. 3,
No. 2, (2020), 159-171.

[4] S.H. Shahalami, and F. Rajab Nejad, “Design of Adaptive
Back-Stepping Controller for Chaos Control in Boost
Converter and Controller Coefficients Optimization Using
CHPSO Algorithm”, International Journal of Industrial
Electronics, Control and Optimization. Vol. 3, No.3,
(2020), 249-257.

[5] A. Haqshenas M, M.M. Fateh, and S.M. Ahmadi, “Adaptive
control of electrically‐driven nonholonomic wheeled
mobile robots: Taylor series‐based approach with
guaranteed asymptotic stability”, International Journal of
Adaptive Control and Signal Processing, (2020),
https://doi.org/10.1002/acs.3104.

[6] S.M. Ahmadi, and M.M. Fateh, “Task-space control of robots
using an adaptive Taylor series uncertainty
estimator”, International Journal of Control, Vol. 92, No. 9,
(2019), 2159-2169.

[7] M. Van, S.S. Ge and H. Ren, “Finite time fault tolerant
control for robot manipulators using time delay estimation
and continuous nonsingular fast terminal sliding mode
control”, IEEE transactions on cybernetics, Vol. 47, No. 7,
(2017), 1681-1693.

[8] R. Gholipour, A. Khosravi and H. Mojallali, “Multi-objective
optimal backstepping controller design for chaos control in
a rod-type plasma torch system using Bees
Algorithm”, Applied Mathematical Modelling, Vol. 39,
No. 15, (2015), 4432-4444.

[9] S. Park and S. Rahmdel, “A new fuzzy sliding mode
controller with auto-adjustable saturation boundary layers
implemented on vehicle suspension”, International Journal
of Engineering-Transactions C: Aspects, Vol. 26, No. 12,
(2013), 1401-1410.

[10] Y. Zhao, P. Huang and F. Zhang, “Dynamic modeling and
Super-Twisting Sliding Mode Control for Tethered Space
Robot”, Acta Astronautica, Vol. 143, (2018), 310-321.

[11] G. Chen, B. Jin, Y. Chen, “Nonsingular fast terminal sliding
mode posture control for six-legged walking robots with
redundant actuation”, Mechatronics, Vol. 50, (2018),
1-15.

[12] F.J. Lin, S.Y. Chen K.K. Shyu, “Robust dynamic
sliding-mode control using adaptive RENN for magnetic
levitation system”, IEEE Transactions on Neural
Networks, Vol. 20, No. 6, (2009), 938-951.

[13] S.Y. Chen, S.S. Gong, “Speed tracking control of pneumatic
motor servo systems using observation-based adaptive
dynamic sliding-mode control”, Mechanical Systems and
Signal Processing, Vol. 94, (2017) , 111-128.

[14] S. Wen, M.Z. Chen, Z. Zeng, X. Yu and T. Huang, “Fuzzy
control for uncertain vehicle active suspension systems via
dynamic sliding-mode approach”, IEEE Transactions on
Systems, Man, and Cybernetics: Systems, Vol. 47, No. 1,
(2017), 24-32.

[15] S. Khorashadizadeh and M.M. Fateh, “Uncertainty
estimation in robust tracking control of robot manipulators
using the Fourier series expansion”, Robotica, Vol. 35, No.
2, (2017), 310-336.

[16] S. Khorashadizadeh and M.H. Majidi, “Chaos
synchronization using the Fourier series expansion with
application to secure
communications”, AEU-International Journal of
Electronics and Communications, Vol. 82, (2017), 37-44.

[17] R. Gholipour and M.M. Fateh, “Adaptive task-space control
of robot manipulators using the Fourier series expansion
without task-space velocity
measurements”, Measurement, Vol. 123, (2018), 285-292.

[18] M.R. Shokoohinia and M.M. Fateh, “Robust dynamic
sliding mode control of robot manipulators using the
Fourier series expansion”, Transactions of the Institute of
Measurement and Control,
https://doi.org/10.1177/0142331218802357, 2018.

[19] M.R.Shokoohinia, M.M. Fateh, and R. Gholipour, “Design
of an adaptive dynamic sliding mode control approach for
robotic systems via uncertainty estimators with
exponential convergence rate”, SN Applied Sciences, Vol.
2, No. 2, (2020), 1-11.
[20] M.M. Fateh and M. Sadeghijaleh, “Voltage control strategy
for direct-drive robots driven by permanent magnet
synchronous motors”, International Journal of
Engineering-Transactions B: Applications, Vol. 28, No. 5,
(2015), 709-716.

[21] M.M. Fateh and A. Arab, “Robust control of a wheeled
mobile robot by voltage control strategy”, Nonlinear
Dynamics, Vol. 79, No. 1, (2015), 335-348.

[22] S. Khorashadizadeh and M.M. Fateh, “Robust task-space
control of robot manipulators using Legendre polynomials
for uncertainty estimation”, Nonlinear Dynamics, Vol. 79,
No. 2, (2015), 1151-1161.

[23] R. Gholipour and M.M. Fateh, “Observer-based robust
task-space control of robot manipulators using Legendre
polynomial”, In Electrical Engineering (ICEE), 2017
Iranian Conference on (pp. 766-771). IEEE, (2017).

[24] M.W. Spong, S. Hutchinson and M. Vidyasagar, “Robot
modeling and control”, (Vol. 3, pp. 187-227). New York:
Wiley, (2006).

[25] F.J. Lin, S.G. Chen and I.F. Sun, “Intelligent sliding-mode
position control using recurrent wavelet fuzzy neural
network for electrical power steering system” International
journal of fuzzy systems, Vol. 19, No. 5, (2017),
1344-1361.

[26] F.J. Lin, S.G. Chen and I.F. Sun, “Adaptive backstepping
control of six‐phase PMSM using functional link radial
basis function network uncertainty observer”, Asian
Journal of Control, Vol. 19, No. 6, (2017), 2255-2269.

[27] R. Gholipour and M.M. Fateh, “Designing a Robust Control
Scheme for Robotic Systems with an Adaptive
Observer”, International Journal of Engineering,
Transactions B: Applications, Vol. 32, No. 2, (2019),
270-276.

[28] F. Lin, S. Chen and C. Hsu, , “Intelligent Backstepping
Control Using Recurrent Feature Selection Fuzzy Neural
Network for Synchronous Reluctance Motor Position
Servo Drive System”, IEEE Transactions on Fuzzy
Systems, Vol. 27, No. 3, (2019), 413-427.

[29] J.J.E. Slotine and W. Li, “Applied nonlinear control”, (Vol.
199, No. 1). Englewood Cliffs, NJ: Prentice hall, (1991).

[30] M.M. Fateh and S. Khorashadizadeh, “Robust control of
electrically driven robots by adaptive fuzzy estimation of
uncertainty”, Nonlinear Dynamics, Vol. 69, No. 3, (2012),
1465-1477.

[31] E. Salahshour, M. Malekzadeh, R. Gholipour, and
S.Khorashadizadeh, “Designing multi-layer quantum
neural network controller for chaos control of rod-type
plasma torch system using improved particle swarm
optimization”, Evolving Systems, Vol. 10, No. 3, (2019),
317-331.

[32] R. Gholipour, J. Addeh, H. Mojallali, and A. Khosravi,
“Multi-objective evolutionary optimization of PID
controller by chaotic particle swarm
optimization”, International Journal of Computer and
Electrical Engineering, Vol. 4, No. 6, (2012), 833-838.

[33] R. Gholipour, , H. Mojallali, and S.M.K., Akhlaghi, “A
Novel Particle Swarm Optimization with Passive
Congregation via Chaotic Sequences”, International
Journal of Computer and Electrical Engineering, Vol. 4,
No. 6, (2012), 809-815.

[34] R. Gholipour, A. Khosravi, and H. Mojallali, “Suppression
of chaotic behavior in duffing-holmes system using
back-stepping controller optimized by unified particle
swarm optimization algorithm”, International Journal of
Engineering, Transactions B: Applications, Vol. 26, No.
11, (2013), 1299-1306.

[35] R. Gholipour, A. Khosravi, and H. Mojallali, “Parameter
estimation of loranz chaotic dynamic system using bees
algorithm”, International Journal of Engineering,
Transactions C: Aspects, Vol. 26, No. 3, (2013), 257-262.

[36] N. Pourmousa, S.M. Ebrahimi, M. Malekzadeh, and M.
Alizadeh, “Parameter estimation of photovoltaic cells
using improved Lozi map based chaotic optimization
Algorithm”, Solar Energy, Vol. 180, (2019), 180-191.

[37] J. Farzaneh, , R. Keypour, and A. Karsaz, “A novel fast
maximum power point tracking for a PV system using
hybrid PSO-ANFIS algorithm under partial shading
conditions”, International Journal of Industrial Electronics,
Control and Optimization, Vol. 2, No. 1, (2019), 47-58.

[38] H. Moradi CheshmehBeigi, and A. Mohamadi, “Torque
Ripple Minimization in SRM Based on Advanced Torque
Sharing Function Modified by Genetic Algorithm
Combined with Fuzzy PSO”, International Journal of
Industrial Electronics, Control and Optimization, Vol. 1,
No. 1, (2018), 71-80.

[39] M. Dehghani, Z. Montazeri, O.P. Malik, A. Ehsanifar, and
A. Dehghani, “OSA: Orientation Search
Algorithm”, International Journal of Industrial Electronics,
Control and Optimization, Vol. 2, No. 2, (2019), 99-112.

[40] N. Ghaffarzadeh, and H. Faramarzi, “A new whale
optimization algorithm based fault location method by
focusing on dispersed model of the transmission
line”, International Journal of Industrial Electronics,
Control and Optimization, (2020), doi:
10.22111/ieco.2020.32027.1218.

[41] M.E.B. Aguilar, D.V. Coury, R. Reginatto, and R.M.
Monaro, “Multi-objective PSO applied to PI control of
DFIG wind turbine under electrical fault
conditions”, Electric Power Systems Research, Vol. 180,
(2020), p.106081,
https://doi.org/10.1016/j.epsr.2019.106081.

[42] Z. Hu, , X. Xu, , Q. Su, , H. Zhu, and J. Guo, “Grey
prediction evolution algorithm for global
optimization”, Applied Mathematical Modelling, Vol. 79,
(2020), 145-160.

[43] M. Kohler, M.M. Vellasco, and R. Tanscheit, “PSO+: A
new particle swarm optimization algorithm for constrained
problems”, Applied Soft Computing, Vol. 85, (2019),
p.105865, https://doi.org/10.1016/j.asoc.2019.105865.

[44] A. Taheri, and N. Asgari, “Sliding Mode Control of LLC
Resonant DC-DC Converter for Wide Output Voltage
Range in Battery Charging”, International Journal of
Industrial Electronics, Control and Optimization, Vol. 2,
No. 2, (2019), 127-136.