Disturbance rejection of three-axis gimbal mechanism using PSO-optimized Fuzzy-PID controller

Document Type : Full Length Article

Authors

1 Assistant Professor, Department of Computer and Electrical Engineering, Arak University of Technology, Arak, Iran

2 Assistant Professor, Department of Mechanical Engineering, Arak University of Technology, Arak, Iran

10.22064/tava.2021.124105.1162

Abstract

This paper covers the application of the Fuzzy-PID controller to stabilize the three-axis gimbal payload orientation with respect to the inertial frame in the presence of platform motion. In this way, the effect of external disturbances induced by the operating environment is greatly reduced, thereby increasing the accuracy of ultimate operation. Three independent controllers are employed to maintain the triple angles of the three-axis gimbal payload at a desired orientation. The Fuzzy-PID controller benefits the typical PID control structure in which the control gains are adaptively computed by a fuzzy inference system. The particle swarm optimization algorithm is also used to calculate the optimal values of input-output scale factors. A co-simulation platform is considered by coupling the PSO-optimized Fuzzy-PID controllers modeled in Matlab/Simulink to the mechanical gimbal model designed in Solidworks and exported to Simulink by using Simscape toolbox, aiming at the calculation of control torque needed for stabilization of gimbal payload. The effectiveness of the proposed controller is examined by simulation of the designed system for various commanded angles in the presence of different disturbances, including sinusoidal disturbances with diverse frequencies and random vibrations. According to the adopted collaborative simulations, it is shown that the utilized control system performs very well in the tracking of the desired angles and also the rejection of the applied perturbations.

Highlights

  • Fuzzy-PID controller to stabilize the three-axis gimbal payload orientation is introduced.
  • Three controllers are employed to maintain the angles of the gimbal payload.
  • PID control parameters are adaptively computed by a fuzzy inference system.
  • A co-simulation platform is created by coupling Solidworks and Matlab/Simulink.
  • PSO algorithm is used to calculate the optimal values of input-output scaling factors.

Keywords

Main Subjects


[1] J. Hilkert, Inertially stabilized platform technology concepts and principles, IEEE control systems magazine, 28 (2008) 26-46.
[2] M.K. Masten, Inertially stabilized platforms for optical imaging systems, IEEE Control Systems Magazine, 28 (2008) 47-64.
[3] P.J. Kennedy, R.L. Kennedy, Direct versus indirect line of sight (LOS) stabilization, IEEE Transactions on control systems technology, 11 (2003) 3-15.
[4] S. Kim, S. Kim, Y. Kwak, Robust control for a two-axis gimbaled sensor system with multivariable feedback systems, IET control theory & applications, 4 (2010) 539-551.
[5] B. Ahi, A. Nobakhti, Hardware implementation of an ADRC controller on a gimbal mechanism, IEEE Transactions on Control Systems Technology, 26 (2017) 2268-2275.
[6] W. Ren, Q. Qiao, K. Nie, Y. Mao, Robust DOBC for Stabilization Loop of a Two-Axes Gimbal System, IEEE Access, 7 (2019) 110554-110562.
[7] F. Wang, R. Wang, E. Liu, W. Zhang, Stabilization Control Mothed for Two-Axis Inertially Stabilized Platform Based on Active Disturbance Rejection Control With Noise Reduction Disturbance Observer, IEEE Access, 7 (2019) 99521-99529.
[8] A. Altan, R. Hacıoğlu, Model predictive control of three-axis gimbal system mounted on UAV for real-time target tracking under external disturbances, Mechanical Systems and Signal Processing, 138 (2020) 106548.
[9] Y.B. Shtessel, Decentralized sliding mode control in three-axis inertial platforms, Journal of Guidance, Control, and Dynamics, 18 (1995) 773-781.
[10] F. Dong, X. Lei, W. Chou, A dynamic model and control method for a two-axis inertially stabilized platform, IEEE Transactions on Industrial Electronics, 64 (2016) 432-439.
[11] J. Mao, J. Yang, X. Liu, S. Li, Q. Li, Modeling and Robust Continuous TSM Control for an Inertially Stabilized Platform With Couplings, IEEE Transactions on Control Systems Technology, (2019).
[12] F. Liu, H. Wang, Q. Shi, H. Wang, M. Zhang, H. Zhao, Comparison of an ANFIS and fuzzy PID control model for performance in a two-axis inertial stabilized platform, IEEE Access, 5 (2017) 12951-12962.
[13] M.M. Abdo, A.R. Vali, A.R. Toloei, M.R. Arvan, Stabilization loop of a two axes gimbal system using self-tuning PID type fuzzy controller, ISA transactions, 53 (2014) 591-602.
[14] G. Sun, X. Wu, Z. Zhong, A self-tuning fuzzy-PID stabilization experiment of a seeker inertial platform's tracking loop subject to input saturation and dead-zone, in: 2016 IEEE International Conference on Aircraft Utility Systems (AUS), IEEE, 2016, pp. 580-585.
[15] G. Hummer, J.C. Rasaiah, J.P. Noworyta, Water conduction through the hydrophobic channel of a carbon nanotube, Nature, 414 (2001) 188-190.
[16] S.M. Hasheminejad, A.H. Rabiee, H. Bahrami, Active closed-loop vortex-induced vibration control of an elastically mounted circular cylinder at low Reynolds number using feedback rotary oscillations, Acta Mechanica, 229 (2018) 231-250.
[17] A.H. Rabiee, Regenerative semi-active vortex-induced vibration control of elastic circular cylinder considering the effects of capacitance value and control parameters, Journal of Mechanical Science and Technology, 32 (2018) 5583-5595.
[18] A.H. Rabiee, Galloping and VIV control of square-section cylinder utilizing direct opposing smart control force, Journal of Theoretical and Applied Vibration and Acoustics, 5 (2019) 69-84.
[19] A.H. Rabiee, M. Esmaeili, Simultaneous vortex-and wake-induced vibration suppression of tandem-arranged circular cylinders using active feedback control system, Journal of Sound and Vibration, (2019) 115131.
[20] S.M. Hasheminejad, A.H. Rabiee, A. Markazi, Dual-Functional Electromagnetic Energy Harvesting and Vortex-Induced Vibration Control of an Elastically Mounted Circular Cylinder, Journal of Engineering Mechanics, 144 (2017) 04017184.
[21] C. Killian, Modern Control Technology: Components and Systems, Thompson Delmar, 2005.
[22] Z.-Y. Zhao, M. Tomizuka, S. Isaka, Fuzzy gain scheduling of PID controllers, IEEE transactions on systems, man, and cybernetics, 23 (1993) 1392-1398.
[23] X. Gong, D. Cao, Fuzzy proportional-integral-derivative control of an overhang rotor with double discs based on the active tilting pad journal bearing, Journal of Vibration and Control, 19 (2013) 1487-1498.
[24] G. Li, B. Li, D. Wu, J. Du, G. Yang, Feedback linearization-based self-tuning fuzzy proportional integral derivative control for atmospheric pressure simulator, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 228 (2014) 385-392.
[25] S. Azali, M. Sheikhan, Intelligent control of photovoltaic system using BPSO-GSA-optimized neural network and fuzzy-based PID for maximum power point tracking, Applied Intelligence, 44 (2016) 88-110.
[26] J. Kennedy, R. Eberhart, Particle swarm optimization, in: Proceedings of ICNN'95-International Conference on Neural Networks, IEEE, 1995, pp. 1942-1948.