Design and real-time implementation of a decentralized sliding mode controller for twin rotor multi-input multi-output system

Abstract

The article deals with a real-time implementation of a decentralized sliding mode controller applied to a twin rotor multi-input multi-output system, a system with 2 degrees of freedom, strongly coupled and its dynamic resembles that of a helicopter. The work is motivated by the fact that in the literature several control techniques have been proposed for the twin rotor multi-input multi-output system control without being applied to the system, and the considered authors presented just the simulation results. To control the vertical and horizontal positions of the twin rotor multi-input multi-output system, the system is decoupled into two subsystems, vertical and horizontal, controlled by two independent sliding mode regulators calculated from the mathematical models of vertical and horizontal subsystems, respectively. From the results of real-time control of the twin rotor multi-input multi-output system in stabilization and tracking modes, and performing robustness and disturbance rejection tests, the effectiveness of the suggested control scheme was proven.

Keywords

Sliding mode controller twin rotor multi-input multi-output system robust control real-time control decentralized control

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References

Feedback Instruments Ltd. Twin rotor MIMO: system user manual. Crowborough: Feedback Instruments Ltd, 1998.

Darus

IZM

Aldebrez

Tokhi

. Parametric modelling of a twin rotor system using genetic algorithms. In: Proceedings of the first international symposium on control, communications and signal processing, Hammamet, Tunisia, 21–24 March 2004, pp.115–118. New York: IEEE.

Shaheed

. Performance analysis of 4 types of conjugate gradient algorithms in the nonlinear dynamic modelling of a TRMS using feedforward neural networks (vol. 6). In: Proceedings of the IEEE international conference on systems, man and cybernetics, The Hague, Netherlands, 10–13 October 2004; pp.5985–5990. New York: IEEE.

Toha

Tokhi

. MLP and Elman recurrent neural network modelling for the TRMS. In: Proceedings of the 7th IEEE international conference on cybernetic intelligent systems, Middlesex University London, United Kingdom 9–10 September 2008, pp.1–6. New York: IEEE.

Toha

Latiff

Mohamad

. Parametric modelling of a TRMS using dynamic spread factor particle swarm optimisation. In: Proceedings of the 11th international conference on computer modelling and simulation, Cambridge, United Kingdom, 25–27 March 2009, pp.95–100.

Aldebrez

Darus

IZM

Tokhi

. Dynamic modelling of a twin rotor system in hovering position. In: Proceedings of the first international symposium on control, communications and signal processing, Hammamet, Tunisia, 21–24 March 2004, pp.823–826. New York: IEEE.

Rahideh

Shaheed

. Dynamic modelling of a twin rotor MIMO system using grey box approach. In: Proceedings of the 5th international symposium on mechatronics and its applications, Amman, Jordan, 27–29 March 2008, pp.1–6. New York: IEEE.

Rahideh

Shaheed

Bajodah

. Adaptive nonlinear model inversion control of a twin rotor system using artificial intelligence. In: Proceedings of the IEEE international conference on control applications, Suntec Convention Centre, Singapore, 1–3 October 2007, pp.898–903. New York: IEEE.

Rahideh

Shaheed

Bajodah

. Neural network based adaptive nonlinear model inversion control of a twin rotor system in real time. In: Proceedings of the 7th IEEE international conference on cybernetic intelligent systems, Middlesex University London, United Kingdom, 9–10 September 2008, pp.1–6. New York: IEEE.

10.

Rahideh

Shaheed

. Robust model predictive control of a twin rotor MIMO system. In: Proceedings of the IEEE international conference on mechatronics, Hotel Silken Puerta de Malaga, Malaga, Spain, 14–17 April 2009, pp.1–6. New York: IEEE.

11.

Saroj

Kar

. T-S fuzzy model based controller and observer design for a twin rotor MIMO system. In: Proceedings of the IEEE international conference on fuzzy systems (FUZZ), Hyderabad International Convention Center (HICC) Hyderabad, India, 7–10 July 2013, pp.1–8. New York: IEEE.

12.

Chuan-Sheng

Liang-Rui

Bing-Ze

. Improvement of the twin rotor MIMO system tracking and transient response using fuzzy control technology. In: Proceedings of the 1st IEEE conference on industrial electronics and applications, Marina Mandarin, Singapore, 24–26 May 2006, pp.1–6.

13.

Taskin

. Improving pitch and yaw motion control of twin rotor MIMO system. J Vibroeng 2014; 16(4): 1650–1660.

14.

Jahed

Farrokhi

. Robust adaptive fuzzy control of twin rotor MIMO system. Soft Comput 2013; 17(10): 1847–1860.

15.

Zeghlache

Kara

Saigaa

. Type-2 fuzzy logic control of a 2-DOF helicopter (TRMS system). Cent Eur J Eng 2014; 4(3): 303–315.

16.

Young

Utkin

Ozguner

. A control engineer’s guide to sliding mode control. IEEE T Contr Syst T 1999; 7(3): 328–342.

17.

DeCarlo

Zak

Matthews

. Variable structure control of nonlinear multivariable systems: a tutorial. P IEEE 1988; 76(3): 212–232.

18.

Utkin

Jingxin

. Integral sliding mode in systems operating under uncertainty conditions (vol. 4). In: Proceedings of the 35th IEEE conference on decision and control, Kobe, Japan, 13 December 1996, pp.4591–4596. New York: IEEE.

19.

Utkin

. Variable structure systems with sliding modes. IEEE T Automat Contr 1977; 22(2): 212–222.

20.

Slotine

Sastry

. Robustness issues in the sliding control of non-linear systems. In: Proceedings of the 22nd IEEE conference on decision and control, Marriott Hotel, San Antonio, Texas, USA, December 1983, pp.1125–1127. New York: IEEE.