Application of intelligent adaptive force control for a helicopter seat suspension

Document Type : Full Length Article

Authors

1 Assistant Professor, School of Mechanical Engineering, Arak University of Technology, Arak, Iran

2 MSc Student, Arak University of Technology, Arak, Iran

10.22064/tava.2021.122019.1158

Abstract

The high level of transmitted noise and vibrations of a helicopter flight to the aircrew body can cause discomfort and perhaps affect their performance and physical condition. This paper presents an active seat suspension system with intelligent active force control (AFC) using an artificial neural network (ANN), iterative learning (IL) algorithm, and fuzzy logic (FL) to reduce vibration on the helicopter seat. Therefore, three control schemes are considered for this application, namely AFCANN, AFCIL, and AFCFL. Computer simulations have been performed using MATLAB software to verify the proposed control schemes. The pilot head displacement, acceleration, and also seat acceleration transmissibility are selected as target variables. The simulation results illustrate that the usage of proposed control schemes leads to effective control of target variables, especially active force control using fuzzy logic (AFCFL), which is showing superior performance and accuracy between other intelligent adaptive force control schemes.In future work, this controller will be assessed by experimental tests.

Highlights

  • Active seat suspension attenuates helicopter vibrations transmitted to the crew.
  • Intelligent active force controllers were assessed for seat suspension by simulations.
  • AI methods (IL, ANN and FL) were implemented to estimate system’s mass parameter.
  • Active force control using fuzzy logic (AFCFL) shows superior performance among other control schemes.

Keywords

Main Subjects


[1] N.A.C. Branco, E. Rodriguez, The vibroacoustic disease--an emerging pathology, Aviation, space, and environmental medicine, 70 (1999) A1-A6.
[2] Y. Chen, V. Wickramasinghe, D.G. Zimcik, Development of adaptive helicopter seat systems for aircrew vibration mitigation, in:  Active and Passive Smart Structures and Integrated Systems 2008, International Society for Optics and Photonics, 2008, pp. 69280N.
[3] P.C. Chen, I. Chopra, Wind tunnel test of a smart rotor model with individual blade twist control, Journal of Intelligent Material Systems and Structures, 8 (1997) 414-425.
[4] F.K. Straub, H.T. Ngo, V. Anand, D.B. Domzalski, Development of a piezoelectric actuator for trailing edge flap control of full scale rotor blades, Smart materials and structures, 10 (2001) 25.
[5] K. Nguyen, M. Betzina, C. Kitaplioglu, Full‐Scale Demonstration of Higher Harmonic Control for Noise and Vibration Reduction on the XV‐15 Rotor, Journal of the American Helicopter Society, 46 (2001) 182-191.
[6] S.B. Choi, J.H. Choi, M.H. Nam, C.C. Cheong, H. Lee, A semi-active suspension using ER fluids for a commercial vehicle seat, Journal of Intelligent Material Systems and Structures, 9 (1998) 601-606.
[7] S.-B. Choi, M.-H. Nam, B.-K. Lee, Vibration control of a MR seat damper for commercial vehicles, Journal of intelligent material systems and structures, 11 (2000) 936-944.
[8] P. De Man, P. Lemerle, P. Mistrot, J.-P. Verschueren, A. Preumont, An investigation of a semiactive suspension for a fork lift truck, Vehicle System Dynamics, 43 (2005) 107-119.
[9] C. Liangbin, C. Dayue, A two-stage vibration isolation system featuring an electrorheological damper via the semi-active static output feedback variable structure control method, Journal of Vibration and Control, 10 (2004) 683-706.
[10] C. Park, D. Jeon, Semiactive vibration control of a smart seat with an MR fluid damper considering its time delay, Journal of intelligent material systems and structures, 13 (2002) 521-524.
[11] S.J. McManus, K.S.t. Clair, P.E. Boileau, J. Boutin, S. Rakheja, Evaluation of vibration and shock attenuation performance of a suspension seat with a semi-active magnetorheological fluid damper, Journal of Sound and Vibration, 253 (2002) 313-327.
[12] Y.-T. Choi, N.M. Wereley, Biodynamic response mitigation to shock loads using magnetorheological helicopter crew seat suspensions, Journal of aircraft, 42 (2005) 1288-1295.
[13] G.J. Hiemenz, W. Hu, N.M. Wereley, Semi-active magnetorheological helicopter crew seat suspension for vibration isolation, Journal of Aircraft, 45 (2008) 945-953.
[14] Y. Chen, V. Wickramasinghe, D. Zimcik, Development of adaptive seat mounts for helicopter aircrew body vibration reduction, Journal of Vibration and Control, 15 (2009) 1809-1825.
[15] Y. Chen, V. Wickramasinghe, D.G. Zimcik, Development of adaptive helicopter seat for aircrew vibration reduction, Journal of Intelligent Material Systems and Structures, 22 (2011) 489-502.
[16] M. Mailah, N.I.A. Rahim, Intelligent active force control of a robot arm using fuzzy logic, in:  2000 TENCON proceedings. Intelligent systems and technologies for the new millennium (Cat. No. 00CH37119), IEEE, 2000, pp. 291-296.
[17] G. Priyandoko, M. Mailah, Simulation of suspension system with adaptive fuzzy active force control, To all Intelligent Active Force Control Research Group, 30 (2007).
[18] G. Priyandoko, M. Mailah, H. Jamaluddin, Vehicle active suspension system using skyhook adaptive neuro active force control, Mechanical systems and signal processing, 23 (2009) 855-868.
[19] K. Rajeswari, P. Lakshmi, Simulation of suspension system with intelligent active force control, in:  2010 International Conference on Advances in Recent Technologies in Communication and Computing, IEEE, 2010, pp. 271-277.
[20] S. Ahmadi, M. Gohari, M. Tahmasebi, Intelligent active force control of a helicopter seat suspension using iterative learning algorithm, in:  2016 6th International Conference on Computer and Knowledge Engineering (ICCKE), IEEE, 2016, pp. 30-35.
[21] J.S. Burdess, J.R. Hewit, An active method for the control of mechanical systems in the presence of unmeasurable forcing, Mechanism and Machine Theory, 21 (1986) 393-400.
[22] H. Jahanabadi, M. Mailah, h. M.d., Active control of a robotic arm with pneumatic artificial muscle actuator, in:  Proceedings of third South East Asian Technical Universities Consortium Symposium, 2009, pp. 417-420.
[23] M. Mailah, Intelligent active force control of a rigid robot arm using neural network and iterative learning algorithms, in, University of Dundee, 1998.
[24] M. Tahmasebi, M. Mailah, M. Gohari, R. Abd Rahman, Vibration suppression of sprayer boom structure using active torque control and iterative learning. Part I: Modelling and control via simulation, Journal of Vibration and Control, 24 (2018) 4689-4699.
[25] S.B. Hussein, H. Jamaluddin, M. Mailah, A.M.S. Zalzala, A hybrid intelligent active force controller for robot arms using evolutionary neural networks, in:  Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512), IEEE, 2000, pp. 117-124.
[26] C.K. Loo, R. Mandava, M.V.C. Rao, A hybrid intelligent active force controller for articulated robot arms using dynamic structure neural network, Journal of Intelligent and Robotic Systems, 40 (2004) 113-145.
[27] M. Tahmasebi, R.A. Rahman, M. Mailah, M. Gohari, Roll movement control of a spray boom structure using active force control with artificial neural network strategy, Journal of low frequency noise, vibration and active control, 32 (2013) 189-201.
[28] L.C. Kwek, E.K. Wong, C.K. Loo, M.V.C. Rao, Application of active force control and iterative learning in a 5-link biped robot, Journal of Intelligent and Robotic Systems, 37 (2003) 143-162.
[29] M. Mailah, H.M. Hooi, S. Kazi, H. Jahanabadi, Practical active force control with iterative learning scheme applied to a pneumatic artificial muscle actuated robotic arm, International Journal of Mechanics, 1 (2012) 88-96.
[30] M. Tahmasebi, M. Gohari, M. Mailah, R. Abd Rahman, Vibration suppression of sprayer boom structure using active torque control and iterative learning. Part II: Experimental implementation, Journal of Vibration and Control, 24 (2018) 4740-4750.
[31] H. Jahanabadi, M. Mailah, M.-M. Zain, H.M. Hooi, Active force with fuzzy logic control of a two-link arm driven by pneumatic artificial muscles, Journal of Bionic Engineering, 8 (2011) 474-484.
[32] S. Arimoto, S. Kawamura, F. Miyazaki, Bettering operation of robots by learning, Journal of Robotic systems, 1 (1984) 123-140.
[33] L.A. Zadeh, Fuzzy sets. Information and control, in:  Fuzzy sets, World Scientific, 1965, pp. 338-353.