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PID and inverse-model-based control of a twin rotor system

Published online by Cambridge University Press:  15 March 2011

S. F. Toha  and
M. O. Tokhi
S. F. Toha*
Affiliation:
Department of Mechatronics, Faculty of Engineering, International Islamic University Malaysia, Kuala Lumpur, Malaysia
M. O. Tokhi
Affiliation:
Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK
*
*Corresponding author. E-mail: [email protected]
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Summary

The use of active control techniques has intensified in various control applications, particularly in the field of aircraft systems. This paper presents an investigation into the control of rigid-body and flexible motion of a twin rotor multi-input multi-output system (TRMS) using intelligent inverse-model-based control schemes. The TRMS is an aerodynamic test rig representing the control challenges of modern air vehicle. The augmented feedback PID and feedforward inverse-model-based control has led to good tracking response and vibration reduction of the TRMS, with the use of particle swarm optimisation (PSO). As a comparison, methods using PID controllers are also presented. Experimental results are obtained using the test rig, confirming the viability and effectiveness of the proposed methodology as opposed to conventional PID controllers. The results and evidence from this method are justified, presented and discussed.

Keywords

Twin rotor systemPIDInverse modelParticle swarm optimisation
Type
Articles
Information
Robotica , Volume 29 , Issue 6 , October 2011 , pp. 929 - 938
Copyright
Copyright © Cambridge University Press 2011

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References

1.Callender, D. R.Hartree, and Porter, A., “Time-lag in a control system,” Phil. Trans. R. Soc. 235 (756), 415–444 (1936).Google Scholar
2.Odwyer, , Handbook of PI and PID Controller Tuning Rules (Imperial College Press, London, 2003).CrossRefGoogle Scholar
3.Kuo, C., Automatic Control Systems (Prentice-Hall, New Jersey, 1995).Google Scholar
4.Kumar, V., Rana, K. P. S. and Gupta, V., “Real-time performance evaluation of a Fuzzy PI + Fuzzy PD controller for liquid-level process,” Int. J. Intell. Control Syst. 13 (2), 89–96 (2008).Google Scholar
5.Ziegler, J. G. and Nichols, N. B., “Optimum settings for automatic controllers,” Trans. ASME 64, 759–768 (1942).Google Scholar
6.Cohen, G. H. and Coon, G. A., “Theoretical consideration of retarded control,” Trans. ASME 75, 827–834 (1953).Google Scholar
7.Åström, K. J. and Hagglud, T., PID Controller: Theory, Design and Tuning (Research Triangle Park, NC, USA, 1995).Google Scholar
8.Ho, W. K., Hang, C. C. and Cao, L. S., “Tuning of PID controllers based on gain and phase margins specifications,” Automatica 31 (3), 497–502 (1995).CrossRefGoogle Scholar
9.Gaing, Z. L., “A particle swarm optimisation approach for optimum design of PID controller in AVR system,” IEEE Tran. Energy Convers. 19, 384–391 (2004).CrossRefGoogle Scholar
10.Coelho, L. D. S., “Tuning of PID controller for an automatic regulator voltage system using chaotic optimization approach,” Chaos Solitons Fractals 39 (4), 1504–1514 (2009).CrossRefGoogle Scholar
11.Van Overschee, P. and De Moor, B., “RaPID: The End of Heuristic PID Tuning,” Preprints Proceeding PID '00: IFAC Workshop, Terrassa, Spain (2000) pp. 687–692.Google Scholar
12.Ender, B., “Process control performance: Not as good as you think,” Control Eng. 9, 180–190 (1993).Google Scholar
13.Hägglund, T., “A control-loop performance monitor,” Control Eng. Pract. 3 (11), 1543–1551 (1995).CrossRefGoogle Scholar
14.Lin, M., Lakshminarayanan, S. and Rangaiah, G. P., “A comparative study of recent/popular PID tuning rules for stable, first order plus dead time single input single output processes,” Ind. Eng. Chem. Res. 47 (2), 344–368 (2008).CrossRefGoogle Scholar
15.Chang, W.-D. and Shih, S.-P., “PID controller design of nonlinear systems using an improved particle swarm optimization approach,” Commun. Nonlinear Sci. Numer. Simul. 15 (11), 3632–3639 (2010).CrossRefGoogle Scholar
16.Leva, A., Negro, S. and Vittorio Papadopoulos, A., “PI/PID autotuning with contextual model parametrisation,” J. Process Control 20 (4), 452–463 (2010).CrossRefGoogle Scholar
17.Kao, C.-C., Chuang, C.-W. and Fung, R.-F., “The self-tuning PID control in a slider-crank mechanism system by applying particle swarm optimization approach,” Mechatronics 16 (8), 513–522 (2006).CrossRefGoogle Scholar
18.Kim, T.-H., Maruta, I. and Sugie, T., “Robust PID controller tuning based on the constrained particle swarm optimization,” Automatica 44 (4), 1104–1110 (2008).CrossRefGoogle Scholar
19.Kennedy, J. and Eberhart, R., “Particle Swarm Optimization,” IEEE International Conference on Neural Networks, Perth, Australia (1995) pp. 1942–1948.Google Scholar
20.Clerc, M. and Kennedy, J., “The particle swarm – explosion, stability, and convergence in a multidimensional complex space,” IEEE Trans. Evolutionary Comput. 6 (1), 58–73 (2002).CrossRefGoogle Scholar
21.Eberhart, R. C. and Shi, Y., Computational Intelligence Concepts to Implementations (Morgan Kauffmann Publisher, USA, 2007).Google Scholar
22.Toha, S. F., Abd Latiff, I., Mohammad, M. and Tokhi, M. O., “Parametric Modelling of a TRMS using Dynamic Spread Factor Particle Swarm Optimisation,” 11th International Conference on Computer Modelling and Simulation (UKSIM09), Cambridge, UK (2009) pp. 95–100.CrossRefGoogle Scholar
23.Toha, S. F. and Tokhi, M. O., “Parametric modelling application to a twin rotor system using RLS, genetic and swarm optimisation techniques,” Proc. Inst. Mech. Eng. 224 (9), 961–977 (2010), DOI: 10.1243/09544100JAERO09544706.CrossRefGoogle Scholar
24.Zeng, S. and Zhu, J., “Adaptive Compensated Dynamic Inversion Control for a Helicopter with Approximate Mathematical Model,” International Conference on Computational Intelligence for Modelling, Control and Automation, Sydney, Australia (2006) pp. 208–208.Google Scholar
25.Rahideh, A., Shaheed, H. and Bajodah, A., “Adaptive non-linear model inversion control of a twin rotor multi-input multi-output system using artificial intelligence,” Proc. Inst. Mech. Eng. 221 (3), 343–351 (2007).CrossRefGoogle Scholar
26.Danai, K., “Helicopter Rotor Tuning,” In: Vibration and Shock Handbook (Silva, C. W. D., ed.) (Taylor & Francis, CRC Press, USA, 2005).Google Scholar
27.Feedback Instrument Limited, Twin Rotor MIMO System Manual 33-007-0 (Sussex, UK, 1996).Google Scholar
28.Ahmad, S., Chipperfield, A. and Tokhi, M., “Parametric modelling and dynamic characterization of a two-degree-of-freedom twin-rotor multi-input multi-output system,” Proc. Inst. Mech. Eng. 215 (2), 63–78 (2001).CrossRefGoogle Scholar
29.Abd Latiff, I. and Tokhi, M. O., “Fast Convergence Strategy for Particle Swarm Optimization using Spreading Factor,” IEEE Congress on Evolutionary Computation, Trondheim, Norway (2009) pp. 2693–2700.Google Scholar
30.Rapaic, M. R. and Kanovic, Z., “Time-varying PSO – convergence analysis, convergence-related parameterization and new parameter adjustment schemes,” Inf. Process. Lett. 109 (11), 548–552 (2009).CrossRefGoogle Scholar
31.Lee, S., Ha, C. and Kim, B. S., “Adaptive nonlinear control system design for helicopter robust command augmentation,” Aerosp. Sci. Technol. 9 (3), 241–251 (2005).CrossRefGoogle Scholar
32.Ljung, L., System Identification: Theory for the User (Prentice-Hall, Englewood Cliffs, New Jersey, 1999).Google Scholar