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A Trajectory Tracking Design of Model Predictive Control for Autonomous Unmanned Helicopters

Endra Joelianto, Mega Yulianti

Abstract


Autonomous unmanned helicopters represent nonlinear complex systems, have multi mode characteristics and reactive dynamics in their flight missions. To fulfill flight missions completely and safely, model predictive control (MPC) has been considered recently as a tracking control system of autonomous unmanned helicopters especially for small helicopters. In recent years, the tracking control of autonomous unmanned helicopters is required to accomplish a complex trajectory such as in swarm and  formation control. Unfortunately, any steady state errors caused by insufficient control system designs may lead to degradation of tracking performances or even failure in tracking mission. The internal model principle (IMP) has been known to produce perfect tracking for control systems. In order to establish good trajectory tracking of autonomous unmanned helicopters, the paper considers the internal model principle for designning the trajectory tracking of model predictive control. Simulation results show that the tracking erros are reduced significantly by selecting the trajectory as combination of modes of the open loop control systems. Autonomous unmanned helicopters represent nonlinear complex systems, have multi mode characteristics and reactive dynamics in their flight missions. To fulfill flight missions completely and safely, model predictive control (MPC) has been considered recently as a tracking control system of
autonomous unmanned helicopters especially for small helicopters. In recent years, the tracking control of autonomous unmanned helicopters is required to accomplish a complex trajectory such as in swarm and formation control. Unfortunately, any steady state errors caused by insufficient control system designs may lead to degradation of tracking
performances or even failure in tracking mission. The internal model principle (IMP) has been known to produce perfect tracking for control systems. In order to establish good trajectory tracking of autonomous unmanned helicopters, the paper considers the internal model principle for designning the trajectory tracking of model predictive control. Simulation
results show that the tracking erros are reduced significantly by selecting the trajectory as combination of modes of the open loop control systems.

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References


K.P. Valavanis and G.J. Vachtsevanos, Handbook of Unmanned Aerial

Vehicles, Springer, 2015.

B. Mettler, Identification Modeling and Characteristic of Miniature Rotorcraft, Kluwer Academic Publishers, Boston, Massachusett, USA, 2003.

Pradana, W., Joelianto, E., Budiyono, A., and Adiprawita, W., ”Robust

MIMO H∞ Integral-Backstepping PID Controller for Hovering Control of Unmanned Model Helicopter.” J. Aerosp. Eng., 24(4), 454–462, 2011.

E. Joelianto, "Robust H∞ PID Controller Design Via LMI Solution of Dissipative Integral Backstepping with State Feedback Synthesis.", in in Robust Control, Theory and Applications, Andrzej Bartoszewicz (Ed.), pp. 69-88, 2011.

Qin, S.J. and T.A. Badgwell, T.A. (1997), “An Overview of Industrial Model Predictive Control Technology”, in Chemical Process Control-V: AIChe Symp. Series-American Institute Chemical Engineering, Vol. 93, pp. 232-256.

Wan, E.A. and Bogdanov, A.A. (2001), "Model predictive neural control

with applications to 6 DoF helicopter model," in American Control Conference 2001 proceedings of the international conference in Arlington, VA, 2001, pp. 488-493.

Singh, L. and Fuller, J. (2001), "Trajectory Generation for UAV in Urban Terrain, using Nonlinear MPC," in American Control Conference 2001 proceedings of the international conference in Arlington, VA, 2001, pp. 2301-2308.

Kim, H.J., Shim, D.H., and Sastry, S. (2002), "Nonlinear Model Predictive Tracking Control for Rotorcraft-based Unmanned Aerial Vehicles," in American Control Conference 2002 proceedings of the international conference in Anchorage, AK, 2002, pp. 3576-3581.

Castillo, C.L., Moreno, W. and Valavanis, K.P. (2009), “Unmanned Helicopter Waypoint Trajectory Tracking Using Model Predictive Control”, in 15th Mediterranean Conference on Control and Automation proceedings of the international conference in Athens, Greece, July 27-29, 2009.

Palunko, I. And Bogdan, S. (2008), “Small Helicopter Control Design Based on Model Reduction and Decoupling”, in Valavanis, K.P., Piegl, L.A. and Oh, P.Y. (Eds),Unmanned Aircraft Systems International Symposium on Unmanned Aerial Vehicles, UAV’08, Springer, pp. 201-228.

E. Joelianto, E.M. Sumarjono, A. Budiyono, D. R. Penggalih, "Model predictive control for autonomous unmanned helicopters", Aircraft Engineering and Aerospace Technology, Vol. 83 Iss: 6, pp.375 – 387, 2011.

Salmah, Sutrisno, Joelianto, E., Budiyono, A., Wijayanti, I. E., & Megawati, N. Y. (2013, August). Model predictive control for obstacle avoidance as hybrid systems of small scale helicopter. In Instrumentation Control and Automation (ICA), 2013 3rd International Conference on (pp. 127-132), 2013.

Sutrisno, Salmah, E. Joelianto, A. Budiyono, I.E. Wijayanti and N.Y. Megawati, Tracking Control for Hybrid System of Unmanned Small Scale Helicopter using Predictive Control, ROBIONETICS 2013, 25-27 November 2013, Yogyakarta-Indonesia, pp. 182-187, 2013.

Francis, B.A., and Wonham, W.M. (1976), ‘The Internal Model Principle of Control Theory’, Automatica, 12, 457–465.

Joelianto, E., and Williamson, D. (2009), ‘Transient Response Improvement of Feedback Control Systems Using Hybrid Reference Control’, International Journal of Control, 81(10), 1955–1970.

Joelianto, E., 2014, Finite Horizon Optimal Hybrid Reference Control

for Improving Transient Response of Feedback Control Systems, International Journal of Systems Science (IJSS), Vol. 45, No. 9, 1814-1829.




DOI: http://dx.doi.org/10.21535%2FProICIUS.2015.v11.655

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