Combined system identification and robust control of a gimbal platform

Gimbaled imaging systems require very high performance inertial stabilization loops to achieve clear image acquisition, precise pointing, and tracking performance. Therefore, higher bandwidths become essential to meet recent increased performance demands. However, such systems often posses flexible dynamics around target bandwidth and time delay of gyroscope sensors which put certain limit to achievable bandwidths. For inertial stabilization loops, widely used design techniques have difficulty in achieving large bandwidth and satisfying required robustness simultaneously. Clearly, high performance control design hinges on accurate control-relevant model set. For that reason, combined system identification and robust control method is preferred. In the system identification step, accurate nominal model is obtained, which is suitable for subsequent robust control synthesis. Model validation based uncertainty modeling procedure constructs the robust-control-relevant uncertain model set, which facilitates the high performance controller design. Later, with skewed-µ synthesis, controller is designed which satisfies large bandwidth and robustness requirements. Finally, the experimental results show that significant performance improvement is achieved compared to common manual loop shaping methods. In addition, increased performance demands for new imaging systems are fulfilled.

PDF

___

[1] Ali H, Ali A, Shaikh IUH. Disturbance observer based control of twin rotor aerodynamic system. Turkish Journal of Electrical Engineering & Computer Sciences 2020; 28 (4): 2213-2227. doi:10.3906/elk-1912-34
[2] Baskın M, Leblebicioğlu K. Robust control for line-of-sight stabilization of a two-axis gimbal system. Turkish Journal of Electrical Engineering & Computer Sciences 2017; 25 (5): 3839-3853. doi:10.3906/elk-1606-435
[3] Moaveni B, Khorshidi M. Robust speed controller design for induction motors based on IFOC and Kharitonov theorem. Turkish Journal of Electrical Engineering & Computer Sciences 2015; 23 (4): 1173-1186. doi:10.3906/elk1306-22
[4] Oomen T, Bosgra O. Selecting uncertainty structures in identification for robust control with an automotive application. In: 16th IFAC Symposium on System Identification; Brussels, Belgium; 2012. pp. 601-606.
[5] Oomen T, van Herpen R, Quist S, van de Wal M, Bosgra O et al. Connecting system identification and robust control for next-generation motion control of a wafer stage. IEEE Transactions on Control Systems Technology 2014; 22 (1): 102-118. doi: 10.1109/TCST.2013.2245668
[6] Özdoğan G, Leblebicioğlu K. Frequency response function measurement and parametric SISO system modelling of a gyro-stabilized infrared electro optic gimbal system. Transactions of the Institute of Measurement and Control 2016; 38 (5): 512-528. doi:10.1177/0142331215596806
[7] Oomen T, Bosgra O. Well-posed model uncertainty estimation by design of validation experiments. In: 15th IFAC Symposium on System Identification; Saint-Malo, France; 2009. pp. 1199-1204.
[8] Oomen T, Bosgra O. Estimating disturbances and model uncertainty in model validation for robust control. In: 47th IEEE Conference on Decision and Control; Cancun, Mexico; 2008.
[9] Oomen T, Maarten S. Identification for robust control of complex systems: Algorithm and motion application. In: Lovera M (editor). Control-oriented Modelling and Identification: Theory and practice. Institution of Engineering and Technology, 2015, pp. 101-124.
[10] Oomen T, van Herpen R, Bosgra O. Robust-control-relevant coprime factor identification with application to model validation of a wafer stage. In: 15th IFAC Symposium on System Identification; Saint-Malo, France; 2009. pp. 1044-1049.
[11] Oomen T, Bosgra O. System identification for achieving robust performance. Automatica 2012; 48 (9): 1975-1987 doi:10.1016/j.automatica.2012.06.011
[12] Schrama RJP. Accurate identification for control: The necessity of an iterative scheme. IEEE Transactions on Automatic Control 1992; 37 (7): 991-994 doi:10.1109/9.148355
[13] Zhou K, Doyle JC, Glover K. Robust and optimal control. New Jersey, USA: Prentice Hall, 1996.
[14] van Herpen R, Oomen T, Bosgra O. Numerically reliable frequency-domain estimation of transfer functions: A computationally efficient methodology. In: 16th IFAC Symposium on System Identification; Brussels, Belgium; 2012. pp. 595-600.
[15] McFarlane D, Glover K. A loop-shaping design procedure using H∞ synthesis. IEEE Transactions on Automatic Control 1992; 37 (6): 759-769 doi:10.1109/9.256330
[16] Steinbuch M, Norg ML. Advanced motion control: An industrial perspective. European Journal of Control 1998; 4 (4): 278-293 doi:10.1016/S0947-3580(98)70121-9
[17] Oomen T, Quist S, van Herpen R, Bosgra O. Identification and visualization of robust-control-relevant model sets with application to an industrial wafer stage. In:49th IEEE Conference on Decision and Control; Atlanta, GA, USA; 2010. pp. 5530-5535