Quantitative feedback theory and zero phase error tracking control combined robust control for radar truck leveling simulator

Radar leveling system is the key equipment for improving the radar mobility and survival capability. A combined quantitative feedback theory (QFT) controller is designed for the radar truck leveling simulator in this paper, which suffers from strong nonlinearities and system parameter uncertainties. QFT can reduce the plant uncertainties and stabilize the system, but it fails to obtain high-precision tracking. This drawback can be solved by a robust QFT control scheme based on zero phase error tracking control (ZPETC) compensation. The combined controller not only possesses high robustness, but greatly improves the system performance. To verify the effectiveness and the potential of the proposed controller, a series of experiments have been carried out. Experimental results have demonstrated its robustness against a large range of parameters variation and high tracking precision performance, as well as its capability of restraining the load coupling among channels. The combined QFT controller can drive the radar truck leveling platform accurately, quickly and stably.

[1]  Jan Wikander,et al.  QFT control design for an approximately linearized pneumatic positioning system , 2003 .

[2]  Jan Swevers,et al.  H∞-controller design for a vibrations isolating platform , 1998 .

[3]  Maarten Steinbuch,et al.  On QFT tuning of multivariable µ controllers , 2000, Autom..

[4]  Muhammad H. Rashid,et al.  Robust high speed and high precision linear motor direct-drive XY-table motion system , 2004 .

[5]  M. Karpenko,et al.  QFT synthesis of a position controller for a pneumatic actuator in the presence of worst-case persistent disturbances , 2006, 2006 American Control Conference.

[6]  Indraneel Sen,et al.  A QFT-based robust SVC controller for improving the dynamic stability of power systems , 1998 .

[7]  Paluri S. V. Nataraj,et al.  Computation of QFT bounds for robust tracking specifications , 2002, Autom..

[8]  Keding Zhao,et al.  An integral variable structure controller with fuzzy tuning design for electro-hydraulic driving Stewart platform , 2006, 2006 1st International Symposium on Systems and Control in Aerospace and Astronautics.

[9]  Nariman Sepehri,et al.  Design and experimental evaluation of a robust force controller for an electro-hydraulic actuator via quantitative feedback theory , 2000 .

[10]  Zhu Xin-jian,et al.  Performance analysis and fuzzy neural networks modeling of direct methanol fuel cell , 2007 .

[11]  Chyi Hwang,et al.  QFT template generation for time-delay plants based on zero-inclusion test , 2002, Syst. Control. Lett..

[12]  Wen-Hua Chen,et al.  Genetic algorithm enabled computer-automated design of QFT control systems , 1999, Proceedings of the 1999 IEEE International Symposium on Computer Aided Control System Design (Cat. No.99TH8404).

[13]  Yoonsu Nam.  QFT force loop design for the aerodynamic load simulator , 2001 .

[14]  I. Horowitz.  Invited paper Survey of quantitative feedback theory (QFT) , 1991 .

[15]  I. Horowitz.  Survey of quantitative feedback theory (QFT) , 2001 .

[16]  Caro Lucas,et al.  Optimal design of robust quantitative feedback controllers using random optimization techniques , 2000, Int. J. Syst. Sci..

[17]  Pyung Hun Chang,et al.  Trajectory planning for the tracking control of systems with unstable zeros , 2003 .