P‐type iterative learning control for systems that contain resonance

The effect of iterative learning control (ILC) on a plant containing resonance has been experimentally investigated using an industrial gantry robot. The plant is controlled by a PID controller and a P-type learning controller in a parallel configuration. It is demonstrated that the learning controller can cause the amplitude of resonant frequencies to grow at each trial, leading to instability. The reasons for this growth are analysed both theoretically and experimentally. In order to solve the problem with a resonance, a straightforward aliasing technique is developed which is experimentally shown to suppress the increase of resonant amplitudes. The novel aliasing technique is compared with three filtering methods, the aliasing technique giving the best experimental performance. Finally, it is found that additional velocity feedback combined with the aliasing technique can further help to limit the effect of residual resonance. Due to the straightforward implementation of these algorithms, they should be highly applicable to industrial systems.

[1]  Suguru Arimoto,et al.  Bettering operation of Robots by learning , 1984, J. Field Robotics.

[2]  Zhiqiang Gao,et al.  Cures for low-frequency mechanical resonance in industrial servo systems , 2001, Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248).

[3]  Robert D. Lorenz,et al.  Resonant load control methods for industrial servo drives , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).

[4]  K. Moore,et al.  An algebraic approach to iterative learning control , 2004 .

[5]  K. Moore,et al.  Editorial: Special issue on iterative learning control , 2000 .

[6]  Paul Lewin,et al.  Practical implementation of a real-time iterative learning position controller , 2000 .

[7]  J. Hätönen.  Issues of algebra and optimality in Iterative Learning Control , 2004 .

[8]  F. Miyazaki,et al.  Applications of learning method for dynamic control of robot manipulators , 1985, 1985 24th IEEE Conference on Decision and Control.

[9]  D. Owens,et al.  Parameter optimization in iterative learning control , 2003 .

[10]  Richard W. Longman,et al.  Simple learning control made practical by zero-phase filtering: applications to robotics , 2002 .

[11]  E. Rogers,et al.  Iterative learning control using optimal feedback and feedforward actions , 1996 .

[12]  Zhihua Qu,et al.  Robust learning control for robotic manipulators with an extension to a class of non-linear systems , 2000 .

[13]  P. Vaidyanathan.  Generalizations of the sampling theorem: Seven decades after Nyquist , 2001 .

[14]  C.E. Shannon,et al.  Communication in the Presence of Noise , 1949, Proceedings of the IRE.

[15]  E. Rogers,et al.  Predictive optimal iterative learning control , 1998 .

[16]  M. Unser.  Sampling-50 years after Shannon , 2000, Proceedings of the IEEE.

[17]  F. Miyazaki,et al.  Bettering operation of dynamic systems by learning: A new control theory for servomechanism or mechatronics systems , 1984, The 23rd IEEE Conference on Decision and Control.

[18]  Jian-Xin Xu,et al.  Adaptive robust iterative learning control with dead zone scheme , 2000, Autom..

[19]  Richard W. Longman,et al.  Iterative learning control and repetitive control for engineering practice , 2000 .