Machine-In-The-Loop control optimization

Control design is an important issue in the development of high-precision motion systems. To meet the increasing performance requirements, it is often necessary to extend the traditional two-degree-of-freedom control structure with additional control-related functionality, such as filters and tables. With this increasing flexibility and complexity of the control system, it is necessary to develop tuning methods for an increasing number of parameters. The current methods can be divided into manual control design and off-line model-based control design, which incorporate several disadvantages. We believe that these shortcomings can be overcome by Machine-In-the-Loop (MIL) control optimization.

[1]  Håkan Hjalmarsson,et al.  Iterative feedback tuning—an overview , 2002 .

[2]  A.H. Haddad,et al.  Applied optimal estimation , 1976, Proceedings of the IEEE.

[3]  Tae Tom Oomen,et al.  The RRR-robot feedback control design via iterative feedback tuning , 2003 .

[4]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[5]  B. G. Dijkstra.  Iterative learning control, with applications to a wafer-stage , 2004 .

[6]  Kevin L. Moore,et al.  Iterative Learning Control: An Expository Overview , 1999 .

[7]  Svante Gunnarsson,et al.  Iterative feedback tuning: theory and applications , 1998 .

[8]  S. Gunnarsson,et al.  A convergent iterative restricted complexity control design scheme , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[9]  R. Tousain,et al.  Design strategy for iterative learning control based on optimal control , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

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

[11]  Dragan Kostic,et al.  Data-driven robot motion control design , 2004 .

[12]  Karl Johan Åström,et al.  Computer-Controlled Systems: Theory and Design , 1984 .

[13]  Bram De Kraker.  A Numerical - Experimental Approach in Structural Dynamics , 2004 .

[14]  R.V. Monopoli,et al.  Adaptive control: The model reference approach , 1981, Proceedings of the IEEE.

[15]  Michael T. Heath,et al.  Scientific Computing: An Introductory Survey , 1996 .

[16]  Karl Johan Åström.  Design Methods of Control Systems , 1991 .

[17]  T. Hughes,et al.  Signals and systems , 2006, Genome Biology.

[18]  Bruce A. Francis,et al.  The internal model principle of control theory , 1976, Autom..

[19]  S. Sastry.  Nonlinear Systems: Analysis, Stability, and Control , 1999 .

[20]  David G. Luenberger,et al.  Linear and nonlinear programming , 1984 .

[21]  Bruce A. Francis,et al.  Feedback Control Theory , 1992 .

[22]  Huibert Kwakernaak,et al.  Modern signals and systems , 1991 .