Sparse Command Generator for Remote Control

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M. Nagahara, D. E. Quevedo, J. Ostergaard, T. Matsuda, and K. Hayashi, Sparse Command Generator for Remote Control, The 9th IEEE International Conference on Control & Automation (ICCA), pp. 1055-1059, Dec., 2011.

Tecnología Empresariales

Sparse Command Generator for
Remote Control

Masaaki Nagahara (Kyoto Univ.)
Daniel E. Quevedo (The Univ. of Newcastle)
Jan Østergaard (Aalborg Univ.)
Takahiro Matsuda (Osaka Univ.)
Kazunori Hayashi (Kyoto Univ.)

Remote Control System

Comamnd
Generator
Robot

In remote control (RC), one
has to transmit control
commands through rate-
limited networks such as
wireless networks.

Remote Control in Sparse Land

Comamnd
Generator
Robot

For rate-limited networks,
control commands should be
compressed.
Sparse Representation can
effectively compress control
commands without much
distortion.

Remote Control Systems
Target points

Radio Control Helicopter

Remote Control Systems

Regularization parameter for tradeoff between
tracking error and control energy

Tracking error on the sampling instants Energy limitation

Solution to Energy-limiting control
The optimal control is given by

[S. Sun et al., IEEE TAC, 2000]

Reference vector Transmitted vector Control input Output

D/A conversion Plant
Actuator

• The solution of

can be effectively obtained via a fast algorithm.

[Beck-Teboulle, SIAM J. Imag. Sci., 2009]
[Zibulevsky-Elad, IEEE SP Mag., 2010]

Quantizing control vectors
We quantize the vectors by a uniform quantizer to encode them.

Conclusion

Thank you for your attention!

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Sparse Command Generator for Remote Control

1. Sparse Command Generator for Remote Control Masaaki Nagahara (Kyoto Univ.) Daniel E. Quevedo (The Univ. of Newcastle) Jan Østergaard (Aalborg Univ.) Takahiro Matsuda (Osaka Univ.) Kazunori Hayashi (Kyoto Univ.)

2. Remote Control System Comamnd Generator Robot In remote control (RC), one has to transmit control commands through rate- limited networks such as wireless networks.

3. Remote Control in Sparse Land Comamnd Generator Robot For rate-limited networks, control commands should be compressed. Sparse Representation can effectively compress control commands without much distortion.

4. Table of Contents

5. Table of Contents

6. Remote Control Systems Target points Radio Control Helicopter

7. Remote Control Systems Regularization parameter for tradeoff between tracking error and control energy Tracking error on the sampling instants Energy limitation

8. Solution to Energy-limiting control The optimal control is given by [S. Sun et al., IEEE TAC, 2000]

9. Reference vector Transmitted vector Control input Output D/A conversion Plant Actuator

10. Table of Contents

11. Sparsity-Promoting Optimization

12. Sparsity-Promoting Optimization

13. Feasible set

14. Feasible set Sparse!

15. Feasible set Not sparse

16. • The solution of can be effectively obtained via a fast algorithm. [Beck-Teboulle, SIAM J. Imag. Sci., 2009] [Zibulevsky-Elad, IEEE SP Mag., 2010]

17. • The solution of can be effectively obtained via a fast algorithm. [Beck-Teboulle, SIAM J. Imag. Sci., 2009] [Zibulevsky-Elad, IEEE SP Mag., 2010]

18. Proposed Remote Control Fast Algorithm

19. A simple way to send a sparse vector

20. Table of Contents

21. Examples

22. Vectors to be sent

23.

24.

25. Tracking error

26. Quantizing control vectors We quantize the vectors by a uniform quantizer to encode them.

27. Tracking error with quantization

28. Conclusion Thank you for your attention!