On numerical optimization design of continuous-time feedback type quantizer for networked control systems

This paper focuses on analysis and synthesis methods of continuous-time dynamic quantizers for networked control systems. Our aim is to propose a numerical optimization design method of multiple (decentralized) quantizers such that a given linear system is optimally approximated by the given linear system with the multiple quantizers. Our method is based on the invariant set analysis and the LMI technique. Also, we clarify that our proposed method naturally extends to multiobjective control problems. Finally, it is pointed out that the proposed method is helpful through a numerical example.

[1]  Daisuke Yashiro,et al.  Optimal dynamic quantizer based acceleration control with narrow bandwidth , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[2]  Panos J. Antsaklis,et al.  Special Issue on Technology of Networked Control Systems , 2007 .

[3]  C. Scherer,et al.  Multiobjective output-feedback control via LMI optimization , 1997, IEEE Trans. Autom. Control..

[4]  Akira Matsuzawa,et al.  A Fifth-Order Continuous-Time Delta-Sigma Modulator With Single-Opamp Resonator , 2010, IEEE Journal of Solid-State Circuits.

[5]  E. Yaz Linear Matrix Inequalities In System And Control Theory , 1998, Proceedings of the IEEE.

[6]  Shun-ichi Azuma,et al.  Optimal dynamic quantizers for discrete-valued input control , 2008, Autom..

[7]  Shun-ichi Azuma,et al.  Stability analysis of quantized feedback systems including optimal dynamic quantizers , 2008, 2008 47th IEEE Conference on Decision and Control.

[8]  Yuki Minami,et al.  Dynamic quantizer design for MIMO systems based on communication rate constraint , 2011 .

[9]  G. Temes Delta-sigma data converters , 1994 .

[10]  Lihua Xie,et al.  The sector bound approach to quantized feedback control , 2005, IEEE Transactions on Automatic Control.

[11]  Seiichi Shin,et al.  Synthesis of continuous-time dynamic quantizers for LFT type quantized feedback systems , 2013, Artificial Life and Robotics.

[12]  Yuki Minami,et al.  Dynamic quantizer design for MIMO systems based on communication rate constraint , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[13]  Shun-ichi Azuma,et al.  Synthesis of Optimal Dynamic Quantizers for Discrete-Valued Input Control , 2008, IEEE Transactions on Automatic Control.

[14]  Seiichi Shin,et al.  Synthesis of continuous-time dynamic quantizers for quantized feedback systems , 2012, ADHS.

[15]  Stephen P. Boyd,et al.  Linear Matrix Inequalities in Systems and Control Theory , 1994 .

[16]  John A. Stankovic,et al.  When Sensor and Actuator Networks Cover the World , 2008 .

[17]  Wing Shing Wong,et al.  Systems with finite communication bandwidth constraints. II. Stabilization with limited information feedback , 1999, IEEE Trans. Autom. Control..

[18]  Sarah J Parsons,et al.  Guest Editors , 2012, Oncogene.

[19]  Shanthi Pavan,et al.  Fundamental Limitations of Continuous-Time Delta–Sigma Modulators Due to Clock Jitter , 2007, IEEE Transactions on Circuits and Systems I: Regular Papers.

[20]  Seiichi Shin,et al.  Synthesis of dynamic quantizers for quantized feedback systems within invariant set analysis framework , 2011, Proceedings of the 2011 American Control Conference.

[22]  Nicola Elia,et al.  Stabilization of linear systems with limited information , 2001, IEEE Trans. Autom. Control..