Min-Max Design of Feedback Quantizers for Netorwked Control Systems

In a networked control system, quantization is inevitable to transmit control and measurement signals. While uniform quantizers are often used in practical systems, the overloading, which is due to the limitation on the number of bits in the quantizer, may significantly degrade the control performance. In this paper, we design an overloading-free feedback quantizer based on a Delta-Sigma modulator,composed of an error feedback filter and a static quantizer. To guarantee no-overloading in the quantizer, we impose an $l_{\infty}$ norm constraint on the feedback signal in the quantizer. Then, for a prescribed $l_{\infty}$ norm constraint on the error at the system output induced by the quantizer, we design the error feedback filter that requires the minimum number of bits that achieves the constraint. Next, for a fixed number of bits for the quantizer, we investigate the achievable minimum $l_{\infty}$ norm of the error at the system output with an overloading-free quantizer. Numerical examples are provided to validate our analysis and synthesis.

[1]  Sekhar Tatikonda,et al.  Control under communication constraints , 2004, IEEE Transactions on Automatic Control.

[2]  Yoshito Ohta,et al.  Optimal Invariant Sets for Discrete-time Systems: Approximation of Reachable Sets for Bounded Inputs , 2004 .

[3]  D. Bernstein,et al.  Induced convolution operator norms for discrete-time linear systems , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[4]  Tran-Thong,et al.  Error spectrum shaping in narrow-band recursive filters , 1977 .

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

[6]  Shuichi Ohno,et al.  Min-max IIR filter design for feedback quantizers , 2017, 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC).

[7]  Huijun Gao,et al.  Design of delta-sigma modulators via generalized Kalman-Yakubovich-Popov lemma , 2014, Autom..

[8]  Gabor C. Temes,et al.  Understanding Delta-Sigma Data Converters , 2004 .

[9]  Timo I. Laakso,et al.  Noise reduction in recursive digital filters using high-order error feedback , 1992, IEEE Trans. Signal Process..

[10]  Shuichi Ohno,et al.  Optimization of Noise Shaping Filter for Quantizer With Error Feedback , 2017, IEEE Transactions on Circuits and Systems I: Regular Papers.

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

[12]  Izumi Masubuchi,et al.  LMI-based controller synthesis: A unified formulation and solution , 1998 .

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

[14]  Graham C. Goodwin,et al.  On Optimal Perfect Reconstruction Feedback Quantizers , 2008, IEEE Transactions on Signal Processing.

[15]  Sergio Callegari,et al.  Output Filter Aware Optimization of the Noise Shaping Properties of ΔΣ Modulators Via Semi-Definite Programming , 2013, IEEE Transactions on Circuits and Systems I: Regular Papers.

[16]  A.G. Alleyne,et al.  Closed-loop control over wireless networks , 2004, IEEE Control Systems.

[17]  Sergio Callegari,et al.  Noise Weighting in the Design of $\Delta\Sigma$ Modulators (With a Psychoacoustic Coder as an Example) , 2013, IEEE Transactions on Circuits and Systems II: Express Briefs.

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

[20]  David C. Munson,et al.  Noise reduction strategies for digital filters: Error spectrum shaping versus the optimal linear state-space formulation , 1982 .

[21]  Yutaka Yamamoto,et al.  Frequency Domain Min-Max Optimization of Noise-Shaping Delta-Sigma Modulators , 2012, IEEE Transactions on Signal Processing.