Disturbance Rejection with Communication Constraints

Shingin, Hidenori; Ohta, Yuichi

doi:10.9746/ve.sicetr1965.43.806

Cited by 1 publication

(2 citation statements)

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“…The limitations need to be represented as the trade-off relations between channel capacity and control performances, while the limitation in stabilization is the minimum channel capacity. Results in this direction include limitations in disturbance rejection for scalar systems [8], [14], [18], their bounds for multidimensional systems [3], [5], [8], and limitations in terms of the terminal cost [15].…”

Section: Introductionmentioning

confidence: 99%

“…The SNR limitation for Gaussian channels given in [13] is shown to be valid for nonlinear controllers by [14] using entropy. Our approach based on generalized entropy [18], [19] provides an unified method for deriving the limitations under bounded and Gaussian disturbances. As information measures adapted to dynamical systems, directed information [3], [4], [11], [16] and non-probabilistic information [9] are also effectively employed for limitation analyses.…”

Section: Introductionmentioning

confidence: 99%

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SNR limitation for feedback control systems with amplitude-bounded noise

Shingin

Ohta

2014

2014 IEEE International Symposium on Intelligent Control (ISIC)

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This paper presents an SNR limitation in control under amplitude-bounded noise. The SNR of feedback link in control system is evaluated with respect to amplitude and minimized over all causal control laws. The minimum SNR is represented by the absolute value of the product of unstable poles of the plant. This index is known to describe performance limitations in various control problems. The optimal control law minimizing the SNR is simply constructed as a combination of scaling factor and the state feedback gain for minimum energy control. This control law overcomes the drawback of computational complexity of the encoding scheme minimizing bit-rate, which is employed to minimize an amplitude-base SNR in our preceding work. This simplification is accomplished by relaxing the requirement of stability to the convergence of the state to near the origin and introducing a new criterion for evaluating SNR.

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