Stabilization Under Signal-to-Noise Ratio Constraints on Feedback Links with Bounded Noise

Shingin, Hidenori; Ohta, Yuichi

doi:10.3182/20130925-2-de-4044.00058

Cited by 3 publications

(3 citation statements)

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“…The optimal encoder constructed in the proof of Theorem 2 is a combination of gain and scaling factors and so requires simple computations as in the case of Gaussian noise [13], [15]. In our preceding work [20], we found that the encoder in [3], [4] which minimizes bit rate also minimizes an SNR for bounded noise. However, this encoder suffers from computational complexity due to its time-varying partition of the state space for sequential quantization.…”

Section: G(r)mentioning

confidence: 96%

“…The uncertainty for a received data is evaluated as the volume of the set of all possible source data, and its worst-case value is lower bounded. The lemma is given from more general point of view than the elementary version given in our preceding work [20]. This will be used for evaluating uncertainty of the state in estimation.…”

Section: Technical Lemmasmentioning

confidence: 99%

“…• Stability in the worst-case scenario is required for safe operation of mechanical systems controlled via communication networks involving many uncertain factors. We have shown the minimum of an amplitude-based SNR in [20]. However, the optimal control law is the one for minimizing bit rate [3], [4], which suffers from computational complexity of the encoding scheme due to its time-varying partition of the state space.…”

Section: Introductionmentioning

confidence: 97%

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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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