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A sector bound approach is proposed in [6] to characterize the quantization error caused by a logarithmic quantizer, by which many quantized problem can be solved by the robust tools.
The measurement signal is quantized by a logarithmic quantizer.
The transmitted measurements triggered according to prespecified events are quantized by a logarithmic quantizer.
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A logarithmic quantizer is employed whose resulting quantization error can be regarded as a multiplicative noise.
We consider the use of a logarithmic quantizer, which is motivated by the fact that the resulting quantization error acts as a multiplicative noise, an important feature in many applications.
Conditions for mean-square poly-quadratical stability are obtained by introducing a new quantization-dependent Lyapunov function approach for linear state feedback with a logarithmic quantizer, which are shown to be less conservative than those derived by a common Lyapunov function.
Theorem 1 For the discrete-time stochastic system (1) and the quantized state feedback control law in the form of (6), given a logarithmic quantizer as in (2), the closed-loop system (8) is mean-square poly-quadratically stable if and only if there exist matrices Q 1 > 0, Q 2 > 0, V 1 and V 2 satisfying (12) (13).
And a logarithmic quantizer is introduced to overcome the channel capacity constraint.
It is pointed out that finite time practical consensus can be achieved when uniform quantizers are used, while consensus can be achieved under some conditions when a logarithmic quantizer is considered.
Secondly, a logarithmic quantizer is used to quantify the sampled data, which can reduce the data transmission rate in the network.
Given a logarithmic quantizer as in (2), the closed-loop system (6) is mean-square poly-quadratically stabilized if there exist matrices Q ¯ 1 > 0, Q ¯ 2 > 0, V and K satisfying (31).
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