Fuzzy Peak-to-Peak Filtering for Networked Nonlinear Systems With Multipath Data Packet Dropouts

Chang, Xiao‐Heng; Liu, Qi; Wang, Yiming; Jun, Xiong

doi:10.1109/tfuzz.2018.2859903

Cited by 101 publications

(45 citation statements)

References 54 publications

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“…Besides, the neural network model is also applied in the fuzzy system that are related with time-varying parameter matrix. For example, in [24], [25], it is concerned with nonfragile H ∞ filtering of continuous-time and fuzzy peak-to-peak filtering with multipath data packet dropouts in fuzzy systems, which are based on dynamic matrix inversion.…”

Section: Introductionmentioning

confidence: 99%

A Fully Complex-Valued and Robust ZNN Model for Dynamic Complex Matrix Inversion Under External Noises

2020

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Dynamic complex-valued matrix inversion is often used in the field of mathematics and engineering. Over the past years, many recurrent neural network models have been designed and researched to analyse the process of matrix inversion without noises interference. However, there are many types of uncertain noises in actual model design and analysis. In this article, a novel fully complex-valued and robust Zeroing neural network (CVRZNN) is firstly proposed for calculating the dynamic complex matrix inversion under the interference of external noise environment, and its robustness is analysed and demonstrated in the presence of various types of external noises. Compared with the previous zeroing neural network (ZNN) and the gradient neural network (GNN) for dynamic complex matrix inversion, this novel CVRZNN model has good robustness under three kinds of external noises. Besides, the theoretical analysis shows that the CVRZNN model can globally converge to zero under constant noise. Through comparative simulation results, the excellent performance of the proposed CVRZNN model is obviously demonstrated, which is much better than that of the previous GNN and ZNN models. INDEX TERMS Zeroing neural network (ZNN), gradient neural network, dynamic complex-valued matrix inversion, robustness, external noise.

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Section: Introductionmentioning

confidence: 99%

A Fully Complex-Valued and Robust ZNN Model for Dynamic Complex Matrix Inversion Under External Noises

2020

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“…On the other hand, the derived results in this work make it interesting to consider the extension of this framework to closed-loop networked control systems where the objective is to identify the performance of control under communication constraints, see for instance [5], [8], [36]. Due to the nonlinear and uncertain characteristics of most of the practical networked systems, see e.g., [6], [7], [37], the design and performance analysis of the coding-estimation and control systems in such cases will be also under consideration in future. Advancements in these relevant researches will be valuable for various applications of networked systems.…”

Section: Discussionmentioning

confidence: 96%

“…Generally, due to bandwidth constraint of the wireless channel, analog measurements made by sensors need to be quantized and encoded before transmission over the communication channel. In addition, when the state estimation or filtering is to be part of the distributed processing or networked control systems, see for instance [7], [8], the quantization and communication constraints will play critical roles on the performance and stability of the systems. In particular, the possible time delay in processing of source coding and estimation is also an important aspect that should be carefully considered.…”

Section: Introductionmentioning

confidence: 99%

Real Time Coding and Estimation of Linear Discrete Time System Over Networks

Chen

Zhou

2020

IEEE Access

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This study deals with the real time coding and estimation of linear discrete time scalar system over communication networks. With the mean-squared error (MSE) distortion criterion, the information rate distortion function describing the performance limit of the coding-estimation system is analyzed and discussed. To achieve near instantaneous encoding and decoding, an asymptotic design scheme has been presented as a realization of real time coding-estimation system. The outputs of standard Kalman filter relying on unquantized observations are encoded using the Lloyd-Max quantization rules and transmitted over the noiseless channel. The decoder side runs the corresponding decoding and reconstruction algorithms to produce the optimal real time state estimate of system. To synchronize the encoder and decoder, a doublepredictor regime on the updating rules of the encoder-decoder pair is proposed, and it does not require any feedback information. The rate distortion function of the proposed scheme is derived and when comparing with the information theoretical lower bound, there is only a factor discrepancy related to the quantization rules. The rate distortion performance results of various design schemes are compared and demonstrated with numerical simulations. INDEX TERMS Real time coding and estimation, mean-squared error, information rate distortion function, Lloyd-Max quantization, double-predictor regime.

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“…Recent examples of such techniques include [6], [7], [30], [31], [41]. The paper [30] represents the nonlinear plant as a Takagi-Sugeno fuzzy system and designs a static output feedback tracking controller to achieve dissipative tracking performance subject to quantization effects.…”

Section: Introductionmentioning

confidence: 99%

“…The paper [30] represents the nonlinear plant as a Takagi-Sugeno fuzzy system and designs a static output feedback tracking controller to achieve dissipative tracking performance subject to quantization effects. The paper [6] assumes a similar representation of the nonlinear plant and studies networked nonlinear systems with multipath data packet dropouts. State estimation of Markovian coupled networks with nonlinear dynamics is studied in [41].…”

Section: Introductionmentioning

confidence: 99%

Automated Synthesis of Safe Digital Controllers for Sampled-Data Stochastic Nonlinear Systems

et al. 2020

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We present a new method for the automated synthesis of digital controllers with formal safety guarantees for systems with nonlinear dynamics, noisy output measurements, and stochastic disturbances. Our method derives digital controllers such that the corresponding closed-loop system, modeled as a sampled-data stochastic control system, satisfies a safety specification with probability above a given threshold. Our technique uses a fast solver and an optimization method to search for candidate controllers, which are then formally evaluated in closed-loop with the system in question by a verified solver. Unstable candidate controllers are discarded by efficiently checking a sufficient condition for Lyapunov stability of sampled-data nonlinear systems. We evaluate our technique on three case studies: an artificial pancreas model, a powertrain control model, and a quadruple-tank process.

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Fuzzy Peak-to-Peak Filtering for Networked Nonlinear Systems With Multipath Data Packet Dropouts

Cited by 101 publications

References 54 publications

A Fully Complex-Valued and Robust ZNN Model for Dynamic Complex Matrix Inversion Under External Noises

A Fully Complex-Valued and Robust ZNN Model for Dynamic Complex Matrix Inversion Under External Noises

Real Time Coding and Estimation of Linear Discrete Time System Over Networks

Automated Synthesis of Safe Digital Controllers for Sampled-Data Stochastic Nonlinear Systems

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