Reliable Filter Design for Sensor Networks Using Type-2 Fuzzy Framework

Liu, Jianxing; Wu, Chengwei; Wang, Zhenhuan; Wu, Ligang

doi:10.1109/tii.2017.2654323

Cited by 167 publications

(75 citation statements)

References 37 publications

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“…Step 2: Analyze the parametric uncertainty J distribution and decompose the system matrices A i ðJÞ; B i ðJÞ as in equation (20).…”

Section: Design Proceduresmentioning

confidence: 99%

“…There also has been a rapid growing interest in the fuzzy control of the nonlinear HV systems. Especially, the control methods based on the Takagi-Sugeno (T-S) fuzzy model 14 have attracted lots of attention, [15][16][17][18][19][20] since it is conceptually simple and effective for controlling the nonlinear HV system along the trajectory. For example, Wu et al 16 proposed a mixed H 2 =H 1 fuzzy tracking control method for HVs to obtain a satisfactory anti-disturbance performance and quadratic optimal performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A probabilistic robust mixed H₂/H_∞ fuzzy control method for hypersonic vehicles based on reliability theory

Yin

Liu

et al. 2018

International Journal of Advanced Robotic Systems

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Achieving balance between robustness and performance is always a challenge in the hypersonic vehicle flight control design. In this research, we focus on dealing with uncertainties of the fuzzy control system from the viewpoint of reliability. A probabilistic robust mixed H 2 =H 1 fuzzy control method for hypersonic vehicles is presented by describing the uncertain parameters as random variables. First, a Takagi-Sugeno fuzzy model is employed for the hypersonic vehicle nonlinear dynamics characteristics. Next, a robust fuzzy controller is developed by solving a reliability-based multiobjective linear matrix inequality optimization problem, in which the H 2 =H 1 performance is optimized under the condition that the system is robustly reliable to uncertainties. By this method, the system performance and reliability can be taken into account simultaneously, which reduces the conservatism in the robust fuzzy control design. Finally, simulation results of a hypersonic vehicle demonstrate the feasibility and effectiveness of the presented method.

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“…Step 2: Analyze the parametric uncertainty J distribution and decompose the system matrices A i ðJÞ; B i ðJÞ as in equation (20).…”

Section: Design Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A probabilistic robust mixed H₂/H_∞ fuzzy control method for hypersonic vehicles based on reliability theory

Yin

Liu

et al. 2018

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…In the system life, faults inevitably occur in these elements, which leads to the drop of systems performances, or even worse to system damage, with dramatic consequences on the environment. Consequently, in parallel with the development of high-performance technological systems, industrials express a growing demand for reliability, maintainability, and survivability [2]. Fault Tolerant Control (FTC) is an effective way of maintaining system performances under faulty conditions.…”

Section: Introductionmentioning

confidence: 99%

Robust Fault Detection and Estimation in Nonlinear Systems with Unknown Constant Time‐Delays

Yan

Shen

2017

Mathematical Problems in Engineering

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This paper studies the problem of fault detection and estimation in nonlinear time-delayed systems with unknown inputs, where the time-delays are supposed to be constant but unknown. A new fault detection filter, which can estimate online the time-delays, is first introduced. Then, a reference residual model is proposed to formulate the robust fault detection filter design problem as an ∞ model-matching problem. Furthermore, by a novel robust adaptive fault estimation algorithm, the classical assumption that the time derivative of the output error should be known is removed. In addition, applying a robust ∞ optimization control technique, sufficient conditions for the existence of the fault detection filter (FDF) are derived in terms of linear matrix inequality (LMI). Finally, simulation results are presented to illustrate the effectiveness of the proposed algorithm.

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“…Thus to relax the stability analysis results, the information of lower and upper membership functions are utilized. So far, the concept of IT2 FMB control has been extended to various control stratety and systems [29][30][31][32][33][34][35][36] such as state and output feedback control [31,34], control of nonlinear networked systems [30], filter design [32,33,35] of the IT2 fuzzy system, control of time-varying delay system [34,36] etc. Yet, most of them focus on the control methodology and the stability analysis is approached by existing techniques.…”

Section: Introductionmentioning

confidence: 99%

Membership‐Function‐Dependent Stability Analysis of Interval Type‐2 Polynomial Fuzzy‐Model‐Base Control Systems

Song

Lam

Yang

2017

IET Control Theory & Applications

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Abstract:In this paper, the stability analysis for interval type-2 (IT2) polynomial fuzzy-model-based (PFMB) control system using the information of membership functions is investigated. In order to tackle uncertainties, IT2 membership functions are used in the IT2 polynomial fuzzy model and IT2 polynomial fuzzy controller. To improve the design flexibility and reduce the implementation costs, the IT2 polynomial fuzzy controller does not need to share the same premise membership functions nor the same number of rules with the IT2 polynomial fuzzy model. The stability of IT2 PFMB control system is investigated based on the Lyapunov stability theory and both sets of membership function independent (MFI) and membership function dependent (MFD) stability conditions are derived on the basis of the sum-of-squares (SOS) approach. To make the stability conditions membership function dependent, the boundary information of IT2 membership functions is used in the stability analysis. To extract richer information of IT2 membership functions, the operating domain is partitioned into sub-domains. In each sub-domain, the boundary information of IT2 membership functions and those of the upper and lower membership function are obtained. Furthermore, to further relax the conservativeness, a switching polynomial fuzzy controller, together with the informations obtained in each sub-domain, is employed in investigating the stability analysis. Numerical Examples and simulation results are given to demonstrate the validity of MFD and MFD switching methods.

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Reliable Filter Design for Sensor Networks Using Type-2 Fuzzy Framework

Cited by 167 publications

References 37 publications

A probabilistic robust mixed H₂/H_∞ fuzzy control method for hypersonic vehicles based on reliability theory

A probabilistic robust mixed H₂/H_∞ fuzzy control method for hypersonic vehicles based on reliability theory

Robust Fault Detection and Estimation in Nonlinear Systems with Unknown Constant Time‐Delays

Membership‐Function‐Dependent Stability Analysis of Interval Type‐2 Polynomial Fuzzy‐Model‐Base Control Systems

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Reliable Filter Design for Sensor Networks Using Type-2 Fuzzy Framework

Cited by 167 publications

References 37 publications

A probabilistic robust mixed H2/H∞ fuzzy control method for hypersonic vehicles based on reliability theory

A probabilistic robust mixed H2/H∞ fuzzy control method for hypersonic vehicles based on reliability theory

Robust Fault Detection and Estimation in Nonlinear Systems with Unknown Constant Time‐Delays

Membership‐Function‐Dependent Stability Analysis of Interval Type‐2 Polynomial Fuzzy‐Model‐Base Control Systems

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A probabilistic robust mixed H₂/H_∞ fuzzy control method for hypersonic vehicles based on reliability theory

A probabilistic robust mixed H₂/H_∞ fuzzy control method for hypersonic vehicles based on reliability theory