Xiao-Heng Chang

Fuzzy Generalized H₂ Filtering for Nonlinear Discrete-Time Systems With Measurement Quantization

In this paper, the generalized

Robust energy-to-peak filtering for discrete-time nonlinear systems with measurement quantization

{\mathcal {H}_{2}}

LMI approaches to input and output quantized feedback stabilization of linear systems

filter design problems are addressed for a class of nonlinear discrete-time systems with measurement quantization. The considered nonlinear system is represented by Takagi–Sugeno fuzzy model and the system measurement output is quantized by a dynamic quantizer constituted by a static quantizer and a dynamic parameter before it is transmitted to the filter. The attention is focused on the design of both full- and reduced-order filters and the quantizer dynamic parameter such that the quantized filtering error systems are asymptotically stable with prescribed generalized

Generalized nonlinear resilient H ∞ filter design for discrete-time nonlinear systems

{\mathcal {H}_{2}}

H ∞ control design for systems with input and output imprecision

performances. Superior to existing results on the quantized filtering design, the proposed one is given under a unified linear matrix inequality (LMI) characterization, it is shown that the design problem can be solved if the LMIs conditions are feasible. Finally, simulation examples will be exploited to illustrate the effectiveness of the developed quantized generalized

Resilient H ∞ filtering for discrete-time systems

{\mathcal {H}_{2}}

Fuzzy robust dynamic output feedback control of nonlinear systems with linear fractional parametric uncertainties

filtering methods.

Quantized Static Output Feedback Control For Discrete-Time Systems

This paper studied the problem of robust energy-to-peak filtering for a class of uncertain discrete-time nonlinear systems with measurement quantization. To the best of authors knowledge, the problem of designing robust energy-to-peak filters for uncertain discrete-time nonlinear systems with measurement quantization is not addressed in literature. Thus, the problem investigated in this paper is novel.A two-step approach is developed to deal with the products of system uncertainties and quantization error and derive LMIs-based sufficient conditions for designing an energy-to-peak filter. The designed filter can mitigate quantization effects and ensure the filtering error system is asymptotically stable with a prescribed energy-to-peak noise attenuation level.A numerical example is presented to demonstrate the effectiveness of the proposed design method. This paper investigates the problem of robust energy-to-peak filtering for a class of discrete-time systems with norm-bounded uncertain parameters, measurement quantization and Lipschitz nonlinearity. Assume that the system measurement output is quantized by a static, memoryless and logarithmic quantizer before it being transmitted to the filter, while the quantization errors can be treated as sector-bound uncertainties. Attention is focused on the design of a robust energy-to-peak filter to mitigate quantization effects and ensure the filtering error system is asymptotically stable with a prescribed energy-to-peak noise attenuation level. Sufficient conditions for the existence of such a energy-to-peak filter are expressed in terms of linear matrix inequalities (LMIs). A numerical example is presented to demonstrate the effectiveness of the proposed design method.

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

This paper investigates the problem of quantized feedback stabilization for linear systems. In the controlled systems, the measurement output and control input signals are transmitted via the digital communication link, and the quantization errors are treated as sector bound uncertainties. Two different approaches to designing output feedback are developed and the corresponding design conditions in terms of solutions to linear matrix inequalities (LMIs) are presented. The resulting design is such that the homologous closed-loop system is asymptotically stable with respect to the quantization effects. Finally, we illustrate the efficiency of our main results by a numerical example.

Robust observer-based H ∞ control for networked control systems with measurement quantization and packet dropouts

This paper investigates the generalized nonlinear resilient H∞ filter design problem for discrete-time systems with uncertain and nonlinear parameters. The uncertain parameters are assumed to have norm-bounded form and the nonlinear parameters are assumed to have Lipschitz form. We aim to design a generalized nonlinear resilient H∞ filter such that the stability and the H∞ performance of filtering error system can be achieved for all admissible uncertainties. It is shown that the generalized nonlinear resilient filter design problem is solvable if the linear matrix inequalities (LMIs) are feasible. In the end, a discrete-time system with uncertain and nonlinear parameters is presented to illustrate the effectiveness of the developed generalized nonlinear resilient filter design method.