Robust Fuzzy-Model-Based Filtering for Nonlinear Cyber-Physical Systems With Multiple Stochastic Incomplete Measurements

Abstract

This paper is concerned with the state estimation problem for a class of nonlinear cyber-physical systems (CPSs) where the nonlinear dynamical physical process is approximated by a Takagi-Sugeno fuzzy model. The physical plant is measured by a set of wireless sensors and the sensors communicate with the remote estimator via a communication channel. In the considered CPS, the randomly occurring sensor saturation, signal quantization, packet dropouts as well as the medium access constraint are studied in a unified framework. We develop a sufficient condition such that the filtering error system is asymptotically stable in the mean-square sense and also with a prescribed H ∞ performance level. The filter gain parameters are determined by solving a convex optimization problem. Finally, the simulation study on the networked truck-trailer system is presented to show the effectiveness of the proposed estimator design. Index Terms-Communication constraint, cyber-physical systems (CPSs), H ∞ filtering, incomplete measurement, state estimation, Takagi-Sugeno (T-S) systems.

Sliding mode stabilisation of networked systems with consecutive data packet dropouts using only accessible information

“…Now for obtaining Π > 0 and̂utilised in (35), we need to check the feasibility of (39). Given > 0, > 0 and > 0, we then use the Schur complement and Claim 1 in [22] to illustrate that the inequality in (39) is sufficed by the LMI in (36).…”

“…On the other hand, the LMI (36) is derived for the purposes of boundedness characterisation of the system state trajectories as well as the switching function, and has no effect on the achieved controller parameters, and further, the boundary layer thickness. Indeed, the solution of the LMI (36) gives only an upper bound on ( ). Moreover, if the LMI in (17) is feasible, the LMI in (36) will definitely be feasible and the upper bound presented in (35) exists.…”

“…Moreover, if the LMI in (17) is feasible, the LMI in (36) will definitely be feasible and the upper bound presented in (35) exists. Finally, it is worth noting that one may solve the LMIs in (17) and (36) subject to a specific criteria in order to achieve a more accurate upper bound.…”

“…Additionally, [35] and [34] give a unified model for dealing with several phenomena in NCSs such as the nonuniform sampling, the measurement size reduction, the transmission rate reduction, the signal quantization, and the measurement missing. A unified framework is presented in [36] for cyber-physical systems (CPSs) to cope with the randomly occurring sensor saturation, signal quantization, packet dropouts along with the medium access constraint.…”

Estimator-Based Control for Pure-Feedback Systems With Incomplete Measurements

“…Nonlinearity is an important problem in CPS control, which is more general in the real physical world than the linear description [19]- [22]. In [23], based on the T-S fuzzy model, a state estimator with stochastic incomplete measurements of nonlinear CPSs was designed. [7] concerned with nonlinear CPSs subject to intermittent denial of service attacks.…”