This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

Recently, to ensure the reliability and safety of modern large-scale industrial processes, data-driven methods have been receiving considerably increasing attention, particularly for the purpose of process monitoring. However, great challenges are also met under different real operating conditions by using the basic data-driven methods. In this paper, widely applied data-driven methodologies suggested in the literature for process monitoring and fault diagnosis are surveyed from the application point of view. The major task of this paper is to sketch a basic data-driven design framework with necessary modifications under various industrial operating conditions, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.

A Review on Basic Data-Driven Approaches for Industrial Process Monitoring

This paper is concerned with the state estimation and sliding-mode control problems for continuous-time Markovian jump singular systems with unmeasured states. Firstly, a new necessary and sufficient condition is proposed in terms of strict linear matrix inequality (LMI), which guarantees the stochastic admissibility of the unforced Markovian jump singular system. Then, the sliding-mode control problem is considered by designing an integral sliding surface function. An observer is designed to estimate the system states, and a sliding-mode control scheme is synthesized for the reaching motion based on the state estimates. It is shown that the sliding mode in the estimation space can be attained in a finite time. Some conditions for the stochastic admissibility of the overall closed-loop system are derived. Finally, a numerical example is provided to illustrate the effectiveness of the proposed theory.

State Estimation and Sliding-Mode Control of Markovian Jump Singular Systems

Asynchronous l 2 - l ∞ filtering for discrete-time stochastic Markov jump systems with randomly occurred sensor nonlinearities

This paper is focused on reliable fuzzy H∞ controller design for active suspension systems with actuator delay and fault. The Takagi-Sugeno (T-S) fuzzy model approach is adapted in this study with the consideration of the sprung and the unsprung mass variation, the actuator delay and fault, and other suspension performances. By the utilization of the parallel-distributed compensation scheme, a reliable fuzzy H∞ performance analysis criterion is derived for the proposed T-S fuzzy model. Then, a reliable fuzzy H∞ controller is designed such that the resulting T-S fuzzy system is reliable in the sense that it is asymptotically stable and has the prescribed H∞ performance under given constraints. The existence condition of the reliable fuzzy H∞ controller is obtained in terms of linear matrix inequalities (LMIs) Finally, a quarter- vehicle suspension model is used to demonstrate the effectiveness and potential of the proposed design techniques.

/pdf/reliable-fuzzy-control-for-active-suspension-systems-with-1uo5wm4d8y.pdf

Reliable Fuzzy Control for Active Suspension Systems With Actuator Delay and Fault

https://bura.brunel.ac.uk/bitstream/2438/4915/3/Fulltext.pdf

Brief paper: Robust filtering with stochastic nonlinearities and multiple missing measurements

In this paper, the H infin filtering problem is investigated for a general class of nonlinear discrete-time stochastic systems with missing measurements. The system under study is not only corrupted by state-dependent white noises but also disturbed by exogenous inputs. The measurement output contains randomly missing data that is modeled by a Bernoulli distributed white sequence with a known conditional probability. A filter of very general form is first designed such that the filtering process is stochastically stable and the filtering error satisfies H infin performance constraint for all admissible missing observations and nonzero exogenous disturbances under the zero-initial condition. The existence conditions of the desired filter are described in terms of a second-order nonlinear inequality. Such an inequality can be decoupled into some auxiliary ones that can be solved independently by taking special form of the Lyapunov functionals. As a consequence, a linear time-invariant filter design problem is discussed for the benefit of practical applications, and some simplified conditions are obtained. Finally, two numerical simulation examples are given to illustrate the main results of this paper.

/pdf/on-nonlinear-h-infty-filtering-for-discrete-time-stochastic-tcd63gjwxw.pdf

On Nonlinear $H_{\infty }$ Filtering for Discrete-Time Stochastic Systems With Missing Measurements

https://bura.brunel.ac.uk/bitstream/2438/4912/3/Fulltext.pdf

Brief paper: H∞ filtering for nonlinear discrete-time stochastic systems with randomly varying sensor delays

Robust H∞ finite-horizon filtering with randomly occurred nonlinearities and quantization effects

https://bura.brunel.ac.uk/bitstream/2438/4706/3/Fulltext.pdf

Guoliang Wei

Papers

Brief paper: Robust filtering with stochastic nonlinearities and multiple missing measurements

On Nonlinear $H_{\infty }$ Filtering for Discrete-Time Stochastic Systems With Missing Measurements

Brief paper: H∞ filtering for nonlinear discrete-time stochastic systems with randomly varying sensor delays

Robust H∞ finite-horizon filtering with randomly occurred nonlinearities and quantization effects

Brief paper: A note on control of a class of discrete-time stochastic systems with distributed delays and nonlinear disturbances