Nonlinear Model-Based Approach for Accurate Stability Prediction of One-Bit Higher-Order Delta–Sigma Modulators

Abstract

. Another approach to simplify the analysis has been to assume a DC input to the Δ-Σ Nonlinear model based approach for accurate stability prediction of one-bit higher-order deltasigma (Δ-Σ) modulators

Reliable Sliding Mode Control of Fast Sampling Singularly Perturbed Systems: A Redundant Channel Transmission Protocol Approach

“…The relationship in (14) can be obtained directly. Now, we prove the relationship (15). According the notations in (4)-(5), it yields E (MΥ(k)) T P (MΥ(k)) =E Υ T (k)M T P M Υ(k)) − 2E Υ T (k)M T P MῩ…”

“…One is the fast-sampling model [8], [18] and another is the slow-sampling model [31]. It is worth stressing that the stability of a circuit system is a key problem in practical applications [15], [16]. Therefore, it is valuable to study the control problem of the slow-or fastsampling circuit models under a single-rate sample-data.…”

Recursive Discrete-Time Models for Continuous-Time Systems Under Band-Limited Assumptions

“…Moreover, the goal of many measurement procedures is to build a discrete-time (nonlinear) simulation model for various real world systems such as gas sensing systems, gas turbine engines, large-signal amplifiers, RF thermistors etc. [4]- [9]. Discrete-time models for linear and non-linear dynamical systems can be developed under different assumptions of the measurement set-up, e.g.…”

Improved Initialization for Nonlinear State-Space Modeling

“…Having a look at more recent applications, accurate models are needed to tackle many problems in electrical engineering: to describe the behavior of sensors [1], to characterize ∆Σ modulators and study their stability [2], to better understand the functioning of wireless transmitters [3], for fault diagnosis in analog circuits [4], just to mention a few examples.…”

“…(1) y(t) = g(x(t), u(t)) (2) where u(t) ∈ R n u and y(t) ∈ R n y are the given input and output signal vectors at time instant t, x(t) ∈ R n x is the unknown state vector of the system, and f (·) and g(·) are the nonlinear functions to be estimated. Among the different model classes that can be used, statespace models show some advantages: they are general model structures that allow one to naturally describe system dynamics, and they are particularly suited for multiple input multiple output (MIMO) systems.…”