A multimodel scheme control for a tunnel-diode trigger circuit

Yin

Mathematical Problems in Engineering

et al. 2014

This paper investigates the disturbance attraction domain estimation of saturated Markov jump systems with truncated Gaussian process. The aim is to estimate the disturbance domain of attraction so that the state is maintained in a neighbour around the origin by a state feedback controller regardless of bounded disturbance. The problem is formulated as parameter-dependent linear matrix inequalities (LMIs). The optimal disturbance attraction domain is obtained through searching for most appropriate auxiliary parameters in the defined domain. A numerical example is presented to show the potential application of the results.

Section: Introductionmentioning

confidence: 87%

Disturbance Attraction Domain Estimation for Saturated Markov Jump Systems with Truncated Gaussian Process

Yin

Mathematical Problems in Engineering

et al. 2014

“…The cell filter is designed in the bounded sate space x 1 (k) ∈ [5,9], x 2 (k) ∈ [1,5], which is discredited into 40 × 40 state cells under mode 1 and mode 2 respectively. The GCM is implemented with 100 uniformly samples per cell to obtain the transition matrixes and likelihood matrixes.…”

Section: Fig 4 Transitions Between Modesmentioning

confidence: 99%

“…For example, in the target tracking community, the maneuver of a target can be modeled as a class of hybrid systems with two modes (constant acceleration and constant victory), and the switch between them occurs only after confirming whether the height of the target reached to a certain point. The usage of the so-named state dependent transition probabilities (SDTPs) might be necessary, and more examples can be found in [9,10]. In view of this, some existing efforts have been transferred to the hybrid systems with SDTPs.…”

Section: Introductionmentioning

confidence: 99%

Bayesian estimation for nonlinear stochastic hybrid systems with state dependent transitions

J. of Syst. Eng. Electron.

2012

The Bayesian approach is considered as the most general formulation of the state estimation for dynamic systems. However, most of the existing Bayesian estimators of stochastic hybrid systems only focus on the Markov jump system, few literature is related to the estimation problem of nonlinear stochastic hybrid systems with state dependent transitions. According to this problem, a new methodology which relaxes quite a restrictive assumption that the mode transition process must satisfy Markov properties is proposed. In this method, a general approach is presented to model the state dependent transitions, the state and output spaces are discreted into cell space which handles the nonlinearities and computationally intensive problem offline. Then maximum a posterior estimation is obtained by using the Bayesian theory. The efficacy of the estimator is illustrated by a simulated example.

“…In this case, the TP is time varying, and hence the underlying Markov process (or chain) is nonhomogeneous [8]. Similar phenomena are also observed in many engineering systems, such as electronic circuits [5]. Thus, there is an urgent need to develop new theories and methodologies for dynamical systems with nonhomogeneous TP.…”

Section: Introductionmentioning

confidence: 99%

Output Peak Control of Nonhomogeneous Markov Jump System with Unit-Energy Disturbance

Circuits Syst Signal Process

Teo

2014

This paper considers output peak controller design for discrete nonhomogeneous Markov jump systems under unit-energy disturbance. The mode-dependent output peak feedback controller is designed to ensure that the resulting closed-loop system is stochastically stable and the peak of the output is within a specified range. Furthermore, the optimal energy-to-peak gain indices of the mode-dependent and the mode-independent state feedback controllers are evaluated and compared. A numerical example is presented to illustrate the applicability of the results obtained.