... of thermoacoustic instabilities in combustion processes is presented to demonstrate the efficacy of the proposed framework. e-mail:  wm.haddad@aerospace.gatech.edu e-mail:  xu.jin@uky.edu 24 01 2019 30 09 2019 14 11 2019 stochastic systems stochastic control Lyapunov functions...

... 12 01 2012 closed loop systems control system synthesis nonlinear control systems optimal control probability stochastic systems uncertain systems The conventional theory of stochastic control is particularly suitable for taking into account randomly varying system parameters...

... stable are derived. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed conditions. 29 01 2011 18 09 2011 05 12 2011 05 12 2011 linear matrix inequalities Markov processes nonlinear systems stability stochastic systems...

... stability stochastic systems networked control systems (NCSs) generalized predictive control (GPC) time delays packet dropouts Markov chains stochastic stability The study of control systems in which the data between the controller side and the plant side are exchanged via communication...

... of palstance errors Moreover, in order to emphasize the distinction between stochastic systems and deterministic ones, we repeat the same numerical computations along 50 discretized Brownian paths, which illustrate the distributions of the random variables x 1 ( t ) − x r 1 ( t...

...Lifeng Ma; Zidong Wang; Yuming Bo; Zhi Guo This paper is concerned with the variance-constrained controller design problem for a class of uncertain nonlinear stochastic systems with possible actuator faults. The stochastic nonlinearities described by statistical means are quite general that include...

... method. 29 12 2005 09 10 2006 robust control nonlinear control systems uncertain systems linear matrix inequalities stochastic systems continuous time systems fuzzy control closed loop systems suboptimal control Lyapunov methods control system synthesis fuzzy control H...

..., ship, automobile, and robot, etc. When multiple sensors measure the states for the same stochastic system, generally we have two different types of methods to process the measured sensor data. The first method is the centralized filter ( 1 ), where all measured sensor data are communicated...

... 24 March 2003 18 09 2003 fault diagnosis stochastic systems stochastic processes autoregressive processes feedforward neural nets The natural starting point for fault detection is the model of the system. Many systems, such as aircraft, spacecraft, gas turbine engines...

... independent mode in every state, an explicit analysis of the entropy and density function can be obtained. The performance of the closure method is supported by employing three stochastic systems with some stationary exact solutions and through Monte Carlo simulations. Contributed by the Dynamic Systems...

...Bart Peeters; Guido De Roeck This paper reviews stochastic system identification methods that have been used to estimate the modal parameters of vibrating structures in operational conditions. It is found that many classical input-output methods have an output-only counterpart. For instance...

... . Manuscript received by the Dynamic Systems and Control Division January 29, 2001. Associate Editor: S. Fassois. 29 January 2001 identification nonlinear systems linear systems least squares approximations matrix algebra stochastic systems Linear system identification has been...

...Cyril Coumarbatch; Zoran Gajic In this paper we show how to completely and exactly decompose the optimal Kalman filter of stochastic systems in multimodeling form in terms of one pure-slow and two pure-fast, reduced-order, independent, Kalman filters. The reduced-order Kalman filters are all driven...

... expression for the minimum cost of the closed-loop system. linear quadratic Gaussian control control system synthesis Markov processes stochastic systems minimisation LQG Control Markov Parameters Digital Control Output Variance Constraint Control Finite Horizon Optimal Control...