On emulated nonlinear reduced-order observers for networked control systems

On emulated nonlinear reduced-order observers for networked control systems

We consider a general class of nonlinear reduced-order observers and show that the global asymptotic convergence of the observation error in the absence of network-induced constraints is maintained for the emulated observer semiglobally and practically (with respect to the maximum allowable transmission interval) when system measurements are sent through a communication channel. Networks govern...

Discrete-Time Reduced Order Neural Observers for Uncertain Nonlinear Systems

This paper focusses on a novel discrete-time reduced order neural observer for nonlinear systems, which model is assumed to be unknown. This neural observer is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm, using a parallel configu...

Preserving order observers for nonlinear systems

Preserving Order Observers provide an estimation that is always above or below the true variable and, in the absence of uncertainties/perturbations, the estimation converges asymptotically to the true value of the variable. In this paper we propose a novel methodology to design preserving order observers for a class of nonlinear systems in the nominal case or when perturbations/uncertainties ar...

Reduced Order Modeling of Nonlinear Control Systems

Reduced-order State Observers

Because the number of state variables in a reduced-order observer is less than the order n of S by the number m of (independent) observations, the reduced-order observer is parsimonious, often a desirable engineering quality. But, in addition, a reduced-order observer may have better properties than a full-order observer, especially with regard to robustness of a control system which uses an ob...