In this paper we consider the topic of model reduction for nonlinear dynamical systems based on kernel expansions. Our approach allows for a full offline/online decomposition and efficient online computation of the reduced model. In particular we derive an a-posteriori state-space error estimator for the reduction error. A key ingredient is a local Lipschitz constant estimation that enables rig...

We present a working implementation of a dynamics based architecture for visual sensing. This architecture provides field rate estimates of the positions and velocities of two independent falling balls in the face of repeated visual occlusions and departures from the field of view. The practical success of this system can be attributed to the interconnection of two strongly nonlinear dynamical ...

The Joint Maximum Likelihood (JML) criterion is used to derive the optimal recursive-iterative estimator for discrete nonlinear dynamic systems. For linear systems this approach constitutes, in its recursive form, the structure of the Kalman Filter. The JML approach to estimation of nonlinear systems can be solved by batch formulas at the cost of extensive computational effort, i.e. with each n...

We propose an estimator for nonlinear systems with unmatched unknown inputs and under measurement noise. The estimator design is based on the combination of observer design for descriptor systems, sliding-modes theory and adaptive control. The estimation of the measurement noise is achieved thanks to the transformation of the original system into a singular form where the measurement noise make...

in the several past years, extended kalman filter (ekf) and unscented kalman filter (ukf) havebecame basic algorithm for state-variables and parameters estimation of discrete nonlinear systems.the ukf has consistently outperformed for estimation. sometimes least estimation error doesn't yieldwith ukf for the most nonlinear systems. in this paper, we use a new approach for a two variablesta...

This paper is concerned with the problem of event-triggered distributed state estimation for a class of discrete nonlinear stochastic systems with time-varying delays, randomly occurring uncertainties and randomly occurring nonlinearities. Both the uncertainties and nonlinearities enter into the system in a random way characterized by random variables obeying the Bernoulli distribution. An even...

We address the problem of robust state reconstruction for discrete-time nonlinear systems when actuators and sensors are injected with (potentially unbounded) attack signals. Exploiting redundancy in using a bank unknown input observers (UIOs), we propose an observer-based estimator capable providing asymptotic estimates system signals under condition that numbers sufficiently small. Using prop...

Cancer therapy optimization is an issue that can be solved using the control engineering approach. An optimal generation algorithm presented and tested a tractable mouse model of breast cancer. The optimized therapeutic protocol calculated in closed-loop manner at fixed time instants, twice week. controller consists nonlinear predictive which uses state estimation moving horizon estimator. esti...

The feasibility of developing a neural network to perform nonlinear Bayesian estimation from sparse data is explored using an example from clinical pharmacology. The problem involves estimating parameters of a dynamic model describing the pharmacokinetics of the bronchodilator theophylline from limited plasma concentration measurements of the drug obtained in a patient. The estimation performan...