This paper presents a simplified nonlinear observer for power systems by exploring the special features of the nonlinear power system model. Rather than directly applying the existing nonlinear observer theory to the 6 order nonlinear power system model, the model is first decoupled into a 3 order nonlinear subsystem and a 3 order linear subsystem. Low order observers are designed for each subs...

This paper presents the design and the analysis of an indirect adaptive control strategy for a lactic acid production, which is carried out in continuous stirred tank bioreactors. Firstly, an indirect adaptive control structure based on the nonlinear process model is derived by combining a linearizing control law with a new parameter estimator. This estimator is used for on-line estimation of t...

A state estimator design is presented for a Mars rover prototype. Odometry estimates are obtained by utilizing the f u l l kinematics of the vehicle including the nonlinear internal kinematics of the rover rocker-bogey mechanism as well as the contact kinematics between the wheels and the ground. Additional sewing using gyroscopes, acclerometers and visual sensors allows for robust rover motion...

Real-data testing results of a real-time nonlinear freeway traffic state estimator are presented with a particular focus on its adaptive features. The pursued general approach to the real-time adaptive estimation of complete traffic state in freeway stretches or networks is based on stochastic nonlinear macroscopic traffic flow modeling and extended Kalman filtering. One major innovative aspect...

It is well known that the time-varying Kalman Filter (KF) is globally exponentially stable and optimal in the sense of minimum variance under some conditions. However, nonlinear approximations such as the extended KF linearizes the system about the estimated state trajectories, leading in general to loss of both global stability and optimality. Nonlinear observers tend to have strong, often glo...

We study a parametric approach for hidden discrete-time diffusion models based on contrast minimization and deconvolution. This approach leads to estimate a large class of stochastic models with nonlinear drift and nonlinear diffusion. It can be applied, for example, for ecological and financial state space models. After proving consistency and asymptotic normality of the estimator, leading to ...

This paper presents a new multi-passive-sensor target tracking algorithm which yields a nonlinear state estimator called Gaussian filter based on deterministic sampling. Firstly, this state estimator employs a deterministic sample selection scheme, where a parametric density function representation of the sample points is employed to approximate the cumulative distribution function of the prior...

A novel robust estimator is proposed for extracting a single complex sinusoid and its parameter (frequency) from measurements corrupted by white noise. This estimator is called an H1 sinusoidal estimator (HSE), which is derived by applying an H1 filter to a noisy sinusoidal model with the state-space representation. Simulations demonstrate that the HSE is more robust to the nature of observatio...

Original scientific paper The architecture of the proposed Dual Layer Hybrid State Estimator starts with the linear state estimator that only takes synchrophasors as inputs and calculates states of the buses with the deployed Phasor Measurement Units (PMUs) and their adjacent buses. These states are used to obtain the pseudomeasurements that are processed together with conventional SCADA measur...

We introduce a method of observer design for systems described by a linear part with a nonlinear perturbation, where the perturbation is parameterized by a vector of unknown, constant parameters. The design is modular, and consists of a modified high-gain observer that estimates the states of the system together with the full perturbation, and a parameter estimator. The parameter estimator is c...