State estimation is the foundation of any control and decision making in power networks. The first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. This paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual cal...

State estimators for induction motors are generally designed based on standard simplified models, assuming linear magnetic characteristics. Since they are actually nonlinear, especially for high power machines, the mentioned state estimators are likely not able to achieve the estimation accuracy they have been designed for. In this paper, a new state estimator is developed for a (uniform air-ga...

A solution is provided to the problem of computing a convex set of conditional probability distributions that characterize the state of a nonlinear dynamic system as it evolves in time. The estimator uses the Galerkin approximation to solve Kolmogorov's equation for the diiusion of a continuous-time nonlinear system with discrete-time measurement updates. Filtering of the state is accomplished ...

Model predictive control (MPC) relies on real-time optimization to determine open-loop control profiles and state estimates given process measurements. When the underlying process model is nonlinear, the MPC system exhibits unique behavior not seen in linear MPC. In this article, we highlight some of the characteristics of nonlinear models in the context of closedloop performance. We examine th...

The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications in...

This paper proposes a new set-membership state estimator for estimating the state vector of a nonlinear dynamic robot. The method combines a symbolic technique based on flatness concepts with rigorous numerical methods based on interval analysis. Two testcases related to the state estimation of a sailboat robot are proposed to illustrate the principle and the efficiency of the approach.

State estimator design for a nonlinear discrete-time system is a challenging problem, further complicated when additional physical insight is available in the form of inequality constraints on the state variables and disturbances. One strategy for constrained state estimation is to employ online optimization using a moving horizon approximation. In this article we propose a general theory for c...

This paper considers parameter estimation for nonlinear and non-Gaussian state-space models with correlation. We propose an asymptotic quasilikelihood (AQL) approach which utilises a nonparametric kernel estimator of the conditional variance covariances matrix Σt to replace the true Σt in the standard quasi-likelihood. The kernel estimation avoids the risk of potential miss-specification of Σt ...

It is shown that any stabilizing, certainty equivalence control used within an adaptive control system, causes the familiar interconnection of a controlled process and associated output estimator to be detectable through the estimator's output error e p , for every frozen value of the index or parameter vector p upon which both the estimator and controller dynamics depend. The fact that certain...

It is shown that any stabilizing, certainty equivalence control used within an adaptive control system, causes the familiar interconnection of a controlled process and associated output estimator to be detectable through the estimator's output error ep, for every frozen value of the index or parameter vector p upon which both the estimator and controller dynamics depend. The fact that certainty...