A moving horizon state and parameter estimation scheme with guaranteed robust convergence

A Quick Identification Method of Continuous-Time Nonlinear Systems and Its Application to Power Plant Control

This paper presents a Hybrid Particle Filter based RNN method for state estimation of non-linear dynamical system with knowledge of its input and output measurements. Particle filters are sequential Monte Carlo methods based on point mass (or particle) representations of probability densities, which is used to train Recurrent Neural Networks for estimation problems. The performance this method is compared with EKF based estimation and RNN based estimation. An Induction motor is considered as typical non-linear system and is implemented in MATLAB environment.

This paper investigated the problem of state and fault estimation for nonlinear discrete time systems in presence of unknown disturbances. A novel unbiased minimum variance filter (UMVF) is derived by reconstructing the non linear version of NUMV filter. In this work we assume that no prior knowledge about the dynamic of the disturbance and the fault are known. In this paper we considers that the fault affects both the system state and measurement equations, but the disturbance affects only the system state. The NUMV filter presented in this paper is an extension of the filter presented in [11]. The efficacy of the proposed filter is demonstrated by two simulation examples.

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Robust unscented Kalman filter with adaptation of process and measurement noise covariances

Parameter estimation for uncertain systems based on fault diagnosis using Takagi–Sugeno model

Three-stage unscented Kalman filter for state and fault estimation of nonlinear system with unknown input

This paper adresses the problem of simultaneously estimating the state and the fault of nonlinear discrete-time stochastic systems in light of the unknown input filtering framework. The fault and unknown disturbances which may cause great estimate errors and even divergence of conventional filters, affect both the system state and the measurements. Inspired by the robust two stage Kalman filter for linear discrete-time system, a nonlinear robust state and fault estimator is derived. In order to achieve the aim, the nonlinear system with fault and unknown disturbances is first transformed into an equivalent descriptor system. Next, it is shown that the previously proposed robust two stage Kalman filter can be applied to yield a robust state and fault estimation. Simulation results for a robotic manipulator show the effectiveness of the proposed method.

State and parameter estimation of nonlinear stochastic systems by evolution strategies based particle filter

An Improved Gaussian Sum Unscented Kalman Filter

Guaranteed methods based on constrained zonotopes for set-valued state estimation of nonlinear discrete-time systems

This paper presents new estimation methods for discrete fractional-order state-space systems with coloured measurement noise. A novel approach is proposed to convert a fractional system with coloured measurement noise to a system with white measurement noise in which the process and measurement noises are correlated with each other. In this paper, two new Kalman filter algorithms for fractional-order linear state-space systems with coloured measurement noise, as well as a new extended Kalman filter algorithm for state estimation in nonlinear fractional-order state-space systems with coloured measurement noise, are proposed. The accuracy of the equations and relations is confirmed in several theorems. The validity and effectiveness of the proposed algorithms are verified by simulation results and compared with previous work. Results show that for linear and nonlinear fractional-order systems with coloured noise, the proposed methods are more accurate than conventional methods regarding estimation error and estimation error covariance. Simulation results demonstrate that the proposed algorithms can accurately perform estimation in fractional-order systems with coloured measurement noise.

In this article, we propose an improvement of a recently introduced algebraic approach for the non-asymptotic state and parameter estimation of nonlinear systems. In particular, we increase the robustness of the estimation method with respect to zero mean, high frequency, measurement noises by introducing a so-called invariant filtering technique. In order to reduce an already fast transient to the convergence, when subject to measurement noise, we devise an estimation policy consisting of two overlapping estimators with appropriate switchings between their results. These are two identical time-shifted estimators running in parallel with an overlapping estimation period. The benefits of our method are demonstrated on the state observation of a chaotic system of the Rossler type.

It has been a common consensus that general techniques for stabilization of nonlinear systems are available only for some special classes of nonlinear systems. Control design for nonlinear systems with uncertain components is usually carried out on a per system basis, especially when physical control constraints, and certain control performance measures such as optimum time control are imposed. Elegant adaptive control techniques are difficult to apply to this type of problems. A new neural network based control design is proposed and presented in this paper to deal with a special class of uncertain nonlinear systems with multiple inputs. The desired system dynamics are analyzed and utilized in the process of the proposed intelligent control design. The theoretical results are provided to justify the design procedures. The simulation study is conducted on a second-order bilinear system with two inputs and uncertainties on its parameters. The simulation results indicate that the proposed design approach is effective.

An extended model-based observer for state estimation in nonlinear hysteretic structural systems