Extended state observer

In this paper, a point-to-point positioning controller based on Active Disturbance Rejection Control (ADRC) is applied to a linear motion actuator using lead ball screw. In the design, the Extended State Observer (ESO) is adopted to the controller structure, and the ADRC is designed which consists of the ESO and the nonlinear PD controller to estimate and compensate the disturbances.

7p viannee 02-08-2023 5 4   Download

In this paper, a BSMC combined with the ESO is introduced to design a wheel slip controller based on a quarter-car model. ESO is used to estimate the state variables and the total uncertainty of the model without the need for the derivative form of the wheel slip.

6p vifalcon 16-05-2023 4 2   Download

This study investigates load frequency control based generalized extended state observer (GESO) for interconnected power system subject to multi-kind of the power plant. First, the mathematical model of the interconnected power system is proposed based on the dynamic model of thermal power plant with reheat turbine and hydro power plant.

18p nguaconbaynhay11 07-04-2021 22 1   Download

This work presents analysis, design and implementation of two schemes of Extended State Observer (ESO) to estimate the position, velocity and unmeasurable states for magnetic levitation systems, Linear ESO (LESO) and Nonlinear ESO (NESO).

11p vivalletta2711 11-01-2020 27 1   Download

The paper deals with a velocity control problem of a three-mass system. The equations of motion of the system with limited shaft stiffness and damping is derived via d’Alembert principle. Based on the system dynamics, an active disturbance rejection control is developed for the system via a support of an extended state observer.

6p viminotaur2711 29-10-2019 20 0   Download

This paper proposes ADRC in combination with Input shaping approach in which ADRC is used to reject disturbance while keeping the simplicity in design as PID controller, and Input shaping plays the role of vibration suppression. Simulations show the effectiveness of the proposed approach.

6p visasuke2711 25-04-2019 18 0   Download

In this paper, we extend current state-of-theart research on unsupervised acquisition of scripts, that is, stereotypical and frequently observed sequences of events. We design, evaluate and compare different methods for constructing models for script event prediction: given a partial chain of events in a script, predict other events that are likely to belong to the script.

9p bunthai_1 06-05-2013 60 3   Download

The Earth is the only planet in our solar system that supports life. The complex process of evolution occurred on Earth only because of some unique environmental conditions that were present: water, an oxygen-rich atmosphere, and a suitable surface temperature. Climate change refers to a statistically significant variation in either the mean state of the climate or in its variability, persisting for an extended period (typically decades or longer).

0p phoebe75 19-02-2013 45 3   Download

NONLINEAR OBSERVATION SCHEME AND DYNAMIC MODEL (EXTENDED KALMAN FILTER) 16.1 INTRODUCTION In this section we extend the results for the linear time-invariant and timevariant cases to where the observations are nonlinearly related to the state vector and/or the target dynamics model is a nonlinear relationship [5, pp. 105– 111, 166–171, 298–300]. The approachs involve the use of linearization procedures. This linearization allows us to apply the linear least-squares and minimum-variance theory results obtained so far.

10p khinhkha 30-07-2010 102 12   Download

DUAL EXTENDED KALMAN FILTER METHODS Eric A. Wan and Alex T. Nelson Department of Electrical and Computer Engineering, Oregon Graduate Institute of Science and Technology, Beaverton, Oregon, U.S.A. 5.1 INTRODUCTION The Extended Kalman Filter (EKF) provides an efficient method for generating approximate maximum-likelihood estimates of the state of a discrete-time nonlinear dynamical system (see Chapter 1). The filter involves a recursive procedure to optimally combine noisy observations with predictions from the known dynamic model.

51p khinhkha 29-07-2010 102 9   Download

The Extended Kalman Filter (EKF) provides an efficient method for generating approximate maximum-likelihood estimates of the state of a discrete-time nonlinear dynamical system (see Chapter 1). The filter involves a recursive procedure to optimally combine noisy observations with predictions from the known dynamic model. A second use of the EKF involves estimating the parameters of a model (e.g., neural network) given clean training data of input and output data (see Chapter 2).

51p duongph05 07-06-2010 140 16   Download