Multimodel stabilization based on the state estimation with unmeasurable premise variables of a bioreactor

This paper presents a modification of the conventional minimum variance state estimator to accommodate the effects of randomly varying delays in arrival of sensor data at the controller terminal. In this approach, the currently available sensor data is used at each sampling instant to obtain the state estimate which, in turn, can be used to generate the control signal. Recursive relations for the filter dynamics have been derived, and the conditions for uniform asymptotic stability of the filter have been conjectured. Results of simulation experiments using a flight dynamic model of advanced aircraft are presented for performance evaluation of the state estimation filter.

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A Two-Step State Estimation Algorithm for Hybrid AC-DC Distribution Grids

Energies ◽

10.3390/en14071967 ◽

2021 ◽

Vol 14 (7) ◽

pp. 1967

Author(s):

Gaurav Kumar Roy ◽

Marco Pau ◽

Ferdinanda Ponci ◽

Antonello Monti

Keyword(s):

State Estimation ◽

Distribution System ◽

Renewable Energy Sources ◽

Estimation Algorithm ◽

The State ◽

Second Step ◽

Distribution Grid ◽

Dc Distribution ◽

Attractive Option ◽

Distribution Grids

Direct Current (DC) grids are considered an attractive option for integrating high shares of renewable energy sources in the electrical distribution grid. Hence, in the future, Alternating Current (AC) and DC systems could be interconnected to form hybrid AC-DC distribution grids. This paper presents a two-step state estimation formulation for the monitoring of hybrid AC-DC grids. In the first step, state estimation is executed independently for the AC and DC areas of the distribution system. The second step refines the estimation results by exchanging boundary quantities at the AC-DC converters. To this purpose, the modulation index and phase angle control of the AC-DC converters are integrated into the second step of the proposed state estimation formulation. This allows providing additional inputs to the state estimation algorithm, which eventually leads to improve the accuracy of the state estimation results. Simulations on a sample AC-DC distribution grid are performed to highlight the benefits resulting from the integration of these converter control parameters for the estimation of both the AC and DC grid quantities.

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State Estimation for Cooperative Lateral Vehicle Following Using Vehicle-to-Vehicle Communication

Electronics ◽

10.3390/electronics10060651 ◽

2021 ◽

Vol 10 (6) ◽

pp. 651

Author(s):

Wouter Schinkel ◽

Tom van der Sande ◽

Henk Nijmeijer

Keyword(s):

State Estimation ◽

Cooperative Control ◽

The State ◽

State Estimator ◽

Estimation Process ◽

Vehicle Communication ◽

Vehicle To Vehicle ◽

Control Schemes ◽

Input Signals ◽

Vehicle To Vehicle Communication

A cooperative state estimation framework for automated vehicle applications is presented and demonstrated via simulations, the estimation framework is used to estimate the state of a lead and following vehicle simultaneously. Recent developments in the field of cooperative driving require novel techniques to ensure accurate and stable vehicle following behavior. Control schemes for the cooperative control of longitudinal and lateral vehicle dynamics generally require vehicle state information about the lead vehicle, which in some cases cannot be accurately measured. Including vehicle-to-vehicle communication in the state estimation process can provide the required input signals for the practical implementation of cooperative control schemes. This study is focused on demonstrating the benefits of using vehicle-to-vehicle communication in the state estimation of a lead and following vehicle via simulations. The state estimator, which uses a cascaded Kalman filtering process, takes the operating frequencies of different sensors into account in the estimation process. Simulation results of three different driving scenarios demonstrate the benefits of using vehicle-to-vehicle communication as well as the attenuation of measurement noise. Furthermore, in contrast to relying on low frequency measurement data for the input signals of cooperative control schemes, the state estimator provides a state estimate at every sample.

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Methodology for Implementing the State Estimation in Renewable Energy Management Systems

Energies ◽

10.3390/en14082301 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2301

Author(s):

Yun-Sung Cho ◽

Yun-Hyuk Choi

Keyword(s):

Renewable Energy ◽

Power Systems ◽

State Estimation ◽

Measurement Errors ◽

Large Scale ◽

Test System ◽

The State ◽

Inner Function ◽

Outer Function ◽

Bad Data

This paper describes a methodology for implementing the state estimation and enhancing the accuracy in large-scale power systems that partially depend on variable renewable energy resources. To determine the actual states of electricity grids, including those of wind and solar power systems, the proposed state estimation method adopts a fast-decoupled weighted least square approach based on the architecture of application common database. Renewable energy modeling is considered on the basis of the point of data acquisition, the type of renewable energy, and the voltage level of the bus-connected renewable energy. Moreover, the proposed algorithm performs accurate bad data processing using inner and outer functions. The inner function is applied to the largest normalized residue method to process the bad data detection, identification and adjustment. While the outer function is analyzed whether the identified bad measurements exceed the condition of Kirchhoff’s current law. In addition, to decrease the topology and measurement errors associated with transformers, a connectivity model is proposed for transformers that use switching devices, and a transformer error processing technique is proposed using a simple heuristic method. To verify the performance of the proposed methodology, we performed comprehensive tests based on a modified IEEE 18-bus test system and a large-scale power system that utilizes renewable energy.

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Optimal timing of the observation for the state estimation and control of the stochastic discrete linear system

International Journal of Control ◽

10.1080/00207177808922398 ◽

1978 ◽

Vol 27 (4) ◽

pp. 621-637 ◽

Cited By ~ 6

Author(s):

YOSHIKAZU SAWARAGI ◽

TAKASHI SOEDA ◽

YUTAKA TOMITA ◽

ISAO IMAI

Keyword(s):

Linear System ◽

State Estimation ◽

The State ◽

Optimal Timing ◽

Estimation And Control ◽

And Control

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Set-membership filtering for automatic guided vehicles with unknown-but-bounded noises

Transactions of the Institute of Measurement and Control ◽

10.1177/01423312211043666 ◽

2021 ◽

pp. 014233122110436

Author(s):

Hao Yang ◽

Yilian Zhang ◽

Wei Gu ◽

Fuwen Yang ◽

Zhiquan Liu

Keyword(s):

State Estimation ◽

Estimation Algorithm ◽

The State ◽

Estimation Problem ◽

Filtering Method ◽

Automatic Guided Vehicles ◽

Tracking Process ◽

Set Membership ◽

Text Filtering ◽

System States

This paper is concerned with the state estimation problem for an automatic guided vehicle (AGV). A novel set-membership filtering (SMF) scheme is presented to solve the state estimation problem in the trajectory tracking process of the AGV under the unknown-but-bounded (UBB) process and measurement noises. Different from some existing traditional filtering methods, such as Kalman filtering method and [Formula: see text] filtering method, the proposed SMF scheme is developed to provide state estimation sets rather than state estimation points for the system states to effectively deal with UBB noises and reduce the requirement of the sensor precision. Then, in order to obtain the state estimation ellipsoids containing the true states, a set-membership estimation algorithm is designed based on the AGV physical model and S-procedure technique. Finally, comparison examples are presented to illustrate the effectiveness of the proposed SMF scheme for an AGV state estimation problem in the present of the UBB noises.

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Globally Optimal Distributed Kalman Filtering for Multisensor Systems with Unknown Inputs

Sensors ◽

10.3390/s18092976 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2976 ◽

Cited By ~ 3

Author(s):

Yali Ruan ◽

Yingting Luo ◽

Yunmin Zhu

Keyword(s):

State Estimation ◽

Kalman Filtering ◽

Optimal Algorithm ◽

The State ◽

Unknown Input ◽

Initial Value ◽

State Estimate ◽

Multisensor Systems ◽

Unknown Inputs ◽

Augmented State

In this paper, the state estimation for dynamic system with unknown inputs modeled as an autoregressive AR (1) process is considered. We propose an optimal algorithm in mean square error sense by using difference method to eliminate the unknown inputs. Moreover, we consider the state estimation for multisensor dynamic systems with unknown inputs. It is proved that the distributed fused state estimate is equivalent to the centralized Kalman filtering using all sensor measurement; therefore, it achieves the best performance. The computation complexity of the traditional augmented state algorithm increases with the augmented state dimension. While, the new algorithm shows good performance with much less computations compared to that of the traditional augmented state algorithms. Moreover, numerical examples show that the performances of the traditional algorithms greatly depend on the initial value of the unknown inputs, if the estimation of initial value of the unknown input is largely biased, the performances of the traditional algorithms become quite worse. However, the new algorithm still works well because it is independent of the initial value of the unknown input.

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