scholarly journals On the State Estimation of an Agonistic-Antagonistic Muscle System

2017 ◽  
Author(s):  
Thang Nguyen ◽  
Holly Warner ◽  
Hanieh Mohammadi ◽  
Dan Simon ◽  
Hanz Richter
Keyword(s):  
State Estimation ◽  
Control Method ◽  
The State ◽  
Reference Trajectory ◽  
State Variables ◽  
Muscle System ◽  
Spring Force ◽  
Antagonistic Muscle ◽  
Estimation Scheme ◽  
The Hill

In this paper, an agonistic-antagonistic muscle system is presented. This dual muscle system is based on the Hill muscle model. The problem of estimating the state variables and activation signals of the dual muscle system is addressed. A proposed estimation scheme which combines a super-twisting observer and an input estimator is given to provide a solution to the problem. A backstepping control method is used to track a reference trajectory. Numerical results are conducted to show that the relative error for state estimation is about 1% and that for the unknown inputs is about 3% when the measurements of the length of a muscle and its nonlinear spring force are affected by noise profiles whose normalized amplitude is 0.005.

scholarly journals Smart Grid State Estimation with PMUs Time Synchronization Errors

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5148
Author(s):  
Marco Todescato ◽  
Ruggero Carli ◽  
Luca Schenato ◽  
Grazia Barchi
Keyword(s):  
State Estimation ◽  
Distribution System ◽  
Time Synchronization ◽  
The State ◽  
Estimation Accuracy ◽  
Real Field ◽  
State Variables ◽  
Smart Power ◽  
Actual Performance ◽  
Synchronization Errors

State Estimation (SE) is one of the essential tasks to monitor and control the smart power grid. This paper presents a method to estimate the state variables combining the measurement of power demand at each bus with the data collected from a limited number of Phasor Measurement Units (PMUs). Although PMU data are usually assumed to be perfectly synchronized with the Coordinated Universal Time (UTC), this work explicitly considers the presence of time-synchronization errors due, for instance, to the actual performance of GPS receivers and the limited stability of the internal oscillator. The proposed algorithm is a recursive Kalman filter which not only estimates the state variables of the power system, but also the frequency deviations causing clock offsets which eventually affect the timestamps of the measures returned by different PMUs. The proposed solution was tested and compared with alternative approaches using both synthetic data applied to the IEEE 123 bus distribution feeder and real-field data collected from a small-size medium-voltage (MV) distribution system located inside the EPFL campus in Lausanne. Results show the validity of the proposed method in terms of state estimation accuracy. In particular, when some synchronization errors are present, the proposed algorithm can estimate and compensate for them.

scholarly journals Particle filter in power system state estimation – bad measurement data and branch disconnection

2015 ◽  
Vol 64 (2) ◽  
pp. 237-248
Author(s):  
Piotr Kozierski ◽  
Marcin Lis ◽  
Adam Owczarkowski ◽  
Dariusz Horla
Keyword(s):  
Power System ◽  
Particle Filter ◽  
State Estimation ◽  
Measurement Data ◽  
The State ◽  
Filter Method ◽  
System State ◽  
State Variables ◽  
Zero Bias ◽  
Power System State Estimation

Abstract An approach to power system state estimation using a particle filter has been proposed in the paper. Two problems have been taken into account during research, namely bad measurements data and a network structure modification with rapid changes of the state variables. For each case the modification of the algorithm has been proposed. It has also been observed that anti-zero bias modification has a very positive influence on the obtained results (few orders of magnitude, in comparison to the standard particle filter), and additional calculations are quite symbolic. In the second problem, used modification also improved estimation quality of the state variables. The obtained results have been compared to the extended Kalman filter method

scholarly journals Hysteresis-Based Current Control Approach by Means of the Theory of Switched Systems

2021 ◽  
Vol 11 (19) ◽  
pp. 9175
Author(s):  
Malte Thielmann ◽  
Florian Hans
Keyword(s):  
State Space ◽  
Switched Systems ◽  
Multiple Equilibria ◽  
Current Control ◽  
The State ◽  
Voltage Source ◽  
Voltage Source Converter ◽  
Reference Trajectory ◽  
State Variables ◽  
Control Approach

In this paper, a novel hysteresis-based current control approach is presented. The basis of the developed control approach is the theory of switched systems, in particular, the system class of switched systems with multiple equilibria. The proposed approach guarantees the convergence of the state trajectory into a region around a reference trajectory by selective switching between the individual subsystems. Here, the reference trajectory is allowed to be time varying, but lies within the state space spanned by the subsystem equilibria. Since already published approaches only show convergence to a common equilibrium of all subsystems, the extension to the mentioned state space is a significant novelty. Moreover, the approach is not limited to the number of state variables, nor to the number of subsystems. Thus, the applicability to a large number of systems is given. In the course of the paper, the theoretical basics of the approach are first explained by referring to a trivial example system. Then, it is shown how the theory can be applied to a practical application of a voltage source converter that is connected to a permanent-magnet synchronous motor. After deriving the limits of the presented control strategy, a simulation study confirms the applicability on the converter system. The paper closes with a detailed discussion about the given results.

scholarly journals Estimation Of State Variables And Model Parameters For The Evolution Of COVID-19 In The City Of Rio de Janeiro

2020 ◽  
Author(s):  
Helcio R.B. Orlande ◽  
Marcelo Colaco ◽  
George S. Dulikravich ◽  
Luiz F.S. Ferreira
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Rio De Janeiro ◽  
Reproduction Number ◽  
The State ◽  
Estimation Problem ◽  
Evolution Model ◽  
Model Parameters ◽  
State Variables ◽  
The City

Evolution model is based on that used by Hernandez et al., which considers the following groups: Susceptible, Incubating, Asymptomatic, Symptomatic, Hospitalized, Recovered and Accumulated deaths. Evolution model considers the possibility of infections from asymptomatic, symptomatic and hospitalized individuals. Evolution model considers the possibility that individuals who have recovered from the disease become symptomatic again. Observation model accounts for underreport of cases and deaths. Observation model accounts for delays in reporting cases and deaths. Model parameters were initially estimated with the Markov Chain Monte Carlo (MCMC) method, by using the data of the city of Rio de Janeiro from February 28, 2020 to April 29, 2020. These estimations were used as initial input values for the solution of the state estimation problem for the city of Rio de Janeiro. Algorithm of Liu & West for the Particle Filter was used for the solution of the state estimation problem because it allows the simultaneous estimation of state variables and model parameters. State estimation problem was solved with the data of the city of Rio de Janeiro, from February 28, 2020 to May 05, 2020. Monte Carlo simulations were run for 20 future days, considering uncertainties in the model parameters and state variables. Initial conditions were given by the state variables and corresponding distributions estimated with the particle filter on May 05, 2020. Distributions of the model parameters were also given by the estimations obtained for this date. Data of the city of Rio de Janeiro, from May 06, 2020 to May 15, 2020, were used for the validation of the solution of the state estimation problem. The present model, with the parameters obtained with the Particle Filter, accurately fits the number of reported cases and the number of reported deaths, for 10 days ahead of the period used for the solution of the state estimation problem. The Ratio of Infected Individuals per Reported Cases was around 15 on May 05, 2020. The Indexes of Under-Reported Cases and Deaths were around 12 and 2, respectively, on May 05, 2020. The Effective Reproduction Number was around 1.6 on February 28, 2020 and dropped to around 0.9 on May 05, 2020. However, uncertainties related to this parameter are large and the effective reproduction number is between 0.3 and 1.5, at the 95% credibility level. The particle filter must be used to periodically update the estimation of state variables and model parameters, so that future predictions can be made. Day 0 is February 28, 2020.

State and Parametric Estimation of Nonlinear Systems Described by Wiener Sate-Space Mathematical Models

2015 ◽  
pp. 107-145 ◽  
Author(s):  
Houda Salhi ◽  
Samira Kamoun
Keyword(s):  
Nonlinear Systems ◽  
Mathematical Models ◽  
State Estimation ◽  
Estimation Algorithm ◽  
The State ◽  
Parametric Estimation ◽  
State Variables ◽  
Estimation Algorithms ◽  
And Performance ◽  
The Stability

This chapter deals with the description, the parametric estimation, the state estimation, and the parametric and state estimation conjointly of nonlinear systems. The focus is on the class of nonlinear systems, which are described by Wiener state-space discrete-time mathematical models. Thus, the authors develop a new recursive parametric estimation algorithm, which is based on least squares techniques. The stability conditions of the developed parametric estimation scheme are analyzed using the Lyapunov method. The state estimation problem of the considered nonlinear systems is formulated. Thus, the authors propose a recursive state estimation algorithm, which is based on Kalman Filter. A new recursive algorithm is proposed, which permits one to estimate conjointly the parameters and the state variables of nonlinear systems described by Wiener mathematical models, with unknown parameters and state variables. The efficiency and performance of the proposed recursive estimation algorithms are tested on numerical simulation examples.

Linear Parameter Varying Modeling and Estimation of a SMA Wire Actuator

2018 ◽  
Author(s):  
Kyle Kubik ◽  
Austin Gurley ◽  
David Beale ◽  
Amanda Skalitzky
Keyword(s):  
System Analysis ◽  
Input Power ◽  
The State ◽  
Phase Fraction ◽  
Crystal Phase ◽  
State Variables ◽  
Linear Parameter ◽  
Muscle System ◽  
Linear Parameter Varying ◽  
Parameter Varying

Shape Memory Alloys (SMAs) actuators operate via a nonlinear and hysteretic relationship between input power and mechanical motion. This nonlinearity presents a serious challenge when developing methods for controlling these actuators. Because this hysteresis and nonlinearity is caused by the crystal phase transformation however, the SMA constitutive and kinetic models can be written in Linear Parameter Varying (LPV) form, with the partial derivative of crystal phase fraction with respect to temperature as the varying parameter. This allows a SMA system to be written in a state-space format where the coefficients in the state matrices vary as a function of the state variables, allowing for the application of powerful linear system analysis tools to this model without simplifying assumptions. This LPV model can then be used to create an estimator for the system, allowing for real-time approximations of the system states, including temperature and phase fraction. This paper presents the derivation of one such LPV model and explores its ability to accurately represent a physical SMA actuator system by comparison with an instrumented SMA muscle system.

scholarly journals State Estimation For An Agonistic‐Antagonistic Muscle System

2018 ◽  
Vol 21 (1) ◽  
pp. 354-363 ◽  
Author(s):  
Thang Tien Nguyen ◽  
Holly Warner ◽  
Hung La ◽  
Hanieh Mohammadi ◽  
Dan Simon ◽  
...  
Keyword(s):  
State Estimation ◽  
Muscle System ◽  
Antagonistic Muscle

scholarly journals The Extended Kalman Filter in the Dynamic State Estimation of Electrical Power Systems

Enfoque UTE ◽  
2018 ◽  
Vol 9 (4) ◽  
pp. 120-130
Author(s):  
Holger Ignacio Cevallos Ulloa ◽  
Gabriel Intriago ◽  
Douglas Plaza ◽  
Roger Idrovo
Keyword(s):  
Kalman Filter ◽  
Power Systems ◽  
State Estimation ◽  
Extended Kalman Filter ◽  
Electrical Power ◽  
The State ◽  
Dynamic State ◽  
State Variables ◽  
Electrical Power Systems ◽  
Error Index

The state estimation and the analysis of load flow are very important subjects in the analysis and management of Electrical Power Systems (EPS). This article describes the state estimation in EPS using the Extended Kalman Filter (EKF) and the method of Holt to linearize the process model and then calculates a performance error index as indicators of its accuracy. Besides, this error index can be used as a reference for further comparison between methodologies for state estimation in EPS such as the Unscented Kalman Filter, the Ensemble Kalman Filter, Monte Carlo methods, and others. Results of error indices obtained in the simulation process agree with the order of magnitude expected and the behavior of the filter is appropriate due to follows adequately  the true value of the state variables. The simulation was done using Matlab and the electrical system used corresponds to the IEEE 14 and 30 bus test case systems. State Variables to consider in this study are the voltage and angle magnitudes.

scholarly journals A Reference Trajectory Based Discrete Time Sliding Mode Control Strategy

2019 ◽  
Vol 29 (3) ◽  
pp. 517-525 ◽  
Author(s):  
Andrzej Bartoszewicz ◽  
Katarzyna Adamiak
Keyword(s):  
Sliding Mode Control ◽  
Discrete Time ◽  
Control Strategy ◽  
Control Method ◽  
Sliding Mode ◽  
Reference Trajectory ◽  
State Variables ◽  
Reaching Law ◽  
Mode Control ◽  
Sliding Motion

Abstract This study presents a new, reference trajectory based sliding mode control strategy for disturbed discrete time dynamical systems. The desired trajectory, which is generated externally according to an existing switching type reaching law, determines the properties of the emerging sliding motion of the system. It is proved that an appropriate choice of the trajectory generator parameters ensures the existence of the quasi-sliding motion of the system according to the definition by Gao et al. (1995) in spite of the influence of disturbances. Moreover, the paper shows that the application of the desired trajectory based reaching law results in a significant reduction in the quasi-sliding mode band width and errors of all state variables. Therefore, in comparison with Gao’s control method, the system’s robustness is increased. The paper also presents an additional modification of the reaching law, which guarantees a further reduction in the quasi-sliding mode band in the case of slowly varying disturbances. The results are confirmed with a simulation example.

Identification and State Observation of Uncertain Chaotic Systems Using Projectional Differential Neural Networks

2011 ◽  
pp. 42-67
Author(s):  
Alejandro García ◽  
Isaac Chairez ◽  
Alexander Poznyak
Keyword(s):  
Neural Network ◽  
State Estimation ◽  
Chaotic Systems ◽  
Chaotic Behavior ◽  
The State ◽  
State Variables ◽  
Neural Network Approach ◽  
State Observation ◽  
The Neural Network ◽  
Uncertain Chaotic Systems

The following chapter tackles the nonparametric identification and the state estimation for uncertain chaotic systems by the dynamic neural network approach. The developed algorithms consider the presence of additive noise in the state, for the case of identification, and in the measurable output, for the state estimation case. Mathematical model of the chaotic system is considered unknown, only the chaotic behavior as well as the maximal and minimal bound for each one of state variables are taking into account in the algorithm. Mathematical analysis and simulation results are presented. Application considering the so-called electronic Chua’s circuit is carried out; particularly a scheme of information encryption by the neural network observer with a noisy transmission is showed. Formal mathematical proofs and figures, illustrate the robustness of proposed algorithms mainly in the presence of noises with high magnitude.

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