State Space Modeling and Stable Dynamic Inversion for Trajectory Tracking on an Industrial Seat Test Rig

2002 ◽  
Vol 8 (7) ◽  
pp. 1033-1050 ◽  
Author(s):  
J. De Cuyper ◽  
M. Verhaegen
Keyword(s):  
State Space ◽  
Trajectory Tracking ◽  
State Space Model ◽  
State Space Models ◽  
Dynamic Inversion ◽  
The Novel ◽  
Test Rig ◽  
Software Packages ◽  
Novel Approach ◽  
Space Modeling

This paper introduces a novel approach for trajectory tracking on seat test rigs. Seat test rigs are used in the automotive industry to test prototypes of new seats with respect to their comfort and vibration characteristics. Classically, a digital Fourier transform (DFT) approach, based on a Frequency Response Function (FRF) model, is used in this domain and different software packages exist that are based on this approach. The novel approach proposes state space models to identify the test rig and uses the recently developed Stable Dynamic Inversion (SDI) to invert the obtained state space models. The approach is validated on an industrial seat test rig and it is shown that the new approach is a competitive alternative to the existing DFT based methods. A closed loop experiment is also performed; this shows that the feedback controller allows one to reduce the state space model order without loss of accuracy.

scholarly journals Evaluation of methods for spawner–recruit analysis in mixed-stock Pacific salmon fisheries

2020 ◽  
Vol 77 (7) ◽  
pp. 1149-1162 ◽  
Author(s):  
Benjamin A. Staton ◽  
Matthew J. Catalano ◽  
Brendan M. Connors ◽  
Lewis G. Coggins ◽  
Michael L. Jones ◽  
...  
Keyword(s):  
State Space ◽  
Oncorhynchus Tshawytscha ◽  
Pacific Salmon ◽  
State Space Model ◽  
Reference Points ◽  
State Space Models ◽  
Space Model ◽  
Trade Offs ◽  
Salmon Fisheries ◽  
Mixed Stock

Salmon populations harvested in mixed-stock fisheries can exhibit genotypic, behavioral, and life history diversity that can lead to heterogeneity in population productivity and size. Methods to quantify this heterogeneity among populations in mixed-stock fisheries are not well-established but are critical to assessing harvest–biodiversity trade-offs when setting harvest policies. We developed an integrated, age-structured, state-space model that allows for more complete use of available data and sharing of information than simpler methods. We compared a suite of state-space models of varying structural complexity to simpler regression-based approaches and, as an example case, fitted them to data from 13 Chinook salmon (Oncorhynchus tshawytscha) populations in the Kuskokwim drainage in western Alaska. We found biological and policy conclusions were largely consistent among state-space models but differed strongly from regression-based approaches. Simulation trials illustrated our state-space models were largely unbiased with respect to spawner–recruit parameters, abundance states, and derived biological reference points, whereas the regression-based approaches showed substantial bias. These findings suggest our state-space model shows promise for informing harvest policy evaluations of harvest–biodiversity trade-offs in mixed-stock salmon fisheries.

IDENTIFICATION OF ON-AND OFF-LINE LINEAR STATE SPACE MODELS USING SUBSPACE METHODS

2015 ◽  
Vol 77 (28) ◽  
Author(s):  
Nurul Syahirah Khalid ◽  
Norhaliza Abd. Wahab ◽  
Muhammad Iqbal Zakaria
Keyword(s):  
State Space ◽  
State Space Model ◽  
Model Identification ◽  
State Space Models ◽  
Subspace Methods ◽  
State Space System ◽  
Linear State ◽  
Value Decomposition ◽  
Error State

In this paper, subspace identification methods are proposed to analyze the differences between On-And Off-Line Linear State Space Models Using Subspace Methods. There are several ways that can estimate the order of the system. For this paper, Singular Value Decomposition (SVD) is used to estimate the order of the system. Comparing with the others methods, this method only need a limited number of input and output data for the determination of the system matrices. Two methods of the subspace algorithm are used which is N4SID (Numerical algorithm for Subspace State Space System Identification) and MOESP (Multivariable Output-Error State-Space model identification).

Stabilization of a Four-Wheel Mobile Robot From Kinematic Model to Dynamic Model by Feedback Passivation of Cascades Using Chained Form

2009 ◽  
Author(s):  
Hossein Mohammadi ◽  
Arash Haghpanah ◽  
Mohammad Eghtesad
Keyword(s):  
Mobile Robot ◽  
System Dynamics ◽  
Mobile Robots ◽  
State Space ◽  
Dynamic Model ◽  
Kinematic Model ◽  
State Space Model ◽  
Space Model ◽  
Novel Approach

In this paper, a novel approach for dynamics based stabilization of a four-wheel mobile robot is presented. One of the well-known and well-established approaches for stabilization of mobile robots is converting the kinematic model of the robot to a chained form. In order to extend this method to dynamic based stabilization, kinematic and dynamic subsystems of the mobile robot state-space model can be considered as two subsystems of a cascade and then feedback passivation of cascades can be utilized for stabilization of the whole system dynamics.

scholarly journals A review of Bayesian state-space modelling of capture–recapture–recovery data

2012 ◽  
Vol 2 (2) ◽  
pp. 190-204 ◽  
Author(s):  
Ruth King
Keyword(s):  
State Space ◽  
State Space Model ◽  
Model Fitting ◽  
The State ◽  
State Space Models ◽  
Observation Error ◽  
Space Model ◽  
System Process ◽  
Observation Process ◽  
Capture Recapture

Traditionally, state-space models are fitted to data where there is uncertainty in the observation or measurement of the system. State-space models are partitioned into an underlying system process describing the transitions of the true states of the system over time and the observation process linking the observations of the system to the true states. Open population capture–recapture–recovery data can be modelled in this framework by regarding the system process as the state of each individual observed within the study in terms of being alive or dead, and the observation process the recapture and/or recovery process. The traditional observation error of a state-space model is incorporated via the recapture/recovery probabilities being less than unity. The models can be fitted using a Bayesian data augmentation approach and in standard BUGS packages. Applying this state-space framework to such data permits additional complexities including individual heterogeneity to be fitted to the data at very little additional programming effort. We consider the efficiency of the state-space model fitting approach by considering a random effects model for capture–recapture data relating to dippers and compare different Bayesian model-fitting algorithms within WinBUGS.

scholarly journals Novel Method for State Space Modeling of Full Bridge Converter

2019 ◽  
Vol 8 (12) ◽  
pp. 4421-4426
Keyword(s):  
State Space ◽  
Power Conversion ◽  
State Space Model ◽  
Space Model ◽  
Space Modeling ◽  
Novel Method ◽  
Converter Design ◽  
Efficient Power ◽  
Wire Resistance ◽  
The Ideal

The requirement of converters is increasing with the increase in demand for the power conversion devices. For efficient power conversion, stability and time response of the system must be improved. For improving characteristics a mathematical model of the system must be determined. In this paper, an improvised state space model of full bridge converter is presented which can be used in converter design. This state-space model incorporates the non-idealities of the transformers like discharge time of primary inductance and secondary inductance as well as the wire resistance. The interdependency of the parameters affects the state space model of the converter compared to the ideal modeling. This variation in state space model of the converter has an impact on design of compensator which improves the system efficiency of the converter.

scholarly journals Efficient Forced Response Computations of Acoustical Systems with a State-Space Approach

Acoustics ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 581-594
Author(s):  
Art J. R. Pelling ◽  
Ennes Sarradj
Keyword(s):  
State Space ◽  
Large Scale ◽  
State Space Model ◽  
Measurement Data ◽  
Numerical Linear Algebra ◽  
Forced Response ◽  
State Space Models ◽  
High Dimensional ◽  
Diagonal Form ◽  
The Past

State-space models have been successfully employed for model order reduction and control purposes in acoustics in the past. However, due to the cubic complexity of the singular value decomposition, which makes up the core of many subspace system identification (SSID) methods, the construction of large scale state-space models from high-dimensional measurement data has been problematic in the past. Recent advances of numerical linear algebra have brought forth computationally efficient randomized rank-revealing matrix factorizations and it has been shown that these factorizations can be used to enhance SSID methods such as the Eigensystem Realization Algorithm (ERA). In this paper, we demonstrate the applicability of the so-called generalized ERA to acoustical systems and high-dimensional input data by means of an example. Furthermore, we introduce a new efficient method of forced response computation that relies on a state-space model in quasi-diagonal form. Numerical experiments reveal that our proposed method is more efficient than previous state-space methods and can even outperform frequency domain convolutions in certain scenarios.

scholarly journals State Space Modeling with Non-Negativity Constraints Using Quadratic Forms

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1908
Author(s):  
Ourania Theodosiadou ◽  
George Tsaklidis
Keyword(s):  
State Space ◽  
Quadratic Forms ◽  
Hidden Variables ◽  
State Space Model ◽  
Estimation Procedure ◽  
Positive Component ◽  
Filtering Algorithm ◽  
Space Model ◽  
Space Modeling ◽  
The Difference

State space model representation is widely used for the estimation of nonobservable (hidden) random variables when noisy observations of the associated stochastic process are available. In case the state vector is subject to constraints, the standard Kalman filtering algorithm can no longer be used in the estimation procedure, since it assumes the linearity of the model. This kind of issue is considered in what follows for the case of hidden variables that have to be non-negative. This restriction, which is common in many real applications, can be faced by describing the dynamic system of the hidden variables through non-negative definite quadratic forms. Such a model could describe any process where a positive component represents “gain”, while the negative one represents “loss”; the observation is derived from the difference between the two components, which stands for the “surplus”. Here, a thorough analysis of the conditions that have to be satisfied regarding the existence of non-negative estimations of the hidden variables is presented via the use of the Karush–Kuhn–Tucker conditions.

Responses comparison of the two discrete-time linear fractional state-space models

2016 ◽  
Vol 19 (4) ◽  
Author(s):  
Tadeusz Kaczorek ◽  
Piotr Ostalczyk
Keyword(s):  
State Space ◽  
State Space Model ◽  
State Space Models ◽  
Discrete State ◽  
Numerical Examples ◽  
Space Model ◽  
The Difference ◽  
Time Linear ◽  
Discrete State Space ◽  
Fundamental Matrices

AbstractIn this survey we consider two fractional-order discrete state-space models of linear systems. In both cases the crucial elements are the fundamental matrices. The difference between them is analyzed. A fundamental condition for the first state-space model is given. The investigations are illustrated by the numerical examples.

Hybrid State Space Modeling of a Spark Ignition Engine for Online Fault Diagnosis

2017 ◽  
Vol 140 (4) ◽  
Author(s):  
E. P. Nadeer ◽  
Amit Patra ◽  
Siddhartha Mukhopadhyay
Keyword(s):  
State Space ◽  
Gasoline Engine ◽  
State Space Model ◽  
Control Unit ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Intake Manifold ◽  
Hybrid State ◽  
Space Modeling ◽  
State Space Modeling

In this work, a nonlinear hybrid state space model of a complete spark ignition (SI) gasoline engine system from throttle to muffler is developed using the mass and energy balance equations. It provides within-cycle dynamics of all the engine variables such as temperature, pressure, and mass of individual gas species in the intake manifold (IM), cylinder, and exhaust manifold (EM). The inputs to the model are the same as that commonly exercised by the engine control unit (ECU), and its outputs correspond to available engine sensors. It uses generally known engine parameters, does not require extensive engine maps found in mean value models (MVMs), and requires minimal experimentation for tuning. It is demonstrated that the model is able to capture a variety of engine faults by suitable parameterization. The state space modeling is parsimonious in having the minimum number of integrators in the model by appropriate choice of state. It leads to great computational efficiency due to the possibility of deriving the Jacobian expressions analytically in applications such as on-board state estimation. The model was validated both with data from an industry standard engine simulation and those from an actual engine after relevant modifications. For the test engine, the engine speed and crank angle were extracted from the crank position sensor signal. The model was seen to match the true values of engine variables both in simulation and experiments.

Synthetic seismograms using the state‐space approach

Geophysics ◽  
1979 ◽  
Vol 44 (5) ◽  
pp. 880-895 ◽  
Author(s):  
J. M. Mendel ◽  
N. E. Nahi ◽  
M. Chan
Keyword(s):  
State Space ◽  
Time Domain ◽  
State Space Model ◽  
Layered Media ◽  
State Space Models ◽  
State Equations ◽  
Space Model ◽  
Domain State ◽  
The Time Domain ◽  
Treat All

We develop time‐domain state‐space models for lossless layered media which are described by the wave equation and boundary conditions. We develop state‐space models for two cases: (1) source and sensor at the surface, and (2) source and sensor in the first layer. Our models are for nonequal one‐way traveltimes; hence, they are more general than most existing models of layered media which are usually for layers of equal one‐way traveltimes. A notable exception to this is the work of Wuenschel (1960); however, most of the useful results even in his paper are developed only for the uniform traveltime case. Our state‐space model treat all of the equations that describe a layered‐media system together in the time domain. Earlier approaches (e.g., Wuenschel, 1960; Robinson, 1968) recursively connect adjacent layers by means of frequency‐domain relationships. We refer to our state equations as “causal functional equations.” They actually represent a new class of equations. Why are we interested in a different class of models for what appears to be a well‐studied system? As is well known, there is a vast literature associated with systems which are described by time‐domain state‐space models. Most recent results in estimation and identification theories, for example, require a state‐space model. These time‐domain techniques have proven very beneficial outside of the geophysics field and we feel should also be beneficial in the geophysics field. In fact, our ultimate objective is to apply those theories to the layered‐media problem; but, to do so, of course, requires state‐space models—hence, this paper.

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