Determining the UAV State Space Rotational Dynamics Model Using Algebraic Inversion Technique

2017 ◽  
Author(s):  
Jemie Muliadi ◽  
Benyamin Kusumoputro
Keyword(s):  
State Space ◽  
Rotational Dynamics ◽  
Dynamics Model ◽  
Inversion Technique

Stewart Platform Model with Uncertain Parameters

2010 ◽  
Vol 164 ◽  
pp. 177-182 ◽  
Author(s):  
Lukas Březina ◽  
Tomáš Březina
Keyword(s):  
State Space ◽  
Controller Design ◽  
State Space Model ◽  
Stewart Platform ◽  
Dynamics Model ◽  
H Infinity ◽  
Uncertain Dynamics ◽  
Six Dof ◽  
Linear State ◽  
Nominal Position

The paper deals with development of uncertain dynamics model of a six DOF parallel mechanism (Stewart platform) suitable for H-infinity controller design. The model is based on linear state space models of the machine obtained by linearization of the SimMechanics model. The linearization is performed for two positions of the machine in its workspace. It is the nominal position and the position where each link of the machine reaches its maximal length. The uncertainties are then represented as differences between parameters of corresponding state-space matrices. The uncertain state space model is then obtained using upper linear fractional transformation. There are also mentioned several notes regarding H-infinity controller designed according to the obtained model.

Model-Based Inversion Technique Using Element-Free Galerkin Method and State Space Search

2009 ◽  
Vol 45 (3) ◽  
pp. 1486-1489 ◽  
Author(s):  
Xin Liu ◽  
Yiming Deng ◽  
Zhiwei Zeng ◽  
L. Udpa ◽  
S.S. Udpa
Keyword(s):  
Galerkin Method ◽  
State Space ◽  
Element Free Galerkin Method ◽  
Model Based ◽  
Inversion Technique ◽  
Element Free Galerkin ◽  
State Space Search ◽  
Element Free

scholarly journals A curved manifold orients rotational dynamics in motor cortex

2021 ◽  
Author(s):  
David A. Sabatini ◽  
Matthew T. Kaufman
Keyword(s):  
Motor Cortex ◽  
State Space ◽  
Population Level ◽  
Pattern Generation ◽  
Rotational Dynamics ◽  
Simple Relationship ◽  
Time Varying ◽  
Neural Responses ◽  
Complex Time ◽  
Time Courses

SummaryControlling arm movements requires complex, time-varying patterns of muscle activity 1,2. Accordingly, the responses of neurons in motor cortex are complex, time-varying, and heterogeneous during reaching 2–4. When examined at the population level, patterns of neural activity evolve over time according to dynamical rules 5,6. During reaching, these rules have been argued to be “rotational” 7 or variants thereof 8,9, containing coordinated oscillations in the spike rates of individual neurons. While these models capture key aspects of the neural responses, they fail to capture others – accounting for only 20-50% of the neural response variance. Here, we consider a broader class of dynamical models. We find that motor cortex dynamics take an unexpected form: there were 3-4 rotations at fixed frequencies in M1 and PMd explaining more than 90% of neural responses, but these rotations occurred in different portions of state space when movements differ. These rotations appear to reflect a curved manifold of fixed points in state space, around which dynamics are locally rotational. These fixed-frequency rotations obeyed a simple relationship with movement: the orientation of rotations in motor cortex activity were related almost linearly to the movement the animal made, allowing linear decoding of reach kinematic time-courses on single trials. This model constitutes a fundamentally novel way to consider pattern generation: like placing a record player in a large bowl, the frequency of activity is fixed, but the location of motor cortex activity on a curved manifold sets the orientation of locally-rotational dynamics. This system simplifies motor control, helps reconcile conflicting frameworks for interpreting motor cortex, and enables greatly improved neural decoding.

scholarly journals General state-space population dynamics model for Bayesian stock assessment

2015 ◽  
Vol 72 (8) ◽  
pp. 2209-2222 ◽  
Author(s):  
Samu H. P. Mäntyniemi ◽  
Rebecca E. Whitlock ◽  
Tommi A. Perälä ◽  
Paul A. Blomstedt ◽  
Jarno P. Vanhatalo ◽  
...  
Keyword(s):  
Population Dynamics ◽  
State Space ◽  
Stock Assessment ◽  
Life Stage ◽  
Individual Case ◽  
Biological Knowledge ◽  
Model Parameters ◽  
Biological Processes ◽  
Dynamics Model ◽  
Population Dynamics Model

Abstract This study presents a state-space modelling framework for the purposes of stock assessment. The stochastic population dynamics build on the notion of correlated survival and capture events among individuals. The correlation is thought to arise as a combination of schooling behaviour, a spatially patchy environment, and common but unobserved environmental factors affecting all the individuals. The population dynamics model isolates the key biological processes, so that they are not condensed into one parameter but are kept separate. This approach is chosen to aid the inclusion of biological knowledge from sources other than the assessment data at hand. The model can be tailored to each case by choosing appropriate models for the biological processes. Uncertainty about the model parameters and about the appropriate model structures is then described using prior distributions. Different combinations of, for example, age, size, phenotype, life stage, species, and spatial location can be used to structure the population. To update the prior knowledge, the model can be fitted to data by defining appropriate observation models. Much like the biological parameters, the observation models must also be tailored to fit each individual case.

scholarly journals Fitting state–space models to seal populations with scarce data

2014 ◽  
Vol 72 (5) ◽  
pp. 1462-1469
Author(s):  
Tor Arne Øigård ◽  
Hans J. Skaug
Keyword(s):  
Population Dynamics ◽  
State Space ◽  
Barents Sea ◽  
State Space Model ◽  
The State ◽  
Harp Seal ◽  
Dynamics Model ◽  
Greenland Sea ◽  
Space Model ◽  
Population Dynamics Model

Abstract We estimate temporal variation in fecundity, the reproduction rate, for Barents Sea and Greenland Sea harp seals using a state–space approach. A stochastic process model for fecundity is integrated with an age-structured population dynamics model and fit to available data for these two harp seal populations. Owing to scarceness of data, it is necessary to “borrow strength” from the Northwest Atlantic harp seal population in form of prior distributions on autocorrelation and variance in fecundity. Comparison is made to a simpler deterministic population dynamics model. The state–space model is more flexible and is able to account for the variations in the data. For Barents Sea harp seals, the state–space model gives a higher estimate of current population size but also a much higher associated uncertainty. In the Greenland Sea, the differences between the stochastic and deterministic models are much smaller.

Rotational dynamics and velocity segregation in plastic KCIO4: a molecular dynamics simulation

1998 ◽  
Vol 93 (5) ◽  
pp. 703-711
Author(s):  
F. AFFOUARD ◽  
PH. DEPONDT
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Rotational Dynamics ◽  
Dynamics Simulation

Hybrid state-space self-tuning control using dual-rate sampling

1991 ◽  
Vol 138 (1) ◽  
pp. 50 ◽  
Author(s):  
Leang S. Shieh ◽  
Xiao M. Zhao ◽  
John W. Sunkel
Keyword(s):  
State Space ◽  
Hybrid State ◽  
Self Tuning
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