Estimation of parameters and control of systems with unknown parameters

2005 ◽  
pp. 118-126
Author(s):  
S.Ya. Mahno
Keyword(s):  
Estimation Of Parameters ◽  
Unknown Parameters ◽  
And Control

Procedure to Estimate Thermophysical and Geometrical Parameters of Embedded Cancerous Lesions Using Thermography

2012 ◽  
Vol 134 (3) ◽  
Author(s):  
Jose Manuel Luna ◽  
Ricardo Romero-Mendez ◽  
Abel Hernandez-Guerrero ◽  
Francisco Elizalde-Blancas
Keyword(s):  
Research Work ◽  
Blood Perfusion ◽  
Skin Surface ◽  
Geometrical Parameters ◽  
Estimation Of Parameters ◽  
Perfusion Rate ◽  
Unknown Parameters ◽  
Cancerous Lesions ◽  
The Relationship ◽  
Inverse Procedure

Based on the fact that malignant cancerous lesions (neoplasms) develop high metabolism and use more blood supply than normal tissue, infrared thermography (IR) has become a reliable clinical technique used to indicate noninvasively the presence of cancerous diseases, e.g., skin and breast cancer. However, to diagnose cancerous diseases by IR, the technique requires procedures that explore the relationship between the neoplasm characteristics (size, blood perfusion rate and heat generated) and the resulting temperature distribution on the skin surface. In this research work the dual reciprocity boundary element method (DRBEM) has been coupled with the simulated annealing technique (SA) in a new inverse procedure, which coupled to the IR technique, is capable of estimating simultaneously geometrical and thermophysical parameters of the neoplasm. The method is of an evolutionary type, requiring random initial values for the unknown parameters and no calculations of sensitivities or search directions. In addition, the DRBEM does not require any re-meshing at each proposed solution to solve the bioheat model. The inverse procedure has been tested considering input data for simulated neoplasms of different sizes and positions in relation to the skin surface. The successful estimation of unknown neoplasm parameters validates the idea of using the SA technique and the DRBEM in the estimation of parameters. Other estimation techniques, based on genetic algorithms or sensitivity coefficients, have not been capable of obtaining a solution because the skin surface temperature difference is very small.

scholarly journals State estimation and control for a parabolic system with unknown parameters.

1987 ◽  
Vol 13 (4) ◽  
pp. 525-528 ◽  
Author(s):  
Shigeru Matsumoto ◽  
Masatoshi Yoshida
Keyword(s):  
State Estimation ◽  
Parabolic System ◽  
Unknown Parameters ◽  
Estimation And Control ◽  
And Control

New Results on Optimal Joint Parameter and State Estimation of Linear Stochastic Systems

1980 ◽  
Vol 102 (1) ◽  
pp. 28-34 ◽  
Author(s):  
G. Salut ◽  
J. Aguilar-Martin ◽  
S. Lefebvre
Keyword(s):  
Stochastic Systems ◽  
Optimal Estimation ◽  
Canonical Representation ◽  
Parameters Estimation ◽  
Space Representation ◽  
Estimation Of Parameters ◽  
Unknown Parameters ◽  
State Space Representation ◽  
Classical State ◽  
Linear Stochastic Systems

In this paper a complete presentation of a new canonical representation of multiinput, multioutput linear stochastic systems is given. Its equivalence with operator form directly linked with ARMA processes as well as with classical state space representation is given, and a transfer matrix interpretation is developed in an example. The importance of the new representation is mainly in the fact that in the joint state and parameters estimation problem, all unknown parameters appear linearly when an input-output record is available. Moreover, if noises are Gaussian and their statistics are known, a conditionally time varying Kalman-Bucy type filter gives the recursive optimal estimation of parameters and state. Historical comments and remarks about the adaptive version of this algorithm are given. Finally an illustrative low order example is described.

Simultaneous learning and control of parallel Stewart platforms with unknown parameters

2017 ◽  
Vol 266 ◽  
pp. 114-122 ◽  
Author(s):  
Mohammed Aquil Mirza ◽  
Shuai Li ◽  
Long Jin
Keyword(s):  
Unknown Parameters ◽  
Stewart Platforms ◽  
And Control

On the Dynamics and Control of a Full Wrist Exoskeleton for Tremor Alleviation

2019 ◽  
Author(s):  
Jiamin Wang ◽  
Oumar Barry ◽  
Andrew J. Kurdila ◽  
Sujith Vijayan
Keyword(s):  
Controller Design ◽  
Human Motion ◽  
Structural Function ◽  
Wrist Flexion ◽  
Unknown Parameters ◽  
Ulnar Deviation ◽  
Marquardt Algorithm ◽  
Flexion Extension ◽  
Dynamics And Control ◽  
And Control

Abstract This paper introduces a novel wearable full wrist exoskeleton designed for the alleviation of tremor in patients suffering from Parkinson’s Disease and Essential Tremor. The design introduces a structure to provide full observation of wrist kinematics as well as actuation in wrist flexion/extension and radial/ulnar deviation. To examine the feasibility of the design, the coupled dynamics of the device and the forearm is modeled via a general multibody framework. The dynamic analysis considers human motion, wrist stiffness, and tremor dynamics. The analysis of the model reveals that the identification of the wrist kinematics is indispensable for the controller design. Nonlinear regression based on the Levenberg-Marquardt algorithm has been applied to estimate the unknown parameters in a kinematic structural function designed to approximate the wrist kinematics, which leads to the construction of the control system framework. Finally, several simulation cases are demonstrated to conclude the study.

Strongly consistent estimation in a controlled Markov renewal model

1982 ◽  
Vol 19 (03) ◽  
pp. 532-545 ◽  
Author(s):  
Michael Kolonko
Keyword(s):  
Optimal Control ◽  
Dynamic Models ◽  
Consistent Estimation ◽  
Unknown Parameters ◽  
Control Approach ◽  
Renewal Model ◽  
Countable State ◽  
Estimation And Control ◽  
And Control ◽  
Strongly Consistent

The optimal control of dynamic models which are not completely known to the controller often requires some kind of estimation of the unknown parameters. We present conditions under which a minimum contrast estimator will be strongly consistent independently of the control used. This kind of estimator is appropriate for the adaptive or ‘estimation and control' approach in dynamic programming under uncertainty. We consider a countable-state Markov renewal model and we impose bounding and recurrence conditions of the so-called Liapunov type.

scholarly journals Aquaponics Systems Modelling

2019 ◽  
pp. 267-299 ◽  
Author(s):  
Karel J. Keesman ◽  
Oliver Körner ◽  
Kai Wagner ◽  
Jan Urban ◽  
Divas Karimanzira ◽  
...  
Keyword(s):  
Theoretical Perspective ◽  
Daily Practice ◽  
Model Complexity ◽  
Estimation Of Parameters ◽  
Systems Model ◽  
Main Challenge ◽  
Prediction And Control ◽  
Systems Modelling ◽  
And Control ◽  
Fish Tanks

AbstractMathematical models can take very different forms and very different levels of complexity. A systematic way to postulate, calibrate and validate, as provided by systems theory, can therefore be very helpful. In this chapter, dynamic systems modelling of aquaponic (AP) systems, from a systems theoretical perspective, is considered and demonstrated to each of the subsystems of the AP system, such as fish tanks, anaerobic digester and hydroponic (HP) greenhouse. It further shows the links between the subsystems, so that in principle a complete AP systems model can be built and integrated into daily practice with respect to management and control of AP systems. The main challenge is to choose an appropriate model complexity that meets the experimental data for estimation of parameters and states and allows us to answer questions related to the modelling objective, such as simulation, experiment design, prediction and control.

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