Multi-step Identification Algorithm for the Multi-sensor System with Unknown Parameters

2016 ◽  
Author(s):  
Heng Li ◽  
Huifen Sun
Keyword(s):  
Sensor System ◽  
Identification Algorithm ◽  
Unknown Parameters

scholarly journals Two Identification Methods for Dual-Rate Sampled-Data Nonlinear Output-Error Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jing Chen ◽  
Ruifeng Ding
Keyword(s):  
Stochastic Gradient ◽  
The Other ◽  
Identification Algorithm ◽  
Sampled Data ◽  
Unknown Parameters ◽  
Auxiliary Model ◽  
Identification Methods ◽  
Numerical Example ◽  
Output Error ◽  
Polynomial Transformation

This paper presents two methods for dual-rate sampled-data nonlinear output-error systems. One method is the missing output estimation based stochastic gradient identification algorithm and the other method is the auxiliary model based stochastic gradient identification algorithm. Different from the polynomial transformation based identification methods, the two methods in this paper can estimate the unknown parameters directly. A numerical example is provided to confirm the effectiveness of the proposed methods.

scholarly journals Dynamic Model Identification for 6-DOF Industrial Robots

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Li Ding ◽  
Hongtao Wu ◽  
Yu Yao ◽  
Yuxuan Yang
Keyword(s):  
Robot Manipulator ◽  
Industrial Robot ◽  
Model Identification ◽  
Friction Model ◽  
Industrial Robots ◽  
Parameters Identification ◽  
Identification Algorithm ◽  
Unknown Parameters ◽  
Joint Friction ◽  
Dynamical Parameters

A complete and systematic procedure for the dynamical parameters identification of industrial robot manipulator is presented. The system model of robot including joint friction model is linear with respect to the dynamical parameters. Identification experiments are carried out for a 6-degree-of-freedom (DOF) ER-16 robot. Relevant data is sampled while the robot is tracking optimal trajectories that excite the system. The artificial bee colony algorithm is introduced to estimate the unknown parameters. And we validate the dynamical model according to torque prediction accuracy. All the results are presented to demonstrate the efficiency of our proposed identification algorithm and the accuracy of the identified robot model.

Expectation Maximization Method to Identify an Electrically Stimulated Musculoskeletal Model

2015 ◽  
Author(s):  
Harish Ravichandar ◽  
Ashwin Dani ◽  
Jacquelyn Khadijah-Hajdu ◽  
Nicholas Kirsch ◽  
Qiang Zhong ◽  
...  
Keyword(s):  
Dynamic Model ◽  
Expectation Maximization ◽  
Control Design ◽  
Knee Extension ◽  
Musculoskeletal Model ◽  
Effective Control ◽  
Identification Algorithm ◽  
Knee Angle ◽  
Unknown Parameters ◽  
Angle Measurements

A system identification algorithm for a musculoskeletal system using an approximate expectation maximization (E-M) is presented. Effective control design for neuroprosthesis applications necessitates a well defined muscle model. A dynamic model of the lower leg with a fixed ankle is considered. The unknown parameters of the model are estimated using an approximate E-M algorithm based on knee angle measurements collected from an able-bodied subject during stimulated knee extension. The parameters estimated from the data are compared to reference values obtained by conducting experiments that separate the parameters in the dynamics from one another. The presented results demonstrate the capability of the proposed algorithm to identify the parameters of the dynamic model from knee angle measurements.

Identification of Linear Time-Varying Parameters of Nonstationary Systems

2019 ◽  
Vol 20 (5) ◽  
pp. 269-265
Author(s):  
V. T. Le ◽  
M. M. Korotina ◽  
A. A. Bobtsov ◽  
S. V. Aranovskiy ◽  
Q. D. Vo
Keyword(s):  
Linear Time ◽  
Control Object ◽  
Time Interval ◽  
Identification Algorithm ◽  
Time Varying ◽  
State Variables ◽  
Unknown Parameters ◽  
Technical Problems ◽  
True Values ◽  
Stationary Control

The paper considers the identification algorithm for unknown parameters of linear non-stationary control objects. It is assumed that only the object output variable and the control signal are measured (but not their derivatives or state variables) and unknown parameters are linear functions or their derivatives are piecewise constant signals. The derivatives of non-stationary parameters are supposed to be unknown constant numbers on some time interval. This assumption for unknown parameters is not mathematical abstraction because in most electromechanical systems parameters are changing during the operation. For example, the resistance of the rotor is linearly changing, because the resistance of the rotor depends on the temperature changes of the electric motor in operation mode. This paper proposes an iterative algorithm for parameterization of the linear non-stationary control object using stable LTI filters. The algorithm leads to a linear regression model, which includes time-varying and constant (at a certain time interval) unknown parameters. For this model, the dynamic regressor extension and mixing (DREM) procedure is applied. If the persistent excitation condition holds, then, in the case the derivative of each parameter is constant on the whole time interval, DREM provides the convergence of the estimates of configurable parameters to their true values. In the case of a finite time interval, the estimates convergence in a certain region. Unlike well-known gradient approaches, using the method of dynamic regressor extension and mixing allows to improve the convergence speed and accuracy of the estimates to their true values by increasing the coefficients of the algorithm. Additionally, the method of dynamic regressor extension and mixing ensures the monotony of the processes, and this can be useful for many technical problems.

Parameters Identification Strategy for a Generalized Vehicle Concept Model for Ride and Handling Analysis

2013 ◽  
Author(s):  
Mariano Carpinelli ◽  
Marco Gubitosa ◽  
Domenico Mundo ◽  
Wim Desmet
Keyword(s):  
Degrees Of Freedom ◽  
Early Stage ◽  
Parameters Identification ◽  
Least Square ◽  
Identification Algorithm ◽  
Vehicle Concept ◽  
Unknown Parameters ◽  
Multibody Model ◽  
Concept Model ◽  
Predecessor Model

In this paper we propose a structured approach for the parameters identification of a multibody vehicle concept model to be used for the combined analysis of vertical and longitudinal dynamics. The model here proposed adopts eight degrees of freedom in the space. The wheels are connected to the sprung mass in an equivalent trailing arm configuration thus enabling to reproduce the squat and dive phenomena. This conceptual suspension representation allows determining the dynamic response of the vehicle during longitudinal acceleration or braking maneuvers. The identification procedure here suggested evaluates the unknown parameters of the model, being the global stiffness and damping coefficients of the suspensions and the positions of the pivot points of the trailing arms. The identification algorithm is based on non-linear least square costs that can be computed by having as reference the signals of a measurement campaign which is conducted on a real vehicle as well as on a virtual predecessor model. The results here shown make use of virtually measured quantities coming from ride maneuvers performed by means of a high fidelity multibody model of a passenger car. The presented concept model, showing good correlation with respect to the reference signals, is suggested as a reliable prediction and optimization tool in the early stage of the design phase of new vehicles.

scholarly journals Online Dynamic Load Identification Based on Extended Kalman Filter for Structures with Varying Parameters

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1372
Author(s):  
Hongqiu Li ◽  
Jinhui Jiang ◽  
M Shadi Mohamed
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Dynamic Load ◽  
Degrees Of Freedom ◽  
Symmetric System ◽  
Identification Algorithm ◽  
Structure Parameters ◽  
Load Identification ◽  
Unknown Parameters ◽  
Varying Parameters

Dynamic load identification is an inverse problem concerned with finding the load applied on a structure when the dynamic characteristics and the response of the structure are known. In engineering applications, some of the structure parameters such as the mass or the stiffness may be unknown and/or may change in time. In this paper, an online dynamic load identification algorithm based on an extended Kalman filter is proposed. The algorithm not only identifies the load by measuring the structural response but also identifies the unknown structure parameters and tracks their changes. We discuss the proposed algorithm for the cases when the unknown parameters are the stiffness or the mass coefficients. Furthermore, for a system with many degrees of freedom and to achieve online computations, we implement the model reduction theory. Thus, we reduce the number of degrees of freedom in the resulting symmetric system before applying the proposed extended Kalman filter algorithm. The algorithm is used to recover the dynamic loads in three numerical examples. It is also used to identify the dynamic load in a lab experiment for a structure with varying parameters. The simulations and the experimental results show that the proposed algorithm is effective and can simultaneously identify the parameters and any changes in them as well as the applied dynamic load.

Fault Detection and Identification in an IM270 Gas Turbine Using Measurements for Engine Control

2003 ◽  
Author(s):  
Masahiro Kurosaki ◽  
Tadashi Morioka ◽  
Kosuke Ebina ◽  
Masatoshi Maruyama ◽  
Tomoshige Yasuda ◽  
...  
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Identification Algorithm ◽  
Gradual Change ◽  
Engine Control ◽  
Unknown Parameters ◽  
Engine Model ◽  
Detection And Identification ◽  
Fault Detection And Identification ◽  
Parameter Deviation

A unique fault detection and identification algorithm using measurements for engine control use is presented. The algorithm detects an engine fault and identifies the associated component, using a gas path analysis technique with a detailed nonlinear engine model. The algorithm is intended to detect step-like changes in component performance rather than gradual change of all components. Since simultaneous multiple faults are unlikely, a single component fault is assumed, which reduces the number of unknown parameters to less than two. By setting the number of adjustable parameters to that of the available measurements, the parameters are computed using an engine model. After computing all of the six possible combinations of adjustable parameters, the average magnitude of the parameter deviation vectors is used to detect an engine fault. Component performance deviation (efficiency and flow rate) is represented by a magnitude and a phase. The phase is selected to minimize the error of matrices consisting of normalized adjustable parameter deviation vectors. Then the magnitude is computed by the average magnitude ratio of the vectors. Since the algorithm is simple, it is easily applied to newly developed engines. A fault detection and identification program was specifically developed for IM270 engine, a single shaft gas turbine with 2MW output capacity. By utilizing operational data obtained at a remote monitoring center, the algorithm was able to quantitatively identify the compressor and the turbine performance deviation. Although the algorithm correctly identifies the turbine as the faulty component, there remains some ambiguity. Analysis of linear dependency of the measurement deviation vectors shows that identification capability varies with phase. There are several phases where identification is impossible in the current IM270 sensor system.

Parameter Estimation for Wiener Systems with Preload Nonlinearities

2014 ◽  
Vol 989-994 ◽  
pp. 1460-1463
Author(s):  
Yun Xia Ni ◽  
Jian Dong Cao
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Recursive Least Squares ◽  
Switching Function ◽  
Identification Algorithm ◽  
Unknown Parameters ◽  
Recursive Least Squares Algorithm ◽  
Wiener Systems ◽  
Least Squares Algorithm ◽  
Identification Model

This paper proposes a recursive least squares algorithm for Wiener systems. We use a switching function to turn the modelof the nonlinear Wiener systems into an identification model, then propose a recursive least squares identification algorithm toestimate all the unknown parameters of the systems. Finally, an example is provided to show the effectiveness of the proposed algorithm.

Automatic Sensor Frame Identification in Industrial Robots With Joint Elasticity

2013 ◽  
Author(s):  
Chung-Yen Lin ◽  
Wenjie Chen ◽  
Masayoshi Tomizuka
Keyword(s):  
Optimization Problems ◽  
Side Information ◽  
Expectation Maximization Algorithm ◽  
Industrial Robots ◽  
Optimal Placement ◽  
Identification Algorithm ◽  
Unknown Parameters ◽  
Product Cycles ◽  
Task Variation ◽  
Joint Elasticity

For robots with joint elasticity, discrepancies exist between the motor side information and the load side (i.e., end-effector) information. Therefore, high tracking performance at the load side can hardly be achieved when the estimate of load side information is inaccurate. To minimize such inaccuracies, it is desired to calibrate the load side sensor (in particular, the exact sensor location). In practice, the optimal placement of the load side sensor often varies due to the task variation necessitating frequent sensor calibrations. This frequent calibration need requires significant effort and hence is not preferable for industries which have relatively short product cycles. To solve this problem, this paper presents a sensor frame identification algorithm to automate this calibration process for the load side sensor, in particular the accelerometer. We formulate the calibration problem as a nonlinear estimation problem with unknown parameters. The Expectation-Maximization algorithm is utilized to decouple the state estimation and the parameter estimation into two separated optimization problems. An overall dual-phase learning structure associated with the proposed approach is also studied. Experiments are designed to validate the effectiveness of the proposed algorithm.

scholarly journals Identification, Estimation, and Control for Linear Uncertain Systems Using Measurements of Higher-Order Derivatives

2017 ◽  
Vol 139 (12) ◽  
Author(s):  
Zilong Shao ◽  
Gang Zheng ◽  
Denis Efimov ◽  
Wilfrid Perruquetti
Keyword(s):  
Uncertain Systems ◽  
Initial Conditions ◽  
Higher Order ◽  
The State ◽  
Identification Algorithm ◽  
Unknown Parameters ◽  
State Variable ◽  
Higher Order Derivative ◽  
Estimation And Control ◽  
And Control

In this paper, the problem of output control for linear uncertain systems with external perturbations is studied. First, it is assumed that the output available for measurement is only the higher-order derivative of the state variable, instead of the state variable itself (for example, the acceleration for a second-order plant), and the measurement is also corrupted by noise. Then, via series of integration, an identification algorithm is proposed to identify all unknown parameters of the model and all unknown initial conditions of the state vector. Finally, two control algorithms are developed, adaptive and robust; both provide boundedness of trajectories of the system. The efficiency of the obtained solutions is demonstrated by numerical simulation.

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