scholarly journals Nonsmooth Recursive Identification of Sandwich Systems with Backlash-Like Hysteresis

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ruili Dong ◽  
Yonghong Tan ◽  
Hui Chen ◽  
Yangqiu Xie
Keyword(s):  
Estimation Algorithm ◽  
Dynamic Parameter ◽  
Bundle Method ◽  
Recursive Identification ◽  
Identification Algorithm ◽  
Nonsmooth Problem ◽  
Positioning Stage ◽  
Dynamic Parameter Estimation ◽  
Estimation Scheme ◽  
Sandwich System

A recursive gradient identification algorithm based on the bundle method for sandwich systems with backlash-like hysteresis is presented in this paper. In this method, a dynamic parameter estimation scheme based on a subgradient is developed to handle the nonsmooth problem caused by the backlash embedded in the system. The search direction of the algorithm is estimated based on the so-called bundle method. Then, the convergence of the algorithm is discussed. After that, simulation results on a nonsmooth sandwich system are presented to validate the proposed estimation algorithm. Finally, the application of the proposed method to anX-Ymoving positioning stage is illustrated.

Control-Oriented Vehicle Attitude Estimation With Online Sensors Bias Compensation

2009 ◽  
Author(s):  
Giulio Panzani ◽  
Matteo Corno ◽  
Mara Tanelli ◽  
Sergio M. Savaresi ◽  
Andrea Fortina ◽  
...  
Keyword(s):  
Estimation Algorithm ◽  
Industrial Applications ◽  
Stability Control ◽  
Attitude Estimation ◽  
Recursive Identification ◽  
Sideslip Angle ◽  
Vehicle Stability Control ◽  
Estimation Scheme ◽  
Offset Compensation ◽  
Sideslip Estimation

In advanced vehicle stability control systems, the availability of online estimates of the vehicle attitude is essential. In four-wheeled vehicles, attitude information is deeply linked to vehicle sideslip angle and sideslip rate, since these variables are strictly related to instability phenomena, safety and handling performances. As direct measurements of such quantities cannot be performed with standard sensors equipment, the design of robust and efficient estimators is needed. In this paper, we tackle the problem by proposing a sideslip rate observer and by demonstrating how an existing sideslip estimation algorithm can be made robust and reliable by online compensation of the sensors bias via a recursive identification approach. The proposed method does not require any additional sensor, making the final solution suitable for industrial applications. Experimental results confirm the effectiveness of the sensors offset compensation and witness satisfactory results of the overall vehicle attitude estimation scheme.

Tire Rolling Kinematics Model for an Intelligent Tire Based on an Accelerometer

2020 ◽  
Vol 48 (4) ◽  
pp. 287-314
Author(s):  
Yan Wang ◽  
Zhe Liu ◽  
Michael Kaliske ◽  
Yintao Wei
Keyword(s):  
Elastic Deformation ◽  
Estimation Algorithm ◽  
Force Sensor ◽  
Euler Method ◽  
Rolling Contact ◽  
Body Motion ◽  
Identification Algorithm ◽  
Active Perception ◽  
Kinematics Model ◽  
Ring Model

ABSTRACT The idea of intelligent tires is to develop a tire into an active perception component or a force sensor with an embedded microsensor, such as an accelerometer. A tire rolling kinematics model is necessary to link the acceleration measured with the tire body elastic deformation, based on which the tire forces can be identified. Although intelligent tires have attracted wide interest in recent years, a theoretical model for the rolling kinematics of acceleration fields is still lacking. Therefore, this paper focuses on an explicit formulation for the tire rolling kinematics of acceleration, thereby providing a foundation for the force identification algorithms for an accelerometer-based intelligent tire. The Lagrange–Euler method is used to describe the acceleration field and contact deformation of rolling contact structures. Then, the three-axis acceleration vectors can be expressed by coupling rigid body motion and elastic deformation. To obtain an analytical expression of the full tire deformation, a three-dimensional tire ring model is solved with the tire–road deformation as boundary conditions. After parameterizing the ring model for a radial tire, the developed method is applied and validated by comparing the calculated three-axis accelerations with those measured by the accelerometer. Based on the features of acceleration, especially the distinct peak values corresponding to the tire leading and trailing edges, an intelligent tire identification algorithm is established to predict the tire–road contact length and tire vertical load. A simulation and experiments are conducted to verify the accuracy of the estimation algorithm, the results of which demonstrate good agreement. The proposed model provides a solid theoretical foundation for an acceleration-based intelligent tire.

A Hardware Implementation of an Online Frequency Dynamic Parameter Estimation

2021 ◽  
Author(s):  
J. Quiroz ◽  
R. Perez ◽  
H. Chavez ◽  
Julia Matevosyan ◽  
Felix Rafael Segundo Sevilla
Keyword(s):  
Parameter Estimation ◽  
Hardware Implementation ◽  
Dynamic Parameter ◽  
Dynamic Parameter Estimation

Research of Fan System Identification Based on Improved Bayes Algorithm

2011 ◽  
Vol 66-68 ◽  
pp. 448-453
Author(s):  
Hai Tao Wang ◽  
Ze Zhang
Keyword(s):  
System Identification ◽  
Dynamic Characteristics ◽  
System Modeling ◽  
Estimation Algorithm ◽  
Parametric Model ◽  
Testing System ◽  
Identification Algorithm ◽  
Environment Protection ◽  
Parameter Estimation Algorithm ◽  
Bayes Algorithm

In every filed of natural science, more and more researchers attach importance to system quantitative analysis, control and prediction. In filed of automatic control, system identification is the extension of system dynamic characteristics testing. System modeling is the basis of system identification, non-parametric model can be obtained by means of dynamic characteristics testing, but parametric model must be established by means of parameter estimation algorithm, which is more prevalent than dynamic characteristics testing. Coal power plant produces more gas and dust, so how to control the fan system plays a very important role in environment protection. We must clarify the parameter of fan system before controlling it. The traditional Bayes identification algorithm is used widely in research and industry, and the effect is relatively good. The paper induces the concept of loss function based on traditional Bayes identification algorithm, and proposes an improved Bayes identification algorithm, which can be applied to fan system identification successfully.

Developing data fusion and recursive estimation methods for online identification of dive plane dynamics of an autonomous underwater vehicle

2019 ◽  
Vol 234 (2) ◽  
pp. 520-533
Author(s):  
Seid Farhad Abtahi ◽  
Mohammad Mehdi Alishahi ◽  
Ehsan Azadi Yazdi
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Unscented Kalman Filter ◽  
Underwater Vehicle ◽  
Recursive Estimation ◽  
High Rate ◽  
Pole Placement ◽  
Estimation Methods ◽  
Recursive Identification ◽  
Identification Algorithm ◽  
Hydrodynamic Coefficients

The purpose of this article is to develop an online method to identify the hydrodynamic coefficients of pitch plane of an autonomous underwater vehicle. To obtain necessary data for the identification, the dive plane dynamics should be excited through diving maneuvers. Hence, a controller is needed whose performance and stability are appropriate. To design such a controller, first, hydrodynamic coefficients are approximated using semi-empirical methods. Based on these approximated coefficients, a classic controller is designed at the next step. Since the estimation of these coefficients is uncertain, µ-analysis is employed to verify the robustness of stability and performance of the controller. Using the verified robust controller, some oscillating maneuvers are carried out that excite the dive plane dynamics. Using sensor fusion and unscented Kalman filter, smooth and high-rate data of depth is provided for the depth controller. A recursive identification algorithm is developed to identify the hydrodynamic coefficients of heave and pitch motions. It turns out that some inputs required by the identification are not measured directly by the sensors. But the devised fusion algorithm is able to provide the necessary data for identification. Finally, using the identified coefficients and employing pole placement method, a new controller with better performance is synthesized online. To evaluate the performance of the identification and fusion algorithms, a 6-degree-of-freedom simulation of an autonomous underwater vehicle is carried out.

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