A mission oriented accident model based on hybrid dynamic system

2012 ◽  
Author(s):  
Jian Jiao ◽  
Tingdi Zhao
Keyword(s):  
Dynamic System ◽  
Hybrid Dynamic System ◽  
Model Based

Control Electronics and Hybrid Dynamic System-Based API for a 6-DOF Desktop Haptic Interface

1999 ◽  
Author(s):  
S. E. Salcudean ◽  
R. Six ◽  
R. Barman ◽  
S. Kingdon ◽  
I. Chau ◽  
...  
Keyword(s):  
Dynamic System ◽  
Haptic Interface ◽  
State Transitions ◽  
Hybrid Dynamic System ◽  
Finite State ◽  
Control Electronics ◽  
Six Degree Of Freedom ◽  
Electronic Hardware ◽  
Device Location ◽  
Programming Interface

Abstract A six-degree-of-freedom desktop magnetically levitated haptic interface has been developed by the authors. Its electromechanical design is described in (Salcudean and Parker, 1997). In this paper, aspects of electronic hardware architecture and the control of actuator currents are discussed. To program this device, a new low level applications programming interface (API) that models the haptic interface as a hybrid dynamic system is proposed. The user can define a finite state machine in which every state is a device impedance. State transitions occur upon the satisfaction of linear inequalities in terms of the device location, velocity and force. Examples of the use of such hybrid dynamic systems to produce haptic effects are given.

Nonlinear Model Reduction for Automotive System Descriptions

2001 ◽  
Author(s):  
Zeyu Liu ◽  
John Wagner
Keyword(s):  
Dynamic System ◽  
Model Reduction ◽  
Nonlinear Model ◽  
Order System ◽  
Control Algorithms ◽  
Model Based Control ◽  
Reduction Strategy ◽  
Nonlinear Model Reduction ◽  
Model Based ◽  
Model Free

Abstract The mathematical modeling of dynamic systems is an important task in the design, analysis, and implementation of advanced automotive control systems. Although most vehicle control algorithms tend to use model-free calibration architectures, a need exists to migrate to model-based control algorithms which offer greater operating performance. However, in many instances, the analytical descriptions are too complex for real-time powertrain and chassis model-based control algorithms. Therefore, model reduction strategies may be applied to transform the original model into a simplified lower-order form while preserving the dynamic characteristics of the original high-order system. In this paper, an empirical gramian balanced nonlinear model reduction strategy is examined for the simplification process of dynamic system descriptions. The empirical gramians may be computed using either experimental or simulation data. These gramians are then balanced and unimportant system dynamics truncated. For comparison purposes, a Taylor Series linearization will also be introduced to linearize the original nonlinear system about an equilibrium operating point and then a balanced realization linear reduction strategy will be applied. To demonstrate the functionality of each model reduction strategy, two nonlinear dynamic system models are investigated and respective transient performances compared.

High Accuracy Behavior Prediction of Nonlinear Dynamic System with Semi-Parametric Model-Based Signal Separation

2020 ◽  
pp. 2151003
Author(s):  
Wang Jun
Keyword(s):  
Dynamic System ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic System ◽  
Parametric Model ◽  
Signal Separation ◽  
Support Vector ◽  
Behavior Prediction ◽  
Complex Dynamic ◽  
Complex Dynamic System ◽  
Model Based

The behavior prediction of nonlinear dynamic system is a challenging problem, especially when the system includes many independent subsystems. The observations from the complex dynamic system are the result of the interaction of multiple dynamic subsystems, which results in a loss of predictability. In this paper, semi-parametric model-based signal separation technique, in which validity function with penalizing is used to estimate the component number of the Gaussian mixture model (GMM) for every hidden source signal, is adopted to separate the observations of complex nonlinear dynamic system in order to improve its predictability. Then local support vector regression (SVR) technique is used to model the separated observations and make prediction. Finally, the prediction results are remixed as the original observation prediction or the behavior prediction of the complex nonlinear dynamic system. The experimental results show that the proposed method can separate the observation of the complex dynamic system robustly, improve the prediction accuracy substantially and perform better than the other comparison methods.

A dynamic system model-based technique for functional MRI data analysis

NeuroImage ◽  
2004 ◽  
Vol 22 (1) ◽  
pp. 179-187 ◽  
Author(s):  
Masayuki Kamba ◽  
Yul-Wan Sung ◽  
Seiji Ogawa
Keyword(s):  
Data Analysis ◽  
Dynamic System ◽  
Functional Mri ◽  
System Model ◽  
Model Based ◽  
Dynamic System Model
Sign in / Sign up

Export Citation Format

Share Document