Application of Recursive Subspace Method in Vehicle Lateral Dynamics Model Identification

Modeling of vehicle behavior based on the identification method has received a renewed attention in recent years. In order to improve the linear time-invariant vehicle identification model, a more general identifiable vehicle model structure is proposed, in which time-varying characteristics of vehicle speed and cornering stiffness are taken into consideration. To identify the proposed linear time-varying vehicle model, a well-established data-driven method, named recursive optimized version of predictor-based subspace identification, is introduced. Before vehicle model identification, the influences of four parameters in the subspace algorithm are studied based on pulse input road test. And then a set of practical optimal parameters are chosen and used for the vehicle model identification. Through a series of standard road tests under different maneuvers, the linear time-varying vehicle model can be identified in real-time. It is demonstrated by comparison that the predicted outputs of the proposed vehicle model are much closer to the real vehicle outputs and the model has a wider range of application.

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Modeling of linear time-varying systems by linear time-invariant systems of lower order

IEEE Transactions on Automatic Control ◽

10.1109/tac.1973.1100207 ◽

1973 ◽

Vol 18 (1) ◽

pp. 50-52 ◽

Cited By ~ 9

Author(s):

H. Nosrati ◽

H. Meadows

Keyword(s):

Lower Order ◽

Linear Time ◽

Time Varying ◽

Linear Time Invariant ◽

Time Varying Systems ◽

Linear Time Invariant Systems ◽

Time Invariant

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FE Analysis of Communications Systems for Drive-Thru Restaurants in a Business Dispute Over Specifications and Design Process

Journal of the National Academy of Forensic Engineers ◽

10.51501/jotnafe.v38i1.106 ◽

2021 ◽

Vol 38 (1) ◽

Author(s):

Robert Peruzzi

Keyword(s):

Communication System ◽

Communication Systems ◽

Linear Time ◽

Forensic Analysis ◽

Time Varying ◽

Audio Quality ◽

Linear Time Invariant ◽

Linear Time Invariant Systems ◽

Quick Service ◽

Time Invariant

Forensic analysis in this case involves the design of a communication system intended for use in Quick Service Restaurant (QSR) drive-thru lanes. This paper provides an overview of QSR communication system components and operation and introduces communication systems and channels. This paper provides an overview of non-linear, time-varying system design as contrasted with linear, time-invariant systems and discusses best design practices. It also provides the details of how audio quality was defined and compared for two potentially competing systems. Conclusions include that one of the systems was clearly inferior to the other — mainly due to not following design techniques that were available at the time of the project.

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Linear Approximations for Swing Leg Motion During Gait

Journal of Biomechanical Engineering ◽

10.1115/1.3138470 ◽

1984 ◽

Vol 106 (2) ◽

pp. 137-143 ◽

Cited By ~ 5

Author(s):

W. H. Lee ◽

J. M. Mansour

Keyword(s):

Linear Systems ◽

System Analysis ◽

Linear Time ◽

Time Varying ◽

Two Dimensional ◽

State Variables ◽

Linear Time Invariant ◽

Linear Time Invariant Systems ◽

Leg Motion ◽

Time Invariant

The applicability of a linear systems analysis of two-dimensional swing leg motion was investigated. Two different linear systems were developed. A linear time-varying system was developed by linearizing the nonlinear equations describing swing leg motion about a set of nominal system and control trajectories. Linear time invariant systems were developed by linearizing about three different fixed limb positions. Simulations of swing leg motion were performed with each of these linear systems. These simulations were compared to previously performed nonlinear simulations of two-dimensional swing leg motion and the actual subject motion. Additionally, a linear system analysis was used to gain some insight into the interdependency of the state variables and controls. It was shown that the linear time varying approximation yielded an accurate representation of limb motion for the thigh and shank but with diminished accuracy for the foot. In contrast, all the linear time invariant systems, if used to simulate more than a quarter of the swing phase, yielded generally inaccurate results for thigh shank and foot motion.

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Nonparametric estimation of the best linear time-invariant approximation of a linear time-varying system

52nd IEEE Conference on Decision and Control ◽

10.1109/cdc.2013.6760811 ◽

2013 ◽

Author(s):

R. Pintelon ◽

E. Louarroudi ◽

J. Lataire

Keyword(s):

Nonparametric Estimation ◽

Linear Time ◽

Time Varying ◽

Linear Time Invariant ◽

Invariant Approximation ◽

Time Invariant

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Floquet Experimental Modal Analysis for System Identification of Linear Time-Periodic Systems

Volume 5: 6th International Conference on Multibody Systems, Nonlinear Dynamics, and Control, Parts A, B, and C ◽

10.1115/detc2007-34790 ◽

2007 ◽

Cited By ~ 8

Author(s):

Matthew S. Allen

Keyword(s):

System Identification ◽

Linear Time ◽

Time Varying ◽

Response Model ◽

State Matrix ◽

Linear Time Invariant ◽

Response Data ◽

Time Varying Systems ◽

Time Invariant ◽

Time Periodic

A variety of systems can be faithfully modeled as linear with coefficients that vary periodically with time or Linear Time-Periodic (LTP). Examples include anisotropic rotorbearing systems, wind turbines, satellite systems, etc… A number of powerful techniques have been presented in the past few decades, so that one might expect to model or control an LTP system with relative ease compared to time varying systems in general. However, few, if any, methods exist for experimentally characterizing LTP systems. This work seeks to produce a set of tools that can be used to characterize LTP systems completely through experiment. While such an approach is commonplace for LTI systems, all current methods for time varying systems require either that the system parameters vary slowly with time or else simply identify a few parameters of a pre-defined model to response data. A previous work presented two methods by which system identification techniques for linear time invariant (LTI) systems could be used to identify a response model for an LTP system from free response data. One of these allows the system’s model order to be determined exactly as if the system were linear time-invariant. This work presents a means whereby the response model identified in the previous work can be used to generate the full state transition matrix and the underlying time varying state matrix from an identified LTP response model and illustrates the entire system-identification process using simulated response data for a Jeffcott rotor in anisotropic bearings.

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