Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

330
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
330
Linear time-invariant Systems01:23

Linear time-invariant Systems

856
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
856
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.1K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.1K
Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

27.2K
When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
27.2K
Kinematic Equations - II01:17

Kinematic Equations - II

12.9K
The second kinematic equation expresses the final position of an object in terms of its initial position, the distance traveled with the initial constant velocity, and the distance traveled due to a change in velocity. Similar to the first kinematic equation, this equation is also only valid when the acceleration is constant throughout the motion of an object.
Suppose a car merges into freeway traffic on a 200 m long ramp. If its initial velocity is 10 m/s and it accelerates at 2 m/s2, then the...
12.9K
Kinematic Equations - III01:18

Kinematic Equations - III

10.2K
The first two kinematic equations have time as a variable, but the third kinematic equation is independent of time. This equation expresses final velocity as a function of the acceleration and distance over which it acts. The fourth kinematic equation does not have an acceleration term and provides the final position of the object at time t in terms of the initial and final velocities. This equation is useful when the value of the constant acceleration is unknown.
Using the kinematic equations,...
10.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Risk factors analysis for extubation failure following mandibular distraction osteogenesis in infants with Pierre Robin sequence: a retrospective cohort study.

Frontiers in pediatrics·2026
Same author

Interface hyperfine coupling engineering of graphene oxide@MgAl-LDH architectures toward eco-efficient lithium extraction from salt-lake brines.

Journal of colloid and interface science·2026
Same author

Platelet proteome for predictive diagnosis and differentiation of sepsis and septic shock in pediatric patients.

PeerJ·2026
Same author

Self-amplifying ROS nanorobot with orthogonal NIR activation for enhanced photodynamic-chemodynamic combination therapy.

Biomaterials science·2026
Same author

Association between adjunctive corticosteroid therapy and clinical outcomes in children with severe viral community-acquired pneumonia: A retrospective cohort study.

Medicine·2026
Same author

General integration theory of cognitive emergent: Comment on "Brain dynamics shape cognition-spatiotemporal neuroscience" by Georg Northoff, Angelika Wolman and Jianfeng Zhang.

Physics of life reviews·2026

Related Experiment Video

Updated: Jan 12, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

2.1K

Kalman-Based Joint Estimation for Generalized Time-Varying Parameter Systems With the Unknown Invariant Matrix.

Ning Xu, Xiao Zhang, Ling Xu

    IEEE Transactions on Cybernetics
    |November 6, 2025
    PubMed
    Summary

    This study introduces a new state-space model for systems with time-varying parameters, enhancing estimation accuracy. The joint state estimation (JSE) algorithm reduces reliance on prior knowledge, proving reliable in simulations.

    More Related Videos

    Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis
    08:08

    Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

    Published on: May 8, 2014

    17.2K
    Experimental Methods to Study Human Postural Control
    08:12

    Experimental Methods to Study Human Postural Control

    Published on: September 11, 2019

    10.0K

    Related Experiment Videos

    Last Updated: Jan 12, 2026

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
    06:45

    Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

    Published on: October 28, 2022

    2.1K
    Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis
    08:08

    Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

    Published on: May 8, 2014

    17.2K
    Experimental Methods to Study Human Postural Control
    08:12

    Experimental Methods to Study Human Postural Control

    Published on: September 11, 2019

    10.0K

    Area of Science:

    • Control Systems Engineering
    • Signal Processing
    • System Identification

    Background:

    • Traditional state-space methods for time-varying systems often assume Markov evolution of parameters and require prior knowledge of the transfer matrix.
    • Existing approaches can be limited by the need for detailed information about parameter dynamics and system matrices.

    Purpose of the Study:

    • To develop a novel state-space modeling and estimation approach for systems with time-varying parameters.
    • To reduce the dependence on prior knowledge of the invariant matrix in parameter estimation.
    • To enhance the reliability and validity of estimation algorithms for dynamic systems.

    Main Methods:

    • Development of an explicit autoregressive (AR) model for time-varying parameters, where the invariant matrix captures parameter dynamics.
    • Construction of a state-space model by integrating the invariant matrix and time-varying parameters into the state vector.
    • Deduction of a joint state estimation (JSE) algorithm based on the Kalman filtering principle.

    Main Results:

    • Numerical simulations and Monte Carlo tests demonstrate the algorithm's reliability under various random white noise conditions.
    • The developed joint state estimation algorithm effectively estimates time-varying parameters without extensive prior knowledge.
    • Practical estimation results using real-time series data validate the proposed method's effectiveness.

    Conclusions:

    • The proposed state-space method and joint state estimation algorithm offer a robust approach for modeling and estimating systems with time-varying parameters.
    • The algorithm's reduced dependence on prior knowledge and demonstrated reliability make it suitable for real-world applications.
    • This work advances system identification techniques for dynamic systems with evolving characteristics.