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

Control Systems01:10

Control Systems

1.2K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.2K
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

153
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
153
Open and closed-loop control systems01:17

Open and closed-loop control systems

837
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
837
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

88
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
88
Feedback control systems01:26

Feedback control systems

356
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
356
Load-frequency control01:28

Load-frequency control

212
Load-frequency control (LFC) is vital for maintaining power system stability, ensuring that frequency and power flows remain within acceptable limits during load changes. Turbine-governor control eliminates rotor accelerations and decelerations following load changes. However, a steady-state frequency error persists when the change in the turbine-governor reference setting is zero. In an interconnected power system, each area agrees to export or import a scheduled amount of power through...
212

You might also read

Related Articles

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

Sort by
Same author

Repetitive Transcranial Magnetic Stimulation for Major Depressive Disorder: A Systematic Review and Network Meta-Analysis.

Journal of evidence-based medicine·2026
Same author

Cannabidiol for Mucosal Diseases: Therapeutic Potential and Advanced Delivery Strategies.

Pharmaceutics·2026
Same author

Universal diseased-site targeting via glycolysis-driven lactic acid gradient.

Science advances·2026
Same author

Association of Musculoskeletal Pain With Foot Type, Plantar Pressure, Physical Activity, Psychosocial Factors, and Daily Living Habits Among Adolescents.

Clinical medicine insights. Arthritis and musculoskeletal disorders·2026
Same author

Full-polarization and high-coherence thermal radiation from magnetic photonic crystals realized by anisotropic saddle-band design at terahertz frequencies.

Optics express·2026
Same author

Demographic and Screening-to-Diagnosis Cascade Indicators for Risk Stratification in an Organized Cervical Cancer Screening Program: Evidence from 82,141 Women in Yunnan, China.

International journal of women's health·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 30, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

5.0K

Energy-Optimal Adaptive Control Based on Model Predictive Control.

Yuxi Li1, Gang Hao1,2

  • 1School of Electronic Engineering, Heilongjiang University, Harbin 150080, China.

Sensors (Basel, Switzerland)
|May 13, 2023
PubMed
Summary
This summary is machine-generated.

Energy-optimal adaptive cruise control (EACC) significantly reduces energy consumption. This study introduces an improved model predictive control (MPC) that enhances safety and tracking performance by integrating advanced filtering and neural networks.

Keywords:
artificial neural networkcubature Kalman filterenergy-optimal cruise controlmodel predictive control (MPC)

More Related Videos

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.8K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.1K

Related Experiment Videos

Last Updated: Jul 30, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
08:18

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

5.0K
An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
10:51

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

13.8K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.1K

Area of Science:

  • Automotive Engineering
  • Control Systems Theory
  • Artificial Intelligence

Background:

  • Energy-optimal adaptive cruise control (EACC) is gaining traction for its fuel-saving benefits.
  • System noise negatively impacts EACC performance and reliability.
  • Existing control strategies require enhancement to address noise and optimize energy efficiency.

Purpose of the Study:

  • To develop an improved modified model predictive control (MPC) for EACC systems.
  • To mitigate the adverse effects of system noise on EACC performance.
  • To enhance energy savings, safety, and tracking accuracy.

Main Methods:

  • Integration of Sage-Husa adaptive Kalman filter (SHAKF), cubature Kalman filter (CKF), and back-propagation neural network (BPNN) within an MPC framework.
  • Development of a novel control algorithm combining these advanced techniques.
  • Simulation-based validation of the proposed EACC system.

Main Results:

  • The proposed MPC algorithm demonstrates superior energy-saving capabilities compared to existing methods.
  • The system effectively maintains appropriate relative distance and speed with the leading vehicle.
  • Enhanced safety and tracking performance were observed under noisy conditions.

Conclusions:

  • The novel MPC approach effectively addresses system noise in EACC.
  • The integrated SHAKF, CKF, and BPNN strategy significantly improves energy efficiency.
  • The proposed algorithm offers a robust and effective solution for advanced adaptive cruise control.