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

PD Controller: Design01:26

PD Controller: Design

444
In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
444
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

231
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...
231
Controller Configurations01:22

Controller Configurations

218
Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...
218
PID Controller01:19

PID Controller

361
Proportional-Integral-Derivative (PID) controllers are widely used in various control systems to enhance stability and performance. In a thermostat, it adjusts heating or cooling based on the temperature difference between the actual and desired levels. They are often used in automotive speed systems, effectively managing sudden speed changes while maintaining a constant speed under varying conditions. On the other hand, PI controllers, commonly employed in voltage regulation, enhance stability...
361
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

243
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
243
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

888
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
888

You might also read

Related Articles

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

Sort by
Same author

Multi-Target Synergy of Coridius chinensis in Testicular Aging via Network Pharmacology-Metabolomics Integration.

Current medicinal chemistry·2026
Same author

A Knowledge-Guided Weight Optimization Method Based on Augmented Lagrangian for Active Suspension Preview Control.

IEEE transactions on cybernetics·2026
Same author

Factors affecting the gut microbiota of rural residents under different physical activity levels: a cross-sectional study.

Frontiers in microbiology·2026
Same author

<i>Lactobacillus plantarum</i> IOB602 and Its Postbiotics Attenuate Hypertension-Induced Damage by Modulating the RAS, PI3K/AKT/eNOS Pathways, and Gut Microbiota.

Foods (Basel, Switzerland)·2026
Same author

A meta-reasoning and dual-level cognitive decision-making approach for blind-spot safety in autonomous driving.

ISA transactions·2026
Same author

From symptom tracking to prevention - A transformer-based dynamic model for predicting mild cognitive impairment risk in older adults with depression: A longitudinal study based on CHARLS and CLHLS.

Medicine·2026

Related Experiment Video

Updated: Nov 11, 2025

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

1.9K

Autonomous drift controller for distributed drive electric vehicle with input coupling and uncertain disturbance.

Xiaohui Hou1, Junzhi Zhang1, Yuan Ji1

  • 1School of Vehicle and Mobility, Tsinghua University, Beijing, China.

ISA Transactions
|March 24, 2021
PubMed
Summary

This study introduces an autonomous drift controller for electric vehicles, enhancing dynamic control. The novel controller achieves stable vehicle drift maneuvers with proven robustness and fast response.

Keywords:
Autonomous drift controllerDistributed drive electric vehicleFuzzy-integral sliding-mode controllerInput couplingUncertain disturbance

More Related Videos

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.9K
Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

784

Related Experiment Videos

Last Updated: Nov 11, 2025

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

1.9K
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.9K
Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

784

Area of Science:

  • Automotive Engineering
  • Control Systems
  • Robotics

Background:

  • Autonomous vehicles require expanded dynamic control capabilities for advanced maneuvers.
  • Drifting presents unique challenges due to vehicle instability and complex dynamics.
  • Distributed drive electric vehicles offer a flexible platform for exploring novel control strategies.

Purpose of the Study:

  • To develop a novel autonomous drift controller for distributed drive electric vehicles.
  • To enhance the dynamic control envelope and application range of autonomous vehicles through drifting.
  • To address system complexities like input coupling and uncertain disturbances in drift control.

Main Methods:

  • Implemented a two-level control architecture: upper-level and lower-level controllers.
  • Utilized control channel recombination to convert an over-actuated system into a standard square system.
  • Designed a fuzzy-integral sliding-mode controller for the upper level, accounting for system uncertainties.
  • Developed a lower-level controller to manage actuator dynamics and distribute virtual control inputs.

Main Results:

  • The controller successfully enabled autonomous drift maneuvers in simulations and bench tests.
  • Demonstrated fast response and strong robustness of the proposed control system.
  • Validated the effectiveness of the control channel recombination and fuzzy-integral sliding-mode control strategies.

Conclusions:

  • The novel autonomous drift controller significantly expands the dynamic capabilities of electric vehicles.
  • The proposed control strategy is robust and provides fast responses, suitable for complex maneuvers.
  • This research paves the way for more versatile and dynamic autonomous vehicle applications.