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

Controller Configurations01:22

Controller Configurations

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 aligns...
PD Controller: Design01:26

PD Controller: Design

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,...
Reinforcement01:23

Reinforcement

Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Open and closed-loop control systems01:17

Open and closed-loop control systems

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 and...
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.

You might also read

Related Articles

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

Sort by
Same author

Fully Actuated System Approach-Based Tracking Control for High-Order Nonlinear System Under False Data Injection and Malicious Attacks.

IEEE transactions on cybernetics·2026
Same author

Nonfragile Fault-Tolerant Control for Power Cyber-Physical Systems With Cyber Attacks.

IEEE transactions on cybernetics·2025
Same author

Novel SMC for Discrete Interval Type-2 Fuzzy Semi-Markovian Switching Models With Incomplete Semi-Markovian Kernel.

IEEE transactions on cybernetics·2025
Same author

Adaptive Fuzzy Control of Networked Hidden Stochastic Switching Power Systems Under Cyber Attacks.

IEEE transactions on cybernetics·2025
Same author

A Sliding Mode Control Method With Variable Convergence Rate for Nonlinear Impulsive Stochastic Systems.

IEEE transactions on cybernetics·2025
Same author

Event-Triggered Extended Dissipative FTB for T-S Fuzzy Switched Systems With Mismatched Phenomena and Deception Attacks: A Multidomain Framework.

IEEE transactions on cybernetics·2024

Related Experiment Videos

Embedding fuzzy mechanisms and knowledge in box-type reinforcement learning controllers.

Shun-Feng Su1, Sheng-Hsiung Hsieh

  • 1Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|February 5, 2008
PubMed
Summary

This study enhances fuzzy reinforcement learning controllers by addressing credit assignment and weighting issues. Incorporating knowledge into the control network improves learning, unlike using it solely for evaluation.

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Control Systems Engineering

Background:

  • Previous fuzzy reinforcement learning approaches suffered from low success rates.
  • Identified issues include credit assignment and weighting domination problems.
  • Fuzzy mechanisms in temporal difference learning negatively impacted performance.

Purpose of the Study:

  • To embed fuzzy mechanisms and knowledge into box-type reinforcement learning controllers.
  • To address identified problems in fuzzy reinforcement learning.
  • To investigate the impact of knowledge integration on learning performance.

Main Methods:

  • Proposed modifications to overcome credit assignment and weighting domination problems.
  • Applied remedies to a fuzzy learning control scheme and studied variations.
  • Investigated knowledge incorporation into both control and evaluation networks.
  • Utilized Makarovic's (1988) rules for initial control network settings, grouped to avoid ordering issues.

Main Results:

  • Modified fuzzy controllers demonstrated improved performance compared to previous methods.
  • Knowledge integration in the control network yielded significant learning improvements.
  • Knowledge integration solely in the evaluation network showed no substantial advantages.
  • Grouping Makarovic's rules effectively managed the ordering problem.

Conclusions:

  • Embedding fuzzy mechanisms with proposed modifications enhances reinforcement learning controller performance.
  • Knowledge integration is beneficial for the control network in reinforcement learning.
  • Further research could explore optimal knowledge representation and integration strategies.