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Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
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Three-Dimensional Force System01:30

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In mechanical engineering, a three-dimensional force system is a system of forces acting in three dimensions, with forces applied along the x, y, and z coordinate axes. The three-dimensional force system is an important concept in mechanical engineering, as it allows engineers to understand and analyze the behavior of objects and structures in three dimensions. By understanding the forces acting on a system, engineers can design more efficient and effective mechanical systems that can withstand...
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Two-Dimensional Force System: Problem Solving01:29

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Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
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A two-dimensional system in mechanical engineering involves the analysis of motion and forces in a plane. A two-dimensional force vector can be resolved into its components as:
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Static and Kinetic Frictional Force01:05

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One of the simpler characteristics of sliding friction is that it is parallel to the contact surfaces between systems, and is always in a direction that opposes the motion or attempted motion of the systems relative to each other. If two systems are in contact and moving relative to one another, then the friction between them is called kinetic friction. For example, kinetic friction slows a hockey puck sliding on ice.
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One-Degree-of-Freedom System01:24

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In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
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Related Experiment Video

Updated: Mar 15, 2026

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs
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Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

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Deep Learning-Based Contact Force Control for a Robotic Leg.

Hyoseok Lee1, Dongmin Baek2, Hyeokjun Kwon1

  • 1Department of Robot and Smart System Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

This study introduces a deep neural network (DNN) controller for stable humanoid robot walking. The novel DNN-based approach significantly improves contact force control, reducing errors and settling time compared to traditional methods.

Keywords:
admittancedeep learningforce controlrobot controlrobot learning

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Area of Science:

  • Robotics
  • Control Systems
  • Machine Learning

Background:

  • Stable contact force control is crucial for humanoid robot balance during bipedal locomotion.
  • Admittance controllers, commonly used for this purpose, struggle with the inherent nonlinearities of robot systems.

Purpose of the Study:

  • To develop a learning-based contact force controller that overcomes the limitations of traditional admittance controllers.
  • To improve the stability and performance of force control in humanoid robots.

Main Methods:

  • A deep neural network (DNN) was employed as an inverse model to capture system nonlinearities using input-output data.
  • A proportional-integral (PI) controller was integrated to minimize steady-state errors.
  • The controller computes target foot height based on force and height measurements, without needing a dynamic robot model.

Main Results:

  • The proposed DNN-based controller demonstrated significant performance improvements over an admittance controller.
  • Overshoot was reduced by an average of 96% and settling time by 61% in step responses.
  • Root-mean-square error (RMSE) for force tracking decreased by an average of 66.3% across step and sinusoidal experiments.

Conclusions:

  • The learning-based contact force controller effectively enhances stability and reduces errors in humanoid robot locomotion.
  • This approach offers a robust alternative to traditional methods for controlling contact forces in complex robotic systems.