Posted in | News | Industrial Robotics

Enhancing Industrial Robot Performance Through Time-Lag Filtering

In a recent paper published in the journal Machines, researchers have proposed a novel method for reducing vibrations in industrial robots through time-lag filtering based on trapezoidal trajectory (T-trajectory) interpolation. Their technique aims to enhance the accuracy and stability of robot motion by reducing the amplitude of harmonic components within specific frequency bands.

Enhancing Industrial Robot Performance Through Time-Lag Filtering
Study: Enhancing Industrial Robot Performance Through Time-Lag Filtering. Image Credit: Gorodenkoff/

Additionally, the researchers compared this approach with other vibration suppression methods, including closed-loop control-based dynamic feedforward, showcasing its effectiveness in terms of accuracy, real-time performance, and cost.


Industrial robots are extensively utilized in manufacturing due to their high productivity, cost-effectiveness, and safety features. However, a significant challenge in robot control is the presence of vibrations, which can adversely impact the robot's performance, work stability, product quality, and durability.

Vibrations in industrial robots can arise from several sources, including structural flexibility, the joint drive system, load variations, resonance frequencies, changes in mechanical structure rigidity, and high-speed motion. These vibrations can cause positional deviations in the robot's end-effector, lead to instability, accelerate wear and tear on mechanical components, reduce product quality, and compromise worker safety. Consequently, vibration suppression has become a crucial area of focus in industrial robotics research.


Traditional methods for suppressing vibrations in industrial robots encompass mechanical design improvements, advanced control systems, and strategic material selection. These strategies are designed to reduce or eliminate vibrations by increasing the rigidity of the robot's structure, integrating damping materials or shock absorbers, using sensors alongside feedback controllers, applying predictive or adaptive control algorithms, incorporating active vibration suppression devices, and optimizing control inputs and trajectories. However, these approaches can face challenges in terms of accuracy, real-time performance, and cost-effectiveness.

About the Research

In this study, the authors introduced an innovative technique for effectively suppressing vibration in industrial robots based on time-lag filtering with T-trajectory interpolation. They selected a frequency band according to the robot body's vibration characteristics and designed a related time-lag filter. The filtered trajectory could decrease vibration amplitude, potentially minimizing vibration to zero under ideal conditions.

The time-lag filtering technique is especially suitable for incorporation into the trajectory planning component of a robot controller. This approach can directly improve performance via software algorithms without modifying the controller's overall hardware and software architecture. The principle of time-lag filtering involves using a finite impulse response (FIR) filter to shape the input signal and suppress the harmonic components in a specific frequency band.

Additionally, the researchers presented a method for T-trajectory interpolation, a technique aimed at ensuring accurate, smooth, and efficient motion execution when a robot follows a T-trajectory. T-trajectory interpolation involves using a smooth curve to generate the robot's position, velocity, and acceleration at each moment, thereby preventing robot instability during path switching. This technique inserts additional points in the robot's path to achieve the required motion.

The proposed technique combines the time-lag filtering method with the T-trajectory interpolation method to dynamically adjust the trajectory output and reduce the vibration amplitude of the robot. Moreover, the study introduces a straight-line method to measure the degree of vibration and compares the proposed method with other traditional methods through experiments.

Furthermore, the authors conducted experiments on a SIASUN 20 kg flexible robot (SIASUN, Shenyang, China) to test the effectiveness of the proposed method in suppressing vibration. They utilized a dynamic signal test analyzer, a laser tracker, and a micrometer to measure the robot's vibration trajectory, accuracy, and stability. Additionally, they compared the proposed method with two other methods: one based on closed-loop control of feedforward dynamics and the other based on predictive control. The study also introduced a new concept of "average amplitude ratio" (AAR) as a measure of the vibration suppression effect.

Research Findings

The outcomes showed that the proposed technique significantly reduced the vibration of the industrial robot and improved its motion accuracy and stability. It decreased the AAR of vibration from 0.841 and 0.498 to 0.306 compared to classical methods, indicating a more effective vibration suppression effect. The authors emphasized that the proposed method enhanced the robot's vibration performance. It also improved the robot's straightness, demonstrating its ability to maintain trajectory planning, achieving a straightness of 0.001 mm compared to the dynamic feedforward method's 0.005 mm.

The novel method can be applied to various industrial robot applications requiring high precision and efficiency, such as drilling, milling, welding, and assembly. By enhancing robot performance and reliability, it can meet the growing demands of modern manufacturing. Additionally, the method can offer valuable insights into vibration issues across different fields, fostering interdisciplinary application and technological advancement.


In summary, the researchers new method efficiently reduced the vibration of industrial robots and enhanced their reliability and accuracy in task performance. It effectively addressed the demand for precision and efficiency in modern manufacturing, improving overall robot performance.

However, the researchers acknowledged several limitations and challenges, including the delay induced by time-lag filtering and the optimization of filter parameters. Moving forward, they proposed directions to enhance and expand their methods, such as applying them to different robotic systems and various application scenarios.

Journal Reference

Liu, S.; Wu, C.; Liang, L.; Zhao, B.; Sun, R. Research on Vibration Suppression Methods for Industrial Robot Time-Lag Filtering. Machines 2024, 12, 250.,

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Muhammad Osama

Written by

Muhammad Osama

Muhammad Osama is a full-time data analytics consultant and freelance technical writer based in Delhi, India. He specializes in transforming complex technical concepts into accessible content. He has a Bachelor of Technology in Mechanical Engineering with specialization in AI & Robotics from Galgotias University, India, and he has extensive experience in technical content writing, data science and analytics, and artificial intelligence.


Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Osama, Muhammad. (2024, April 25). Enhancing Industrial Robot Performance Through Time-Lag Filtering. AZoRobotics. Retrieved on June 13, 2024 from

  • MLA

    Osama, Muhammad. "Enhancing Industrial Robot Performance Through Time-Lag Filtering". AZoRobotics. 13 June 2024. <>.

  • Chicago

    Osama, Muhammad. "Enhancing Industrial Robot Performance Through Time-Lag Filtering". AZoRobotics. (accessed June 13, 2024).

  • Harvard

    Osama, Muhammad. 2024. Enhancing Industrial Robot Performance Through Time-Lag Filtering. AZoRobotics, viewed 13 June 2024,

Tell Us What You Think

Do you have a review, update or anything you would like to add to this news story?

Leave your feedback
Your comment type

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.