Intelligent S-Curve Acceleration and Deceleration Algorithm in High-Precision Servo Motion Control

To address the issues of vibration in high-speed machining and the challenge of balancing motion smoothness and precision, this paper proposes a cascade control method based on a single-neuron adaptive PID. The method employs a dual closed-loop structure with a position loop and a speed loop, each regulated by a single-neuron adaptive PI controller. By dynamically adjusting the connection weights of the neurons online, real-time tuning of the proportional and integral parameters is achieved, enabling the system to adaptively regulate the control action. Simulation and experimental results demonstrate that the proposed controller ensures a 100% positioning accuracy across diverse motion scenarios with less than 0.05% relative error, enables effectively smooth motion, and effectively suppresses machine tool vibration caused by acceleration and deceleration processes. This significantly improves the system’s dynamic response and motion smoothness, providing an effective solution for high-speed and high-precision machining control.

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Publication Details

Journal
Machines
Published
2026-01-13
DOI
https://doi.org/10.3390/machines14010091
Primary Topic
Iterative Learning Control Systems
Type
article
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0.00
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article

Intelligent S-Curve Acceleration and Deceleration Algorithm in High-Precision Servo Motion Control

Tiansong Zhai, Feng Liu, Nian Li, Xu Yang et al.
Machines
Iterative Learning Control Systems
article

Intelligent S-Curve Acceleration and Deceleration Algorithm in High-Precision Servo Motion Control

Tiansong Zhai, Feng Liu, Nian Li, Xu Yang, Shaoyu Zhao, Lei Xiong
article en

Abstract

To address the issues of vibration in high-speed machining and the challenge of balancing motion smoothness and precision, this paper proposes a cascade control method based on a single-neuron adaptive PID. The method employs a dual closed-loop structure with a position loop and a speed loop, each regulated by a single-neuron adaptive PI controller. By dynamically adjusting the connection weights of the neurons online, real-time tuning of the proportional and integral parameters is achieved, enabling the system to adaptively regulate the control action. Simulation and experimental results demonstrate that the proposed controller ensures a 100% positioning accuracy across diverse motion scenarios with less than 0.05% relative error, enables effectively smooth motion, and effectively suppresses machine tool vibration caused by acceleration and deceleration processes. This significantly improves the system’s dynamic response and motion smoothness, providing an effective solution for high-speed and high-precision machining control.

MachinesVol. 14(1)
Chinese Academy of Sciences (CN), Henan Energy & Chemical Industry Group (China) (CN), Nanyang Institute of Technology (CN), Institute of Solid State Physics (CN)
Affordable and clean energy
Openalex Percentile: Top 88%
Iterative Learning Control Systems
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