Adaptive PID sliding mode control based on new Quasi-sliding mode and radial basis function neural network for Omni-directional mobile robot
Open accessAbstract
This article designs a PID sliding mode controller based on new Quasi-sliding mode (PID-SMC-NQ) and radial basis function neural network (RBFNN) for Omni-directional mobile robot. This is holonomic vehicles that can perform translational and rotational motions independently and simultaneously. The PID-SMC is designed to ensure that the robot's actual trajectory follows the desired in a finite time with the error converges to zero. To decrease chattering phenomena around the sliding surface, in the controller robust term, this paper uses the tanh (hyperbolic tangent) function, so called the new Quasi-sliding mode function, instead of the switch function. The RBFNN is used to approximate the nonlinear component in the PID-SMC-NQ controller. The RBFNN is considered as an adaptive controller. The weights of the network are trained online due to the feedback from output signals of the robot using the Gradient Descent algorithm. The stability of the system is proven by Lyapunov's theory. Simulation results in MATLAB/Simulink show the effectiveness of the proposed controller, the actual response of the robot converges to the reference with the rising time reaches 307.711 ms, 364.192 ms in the x-coordinate in the two-dimensional movement of the robot, the steady-state error is 0.0018 m and 0.00007 m, the overshoot is 0.13% and 0.1% in the y-coordinate, and the chattering phenomena is reduced.
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The PID controller depicts decent accomplishment in overcoming various nonlinearities, such as the chattering effect, delay, and external disturbances present in robotic industries.
“The PID controller depicts decent accomplishment in overcoming various nonlinearities, such as the chattering effect, delay, and external disturbances present in robotic industries [14].”
From A comparative study on time-delay estimation for time-delay nonlinear system control (2025) · cites this paper as [14]
The Abstract and Conclusions report reduced chattering for a PID sliding-mode controller combined with a quasi-sliding-mode function and an RBF neural network. Section 4 reports robust trajectory tracking under simulated sensor noise and parameter changes. The paper does not show that a PID controller alone overcomes these effects or demonstrate that the proposed controller overcomes communication delay; delay appears only as a problem mentioned in the Introduction. Page numbers are unavailable in our copy. (LLM, conf=0.87)
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Flagged first, then by citations
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Cited by
Sorted by citations
A comparative study on time-delay estimation for time-delay nonlinear system control
Nahri, S. N. F., Department of Electrical Engineering, F. O. E. A. T. B. E., Du, S., van Wyk, B. J., & Ajayi, O. K. (2025) · AIMS Electronics and Electrical Engineering · doi:10.3934/electreng.2025015 · Cited by 3 · see citing works
Design of an Omnidirectional Mobile Robot Capable of Launching Projectiles
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An Extended PID Controller for Automatic Control System
Молдабекович, М. М., Zhumabekova, N., Aden, A., Orazaly, Y., Batenov, A., & Tilektes, N. (2026) · Mathematics · doi:10.3390/math14132351
- Authors
- Pham, T. T., & Nguyen, C.-N.
- Affiliations
- Vinh Long University of Technology Education (VN)
- Can Tho University (VN)
- Corresponding author
- Nguyen, Chi-Ngon
- ORCID
- Nguyen, Chi-Ngon
- Venue
- AIMS Electronics and Electrical Engineering
- Published
- 2023
- Volume
- 7
- Issue
- 2
- Pages
- 121-134
- ISSN
- 2578-1588 (ISSN-L)
- OpenAlex
- W4361800930
- Cited by
- 3
- References
- 16
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 1
- FWCI
- 0.32
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by-nc-sa
- Locations
- AIMS Electronics and Electrical Engineering (publishedVersion, cc-by-nc-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Greater South Information System
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Control and Dynamics of Mobile Robots
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Engineering › Automotive Engineering › Vehicle Dynamics and Control Systems
- Keywords
- quasi-sliding mode, radial basis function neural network, omnidirectional mobile robot, chattering reduction, online neural network training, gradient descent, Lyapunov stability, trajectory tracking, finite-time convergence
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