Advancements in STSMC: Fuzzy Logic-Based Time-Varying Sliding Surfaces for Robust Control

Abstract

This paper presents an in-depth exploration of Super Twisting Sliding Mode Control (STSMC) combined with Fuzzy Logic Control (FLC) and time-varying sliding surfaces (SS) to enhance the efficiency and robustness of control systems. Traditional Sliding Mode Control (SMC) techniques suffer from chattering effects and sensitivity to disturbances, limiting their applicability in high-performance control systems. The integration of the Super Twisting Algorithm (STA) with FLC and adaptive time-varying SS offers improved disturbance rejection, reduced steady-state error, and faster convergence rates. The mathematical modeling, simulations, and experimental validation presented in this study demonstrate the advantages of this integrated approach. The paper further discusses implementation challenges and future research directions aimed at optimizing real-time performance and scalability.

Introduction

The study presents an advanced control system that integrates Super Twisting Sliding Mode Control (STSMC) with a Fuzzy Logic Controller (FLC) and time-varying sliding surfaces to improve system robustness, reduce chattering, and enhance adaptability.

Key Points:

• Conventional Control Limitations: Traditional controllers like PID struggle under uncertainties and disturbances. Standard SMC provides robustness but suffers from chattering.

• STSMC Overview: A second-order sliding mode control method that minimizes chattering by using a continuous control signal, maintaining robustness without causing actuator wear.

• Fuzzy Logic Integration:

  • Introduces intelligent adaptability using linguistic rules.

  • Dynamically adjusts the sliding surface and control gains.

  • Improves response, reduces overshoot, and enhances disturbance rejection.

• Time-Varying Sliding Surface (TVSS):

  • The sliding surface changes over time to improve transient and steady-state performance.

  • Adjusted by fuzzy logic based on real-time error and error rate.

• Mathematical Model:

  • Describes SMC and STSMC equations.

  • Demonstrates Lyapunov-based stability for the fuzzy-STSMC system.

• FLC Components:

  • Inputs: Error and its derivative.

  • Output: Time-varying adjustment of the sliding surface.

  • Includes fuzzification, inference, and defuzzification stages with rule-based decision making.

• Implementation & Simulation:

  • Implemented in MATLAB/Simulink on a nonlinear dynamic system.

  • Performance metrics: Tracking error, chattering amplitude, robustness, and computation time.

  • Results show improved tracking, reduced chattering, better robustness, and faster convergence compared to conventional SMC.

• Challenges:

  • High computational demands for real-time fuzzy processing.

  • Complexity in tuning fuzzy rules.

  • Practical implementation requires efficient embedded hardware.

Conclusion

This study highlights the effectiveness of integrating STSMC with FLC and time-varying SS to enhance the robustness and adaptability of control systems. The combined approach successfully mitigates chattering, improves transient response, and enhances overall system stability. Future research should explore real-time optimization techniques and practical implementations in diverse engineering applications.

References

[1] V. Kumara and E. Ganesan, \"A Novel Approach to Wastewater Treatment Control: A Self-Organizing Fuzzy Sliding Mode Controller,\" International Journal of Fuzzy Logic and Intelligent Systems, vol. 24, no. 4, pp. 440-450, Dec. 2024. [2] V. Kumara and E. Ganesan, \"Optimized Robust Fuzzy Sliding Mode Control for Efficient Wastewater Treatment: A Comprehensive Study,\" International Journal of Artificial Intelligence (IJ-AI), vol. 13, no. 4, pp. 1-10, Dec. 2024. [3] A. Levant, \"Sliding Order and Sliding Accuracy in Sliding Mode Control,\" International Journal of Control, vol. 58, no. 6, pp. 1247-1263, Dec. 1993. [4] L. Fridman and A. Levant, \"Higher Order Sliding Modes,\" in Sliding Mode Control in Engineering, W. Perruquetti and J. P. Barbot, Eds. New York, NY, USA: Marcel Dekker, 2002, pp. 53-102. [5] J. J. Slotine and W. Li, Applied Nonlinear Control, Englewood Cliffs, NJ, USA: Prentice-Hall, 1991. [6] H. K. Khalil, Nonlinear Systems, 3rd ed., Upper Saddle River, NJ, USA: Prentice-Hall, 2002. [7] Y. Shtessel, C. Edwards, L. Fridman, and A. Levant, Sliding Mode Control and Observation, New York, NY, USA: Springer, 2014. [8] S. V. Emelyanov, S. K. Korovin, and L. B. Freidovich, \"Adaptive Sliding Mode Control,\" Automation and Remote Control, vol. 67, no. 2, pp. 169-182, Feb. 2006. [9] J. G. Ziegler and N. B. Nichols, \"Optimum Settings for Automatic Controllers,\" Transactions of the ASME, vol. 64, no. 8, pp. 759-768, Nov. 1942. [10] K. D. Young, V. I. Utkin, and Ü. Özgüner, \"A Control Engineer\'s Guide to Sliding Mode Control,\" IEEE Transactions on Control Systems Technology, vol. 7, no. 3, pp. 328-342, May 1999.

Copyright

Copyright © 2025 Balanageshwara S, Rajanna G S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.