Data-driven variable tracking differentiation sigmoid proportional-integral-derivative controller for nonlinear multiple input multiple output system with white noise
Modern industrial systems often operate under complex nonlinear dynamics with multi-input-multi-output (MIMO) configurations, where conventional PID controllers struggle due to high sensitivity to noise and inadequate adaptability. To overcome these limitations, this study introduces a novel data-dr...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Muhammadiyah Yogyakarta
2025
|
| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/45967/ |
| _version_ | 1848827536988962816 |
|---|---|
| author | Qian, Goh Ming Mohd Riduwan, Ghazali Mohd Ashraf, Ahmad |
| author_facet | Qian, Goh Ming Mohd Riduwan, Ghazali Mohd Ashraf, Ahmad |
| author_sort | Qian, Goh Ming |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Modern industrial systems often operate under complex nonlinear dynamics with multi-input-multi-output (MIMO) configurations, where conventional PID controllers struggle due to high sensitivity to noise and inadequate adaptability. To overcome these limitations, this study introduces a novel data-driven Variable Tracking Differentiation Sigmoid PID (VTD-SPID) controller for nonlinear MIMO systems affected by white noise disturbances. The research contribution is the integration of a Variable Tracking Differentiator (VTD) into a sigmoid-base PID structure, optimized using the Safe Experimentation Dynamics Algorithm (SEDA), enabling enhanced noise filtration and improved control accuracy. The proposed VTD-SPID adapts controller inputs based on error and error rate feedback, with SEDA employed to optimize control parameters without requiring a mathematical model of the plant. Simulation was conducted on a Twin Rotor MIMO System (TRMS), known for its significant coupling and nonlinearity. The VTD-SPID controller outperformed conventional PID, SPID, and traditional TD-SPID controllers in all evaluated metrics. Results show a 14.58% improvement in tracking accuracy over TD-SPID, along a 14.93% reduction in the total error norm and a 4.37% reduction in control input energy. These improvements lead to smoother response trajectories, quicker settling times, and improved stability. Convergence analysis validated the effectiveness of SEDA in tuning high-dimensional control parameters efficiently. The study concludes that the VTD-SPID controller offers a superior, noise-resilient, and model-free control solution for nonlinear MIMO systems, with strong potential for broader application in real-world noisy environments. |
| first_indexed | 2025-11-15T04:02:17Z |
| format | Article |
| id | ump-45967 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T04:02:17Z |
| publishDate | 2025 |
| publisher | Universitas Muhammadiyah Yogyakarta |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-459672025-10-17T06:51:02Z https://umpir.ump.edu.my/id/eprint/45967/ Data-driven variable tracking differentiation sigmoid proportional-integral-derivative controller for nonlinear multiple input multiple output system with white noise Qian, Goh Ming Mohd Riduwan, Ghazali Mohd Ashraf, Ahmad TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering Modern industrial systems often operate under complex nonlinear dynamics with multi-input-multi-output (MIMO) configurations, where conventional PID controllers struggle due to high sensitivity to noise and inadequate adaptability. To overcome these limitations, this study introduces a novel data-driven Variable Tracking Differentiation Sigmoid PID (VTD-SPID) controller for nonlinear MIMO systems affected by white noise disturbances. The research contribution is the integration of a Variable Tracking Differentiator (VTD) into a sigmoid-base PID structure, optimized using the Safe Experimentation Dynamics Algorithm (SEDA), enabling enhanced noise filtration and improved control accuracy. The proposed VTD-SPID adapts controller inputs based on error and error rate feedback, with SEDA employed to optimize control parameters without requiring a mathematical model of the plant. Simulation was conducted on a Twin Rotor MIMO System (TRMS), known for its significant coupling and nonlinearity. The VTD-SPID controller outperformed conventional PID, SPID, and traditional TD-SPID controllers in all evaluated metrics. Results show a 14.58% improvement in tracking accuracy over TD-SPID, along a 14.93% reduction in the total error norm and a 4.37% reduction in control input energy. These improvements lead to smoother response trajectories, quicker settling times, and improved stability. Convergence analysis validated the effectiveness of SEDA in tuning high-dimensional control parameters efficiently. The study concludes that the VTD-SPID controller offers a superior, noise-resilient, and model-free control solution for nonlinear MIMO systems, with strong potential for broader application in real-world noisy environments. Universitas Muhammadiyah Yogyakarta 2025 Article PeerReviewed pdf en cc_by_nc_sa_4 https://umpir.ump.edu.my/id/eprint/45967/1/Data-driven%20variable%20tracking%20differentiation%20sigmoid.pdf Qian, Goh Ming and Mohd Riduwan, Ghazali and Mohd Ashraf, Ahmad (2025) Data-driven variable tracking differentiation sigmoid proportional-integral-derivative controller for nonlinear multiple input multiple output system with white noise. Journal of Robotics and Control (JRC), 6 (5). pp. 1-14. ISSN 2715-5072. (Published) https://doi.org/10.18196/jrc.v6i5.27584 https://doi.org/10.18196/jrc.v6i5.27584 https://doi.org/10.18196/jrc.v6i5.27584 |
| spellingShingle | TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering Qian, Goh Ming Mohd Riduwan, Ghazali Mohd Ashraf, Ahmad Data-driven variable tracking differentiation sigmoid proportional-integral-derivative controller for nonlinear multiple input multiple output system with white noise |
| title | Data-driven variable tracking differentiation sigmoid proportional-integral-derivative controller for nonlinear multiple input multiple output system with white noise |
| title_full | Data-driven variable tracking differentiation sigmoid proportional-integral-derivative controller for nonlinear multiple input multiple output system with white noise |
| title_fullStr | Data-driven variable tracking differentiation sigmoid proportional-integral-derivative controller for nonlinear multiple input multiple output system with white noise |
| title_full_unstemmed | Data-driven variable tracking differentiation sigmoid proportional-integral-derivative controller for nonlinear multiple input multiple output system with white noise |
| title_short | Data-driven variable tracking differentiation sigmoid proportional-integral-derivative controller for nonlinear multiple input multiple output system with white noise |
| title_sort | data-driven variable tracking differentiation sigmoid proportional-integral-derivative controller for nonlinear multiple input multiple output system with white noise |
| topic | TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering |
| url | https://umpir.ump.edu.my/id/eprint/45967/ https://umpir.ump.edu.my/id/eprint/45967/ https://umpir.ump.edu.my/id/eprint/45967/ |