System identification and robust PID controller tuning of quarter car suspension system using hybrid optimization techniques
Abstract Developing a control solution for a quarter-car active suspension system, specifically aimed at enhancing ride comfort for individuals with spinal cord injuries while ensuring vehicle stability is important. Road irregularities are treated as external disturbances to the system. Traditional...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-10213-9 |
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| author | S. Sakthiya Ram C. Kumar R. Saravanakumar David Banjerdpongchai |
| author_facet | S. Sakthiya Ram C. Kumar R. Saravanakumar David Banjerdpongchai |
| author_sort | S. Sakthiya Ram |
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| description | Abstract Developing a control solution for a quarter-car active suspension system, specifically aimed at enhancing ride comfort for individuals with spinal cord injuries while ensuring vehicle stability is important. Road irregularities are treated as external disturbances to the system. Traditional PID controllers often fall short due to issues like nonlinear dynamics, uncertain parameters, and limited robustness. To overcome these limitations, the hybrid optimization framework is used for controller tuning. A dataset comprising 397 car models is analyzed, and system parameters are selected using a combined Sequential Quadratic Programming and Pattern Search method. After validating the resulting dynamic model, various PID controllers are designed using standard metaheuristic algorithms—Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA). Furthermore, two hybrid optimization strategies—Ant Colony Optimization with Genetic Algorithm (ACO-GA) and FminSearch with Simulated Annealing (Fmin-SA)—are applied to improve the control system’s robustness and response. Among the performance metrics considered including Integral Square Error (ISE), Integral of Absolute Error (IAE), and Integral of Time Absolute Error (ITAE), the ISE criterion was found to consistently yield superior results and was therefore adopted for the controller design. Simulation results show that the ACO-GA–based PID controller achieves faster response compared to that of other approaches. |
| format | Article |
| id | doaj-art-1ff050a7b1df446e825fc6857ce3ca21 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-1ff050a7b1df446e825fc6857ce3ca212025-08-20T03:43:11ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-10213-9System identification and robust PID controller tuning of quarter car suspension system using hybrid optimization techniquesS. Sakthiya Ram0C. Kumar1R. Saravanakumar2David Banjerdpongchai3Department of Electronics and Instrumentation Engineering, Bannari Amman Institute of TechnologyDepartment of Electrical and Electronics Engineering, Karpagam College of EngineeringDepartment of Electrical and Electronics Engineering, Dayananda Sagar College of EngineeringCenter of Excellence in Intelligent Control Automation of Process Systems, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn UniversityAbstract Developing a control solution for a quarter-car active suspension system, specifically aimed at enhancing ride comfort for individuals with spinal cord injuries while ensuring vehicle stability is important. Road irregularities are treated as external disturbances to the system. Traditional PID controllers often fall short due to issues like nonlinear dynamics, uncertain parameters, and limited robustness. To overcome these limitations, the hybrid optimization framework is used for controller tuning. A dataset comprising 397 car models is analyzed, and system parameters are selected using a combined Sequential Quadratic Programming and Pattern Search method. After validating the resulting dynamic model, various PID controllers are designed using standard metaheuristic algorithms—Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA). Furthermore, two hybrid optimization strategies—Ant Colony Optimization with Genetic Algorithm (ACO-GA) and FminSearch with Simulated Annealing (Fmin-SA)—are applied to improve the control system’s robustness and response. Among the performance metrics considered including Integral Square Error (ISE), Integral of Absolute Error (IAE), and Integral of Time Absolute Error (ITAE), the ISE criterion was found to consistently yield superior results and was therefore adopted for the controller design. Simulation results show that the ACO-GA–based PID controller achieves faster response compared to that of other approaches.https://doi.org/10.1038/s41598-025-10213-9Quarter Car suspensionSystem identificationRobust PID controller tuningHybrid optimizationSequential quadratic programming. |
| spellingShingle | S. Sakthiya Ram C. Kumar R. Saravanakumar David Banjerdpongchai System identification and robust PID controller tuning of quarter car suspension system using hybrid optimization techniques Scientific Reports Quarter Car suspension System identification Robust PID controller tuning Hybrid optimization Sequential quadratic programming. |
| title | System identification and robust PID controller tuning of quarter car suspension system using hybrid optimization techniques |
| title_full | System identification and robust PID controller tuning of quarter car suspension system using hybrid optimization techniques |
| title_fullStr | System identification and robust PID controller tuning of quarter car suspension system using hybrid optimization techniques |
| title_full_unstemmed | System identification and robust PID controller tuning of quarter car suspension system using hybrid optimization techniques |
| title_short | System identification and robust PID controller tuning of quarter car suspension system using hybrid optimization techniques |
| title_sort | system identification and robust pid controller tuning of quarter car suspension system using hybrid optimization techniques |
| topic | Quarter Car suspension System identification Robust PID controller tuning Hybrid optimization Sequential quadratic programming. |
| url | https://doi.org/10.1038/s41598-025-10213-9 |
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