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<p>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.</p>

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System identification and robust PID controller tuning of quarter car suspension system using hybrid optimization techniques

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月7日

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  • S. Sakthiya Ram,
  • C. Kumar,
  • R. Saravanakumar,
  • David Banjerdpongchai

摘要

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.