Key Takeaways & Executive Findings
- •• The study applies STPA to systematically identify SOTIF-related hazards in ECS systems, providing a structured safety analysis framework. • A model predictive control (MPC) strategy with explicit dynamic constraints is developed, integrating actuator performance boundaries to enhance safety and performance. • Hardware-in-the-loop testing validates the proposed control strategy under diverse operating conditions, demonstrating significant improvements in suspension functionality and safety. • The research bridges the gap between SOTIF principles and practical control design for automotive suspension systems, offering a comprehensive approach for future autonomous vehicle chassis development.
Abstract
Electronic control suspension (ECS) systems are of significance to ride comfort and handling stability of ground vehicles. However, ECS systems may pose unreasonable safety risks due to performance inadequacies or improper use by drivers, which are referred to as safety of the intended functionality (SOTIF) issues. Aiming to address the inadequate performance of the ECS system, this study proposes a model predictive control (MPC) method, with a particular focus on ensuring SOTIF. First, Systems theoretic process analysis (STPA) is utilized to assess the SOTIF of the ECS system and the ECS system control architecture is built. Then, Models including the input model, lateral and vertical coupled dynamics model, and nonlinear actuator model are established. In addition, an MPC strategy with explicit dynamic constraints is designed, incorporating the dynamic mechanical performance boundaries of ECS actuators into the constraints of the controller. Subsequently, a hardware-in-the-loop testing platform is constructed for the ECS system to conduct simulation experiments under various operating conditions. Results demonstrate that the designed control strategy effectively mitigates performance inadequacies of the suspension system, significantly enhancing its overall functionality and safety.
1. Introduction
The percentage of autonomous driving technology in the new car market is continuously increasing [1]. As the execution system of autonomous driving, the performance of the automotive chassis directly impacts the overall performance and safety of autonomous vehicles [2]. In addition to fulfilling its traditional roles of support and motion control, the chassis must also integrate closely with advanced sensors and processing platforms [3] to achieve more precise vehicle control and more efficient state feedback, known as the X-by-wire chassis [4]. The X-by-wire chassis primarily includes steer-by-wire, brake-by-wire, and electronic control suspension (ECS). The ECS system achieves precise vertical control of the vehicle through electronic signals, allowing for independent adjustment of each suspension’s stiffness, damping, and height. Adapting to varying road conditions and driving preferences enhances the driving experience and safety.
Currently, the control methods for the ECS system can be categorized into three types: (1) classical control methods based on suspension state parameters; (2) modern control methods based on optimal control theory; (3) intelligent control methods based on intelligent optimization strategies. The classical control methods for the ECS system are led by Sky-Hook Control, proposed by Karnopp et al. [5] in 1974. This method assumes the presence of a damper between the vehicle body and the sky. However, this method uses the product of sprung mass velocity and relative suspension velocity as the criterion, which may result in abrupt changes in damping force. Sammier et al. [6] proposed a linear Sky-Hook Control to mitigate instances of abrupt changes in damping force. Valasek et al. [7, 8] proposed a Ground-Hook Control and developed a Sky-Ground Hybrid Control system. Results show that this hybrid approach ensures vehicle ride comfort while maintaining controllability and stability. Modern control methods mainly include optimal control, fuzzy control, and robust control, typically chosen based on the characteristics of the control strategy and the control objectives. Maurya et al. [9] compared Proportion Integration Differentiation (PID) control [10] and Linear Q...
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Xianxu Bai, Kehe Nie, Haowei Sun, Anding Zhu, Haoxuan Dong, Di Wu (2025). SOTIF-Based Analysis and Design of Control Strategies for Controllable Suspension Systems of Automobiles. Chinese Journal of Mechanical Engineering. https://doi.org/10.1186/s10033-025-01237-3
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Frequently Asked Questions
What is the main contribution of this paper?
The paper proposes a model predictive control (MPC) strategy with explicit dynamic constraints for electronic control suspension (ECS) systems, addressing safety of the intended functionality (SOTIF) issues. It integrates STPA-based hazard analysis and hardware-in-the-loop validation to enhance suspension performance and safety.
How does the proposed control strategy ensure SOTIF?
The strategy incorporates dynamic mechanical performance boundaries of ECS actuators into the MPC constraints, preventing the system from operating outside safe limits. Additionally, STPA is used to identify potential hazards and inform the control architecture design.
What methods were used to validate the control strategy?
A hardware-in-the-loop (HIL) testing platform was constructed to simulate various operating conditions. The results demonstrated that the designed control strategy effectively mitigates performance inadequacies and enhances overall functionality and safety.
What are the key findings of the study?
The study found that the proposed MPC with explicit dynamic constraints significantly improves suspension system performance and safety compared to conventional methods. The HIL tests confirmed the effectiveness under diverse conditions, highlighting the importance of integrating SOTIF considerations into control design.
How does this research contribute to autonomous driving?
By enhancing the safety and performance of ECS systems, this research supports the development of reliable X-by-wire chassis for autonomous vehicles, ensuring better ride comfort and handling stability while addressing SOTIF-related risks.
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