Active vehicle systems are increasingly playing a fundamental role in modern automotive technology, enhancing vehicle comfort and safety.

The integration of electromechanical actuators with advanced control strategies supports the driver during simple driving tasks, improving ride comfort and reducing fuel consumption through systems like Adaptive Cruise Control (ACC), Lane Keeping Assist (LKA) or Park Assist, while also enabling the vehicle to actively intervene in critical situations to prevent accidents or mitigate their effects, as in the case of Autonomous Emergency Braking (AEB) or Automatic Emergency Steering (AES). The development of these functionalities is highly complex, requiring significant efforts to integrate mechanical, electrical, and software components. 

A further critical aspect is the early assessment of both the effectiveness of the new functionality at the vehicle level and its acceptance by drivers and passengers. A key aspect is to analyze how the function under development interacts with existing ones and to calibrate the control logic intervention accordingly. 

To achieve these objectives within competitive timelines, the automotive industry has adopted new development strategies aimed at reducing the time to market of advanced systems.

A central element of this process is the extensive use of simulation. Offline simulation and driving simulators offer several key advantages. 

First, they guarantee repeatability of the test, which is essential to perform sensitivity analysis on different tuning parameters while maintaining identical boundary conditions. This allows the comparison of the reaction of different drivers exposed to the same exact driving scenarios.

Another fundamental advantage is the possibility to reproduce limit handling conditions as well as imminent collision and emergency scenarios without exposing the driver to real danger. This capability is particularly critical for systems designed to intervene in hazardous situations, such as Electronic Stability Control (ESC), Autonomous Emergency Braking (AEB), or Automatic Emergency Steering (AES). 

Once the specifications and objectives of the new functionality have been defined, the testing process begins with a progressively decreasing level of virtualization until the final product is validated on track. 

The first tools employed are co-simulation architectures, which allow evaluation of the interaction between the control logic, the vehicle model, and the actuator model. 

In a subsequent step, static and dynamic driving simulators can be used to involve human drivers at an early stage, providing valuable feedback on the acceptability and perceived effectiveness of the new function. At this stage, physical actuators are not required, as their behavior can be represented with the desired level of fidelity by simulation models.

In the next phase, the control logic is tested with a real actuator by means of a Hardware-in-the-Loop (HiL) simulator. Introducing the physical actuator enables fine-tuning of the control algorithms and facilitates debugging activities related to the integration of the various vehicle electronic control units (ECUs). 

Finally, during on-track testing, where all model uncertainties are removed, the final calibration of the control strategy is performed. 

However, both the HiL and track testing phases require actuators equipped with open and programmable electronic control units. This remains a significant limitation, as commercial actuators typically do not permit direct access to embedded software. Consequently, unless the study is carried out in collaboration with the original equipment manufacturer (OEM), the testing process cannot be fully completed. 

To overcome these constraints research-oriented electromechanical actuators that exhibit performance comparable to series-production systems, while offering open and flexible control architectures, have emerged.

A notable example is the Corner Brake Actuator (CBA), which enables independent control of the pressures on the four vehicle corners and has been successfully used as a research tool in several studies.

The present work focuses on the modeling of a modified steering actuator designed for research applications. The system is based on an existing EPS parallel-axis steering system in which an additional torque sensor is introduced after the steering wheel to measure the driver’s input torque, steering angle and steering velocity. The modeling activity examines the impact of the additional torque sensor on steering dynamics, with the aim of determining whether the modified steering system can replicate the performance of the original steering system.

Results show that the added compliance, represented by the additional torque sensor, significantly affects the passive steering dynamics (EPS off), whereas its impact on the active steering response (EPS on) remains negligible.