SiL

SiL: the first step in the real world

SiL testing involves the evaluation and validation of software components within a simulated environment before their integration into the actual hardware. This methodology allows developers to thoroughly assess the performance, functionality, and reliability of software elements in a controlled virtual setting.

Trying to empower the performance offered by our Corner Brake Actuators, we decided to study and implement a custom ABS logic.

This being the case, we developed three different ABS control algorithms, progressively increasing the level of complexity involved.

Then, the effect of each one was assessed on a virtual environment (M42 Hardware and Human in the Loop steering bench) in different cases.

Longitudinal and combined braking conditions, in both low and optimal road-tire adherence circumstances, were examined.

# Direct use of production-level software

# Complete traceability of both high and low-level requirements

# Validation of the specification framework at HW level

# FuSa/SOTIF assessment at SW level

FuSa: Functional Safety

SOTIF: Safety Of The Intended Functionality

Case study: ABS logic development

Aim of the activity

# Comparison of three different ABS algorithms

Method

# Assessment on a virtual environment with SiL approach

Results

# Advantages and drawbacks of each controller was tested in different conditions

  • Longitudinal braking (high ad low adherence)
  • Combined braking (high ad low adherence)

Case study: Proprietary ABS logic research

Vehicle model

Multi-degree vehicle model developed in ADAMS environment and implemented in Vi-grade CRT to be co-simulated with Matlab-Simulink

Controls in SiL

# State estimator with an Unscented Kalman Filter

# Electronic Stability Control (ESC)

# Electronic Brake-force Distribution (EBD)

# ABS

Case study: autonomous vehicles

# At limit handling condition autonomous driving controller

# Trajectory generation

  • Minimum curvature algorithm

# Sideslip angle estimation

  • Artificial Neural Network (ANN)
  • Unscented Kalman Filter (UKF)
  • Experimental test

# Hierarchical Robot Controller

  • High-level Non-linear Model Predictive Controller
  • Low-level Non-linear Model Predictive Controller

# High-level NMPC

  • Computes vehicle velocity profile for the long prediction horizon
  • Feed the start braking point to the low-level controller
  • High-fidelity 7 DOF vehicle model

# Low-level NMPC

  • Determines vehicle action for the short prediction horizon
  • Simple point-mass vehicle model
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