Adaptive Traction for Optimal Mobility for Heavy Duty Vehicles
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Mobility engineering (MPMOB), MSc
Publicerad
2023
Författare
Choudhary, Mukesh
Mapari, Aditya
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis addresses the critical need for developing prediction-based control allocation strategies for autonomous or manually operated vehicles within low-speed
application area such as construction and mining sites. A prediction horizon focused
in this thesis is approximately 50 meters. In the pursuit of safe and energy-efficient
control, it is essential to harness the potential of multiple traction actuators, which
traditionally operate re-actively. This project seeks to optimize these systems using predictive algorithms, given that drivers often lack the knowledge required to
operate them effectively. Furthermore, the timely responsiveness of actuators is of
critical importance in demanding situations. The current practice involves manual
control of traction actuators, such as differential locks and electronically controlled
air suspension, based on drivers’ real-time observations. However, this approach
is often sub-optimal, as it does not fully utilize the capabilities of these systems.
To address this issue, the thesis centers on automating these traction actuators,
leveraging predictive road data. It assumes the availability of upcoming road data,
including road profile and predicted friction data for the next 50 meters.
The primary objective is to develop an optimal control strategy that maximizes
traction while ensuring adequate steering margin. To achieve this, the thesis initially delves into understanding how these actuators influence traction and steering.
Subsequently, a rule-based control allocation model is developed in MATLAB and
Simulink, which is then tested with a comprehensive vehicle simulation model across
various test cases. The research also extends to practical implementation. The control allocation logic is transferred to real-world conditions using real-time systems,
specifically the MicroAutoBox II, on a physical truck. Impressively, the developed
control function provides results in almost real-time, with a response time of only
approximately 1000 milliseconds. While this computational time may be considered
too high for safety-critical functions in some contexts, it remains adequate for the
specific function under scrutiny, which is focused on predicting the upcoming 50-
meter road conditions. In conclusion, the thesis presents a comprehensive approach
to enhance traction using differential locks and axle load distribution strategy. By
automating traction actuators based on predictive road data and optimizing control strategies, this research contributes to realizing safer, more energy-efficient autonomous driving systems.
Beskrivning
Ämne/nyckelord
Prediction based control allocation , Traction actuators , differential locks , electronically controlled air suspension , predictive road data, real-time systems , axle load distribution , optimizing control