Master Thesis

State and Parameter Estimation for Heavy Truck Steering Systems

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Abstract
Modern heavy trucks rely on advanced steering systems that include mechanical linkages, power assist units, and various sensors. Precise knowledge of the steering state (e.g. road wheel angle, steering rate) and key parameters (e.g. friction or boost gain in the steering mechanism) is crucial for vehicle dynamics control and safety. This project aims to develop physics-based estimation methods for truck steering systems, using models of steering dynamics combined with state observers. Unlike simplistic empirical approaches, model-based estimators (such as Kalman filters or nonlinear observers) can handle the highly nonlinear, time-varying nature of steering behavior and improve accuracy and robustness. The proposed research will design observers (e.g. Extended Kalman Filters, sliding-mode or Luenberger observers, and Moving Horizon Estimation algorithms) to estimate unmeasured states and parameters in real time. The outcomes are relevant to the commercial truck sector: enhanced vehicle stability, better on-board diagnostics of steering components, and readiness for advanced driver-assistance systems. By validating the estimators on real steering hardware or data, the project will bridge theory and practice in heavy vehicle dynamics.

Tasks

The student(s) involved will undertake the following tasks:

1. Steering System Modeling: Develop a mathematical model of the truck’s steering dynamics, including the steering wheel, assist mechanism (hydraulic or electric), and tire-road interaction. This involves deriving state-space equations capturing factors like steering inertia, compliance, and friction. The model will form the basis for observer design and will be calibrated using available vehicle parameters or test data. At Volvo we have developed our own model which could be considered as a basis for this task.

2. Estimator Design: Design and implement state and parameter estimation algorithms starting with classical Kalman Filter designs for linearized models and progress to Extended Kalman Filters (EKF) for nonlinear models of the steering system. It is also possible to explore other nonlinear approaches (e.g. a Lyapunov-based method or sliding mode observer) or optimization-based methods such as Moving Horizon Estimator (MHE) for joint state-parameter estimation. Each estimator will be configured to estimate quantities such as steering rack position (if not directly measured), lateral tire forces, friction parameters in the steering gear or play and unsymmetric behavior in the system. Comparing multiple approaches will highlight trade-offs in complexity and performance.

3. Simulation and Tuning: Implement the vehicle model and the proposed estimators in a simulation environment (MATLAB/Simulink or Python). Simulate various steering scenarios – from low-speed maneuvers to highway lane changes – to generate sensor measurements (steering angle sensor, yaw rate, lateral acceleration, etc.) with noise. The filter parameters need to be tuned to ensure fast convergence and robustness to measurement noise. The simulation will help verify real-time performance, ensuring algorithms can run at the necessary update rate without divergence.

4. Experimental Validation: The selected estimators will be implemented using real-world data and hardware in our HIL rig. The state estimation algorithms will be executed on logged data or in real-time on the test rig/actual truck. 

5. Analysis and Iteration: Analyze the estimator performance under various conditions (different vehicle speeds, road friction levels, and potential faults). If the estimator shows weaknesses (e.g. lag in high-slip scenarios or sensitivity to an unmodeled effect), the model or estimation method need to be refined. This could include augmenting the state vector with additional states (or parameters) to capture unmodeled dynamics or adopting an adaptive/filtering technique (e.g. a forgetting factor in the Kalman filter) to maintain accuracy as conditions change. The final step will be to consolidate the best-performing estimation approach and document its accuracy, convergence speed, and robustness criteria. Throughout this task, an emphasis is placed on generality – ensuring the developed estimation framework can apply across different steering system architectures.

Eventual Goals

By the conclusion of this project, the following goals are expected to be achieved:

• Develop an estimation algorithm that runs in real-time on typical automotive electronic control units. The goal is to achieve low-latency, high-frequency state updates so that the estimator can be directly integrated into a truck’s ECU. Achieving this involves efficient computations and potentially simplifying models without sacrificing accuracy.

• Attain high estimation accuracy for vehicle parameters in the presence of sensor noise and disturbances. The observers should be tuned to filter out high-frequency noise and handle model uncertainties. Robustness will be demonstrated by testing the estimator under various conditions (e.g. rough roads, sensor biases) and showing stable performance.

• Successfully estimate key unmeasured states and parameters of the steering system, thereby providing greater insight than sensor readings alone. For example, the estimator may reconstruct the tire slip angle at the front wheels or the steering rack force, which are valuable for vehicle stability control. It will also track parameters such as the steering friction or boost motor gain over time. This ability to learn parameters online means the system can adapt to changes (like loading or wear) and still provide reliable state information.

• Demonstrate that the estimation framework can facilitate health monitoring of the steering system. By associating certain faults with parameter changes, the observers will be able to detect anomalies. The end goal is a general diagnostic capability where the vehicle can alert maintenance if the steering system shows signs of wear or malfunction, using the estimator’s outputs.

• Generality and Flexibility: Ensure the developed estimation approach is generic enough to apply to various subtopics within steering dynamics. The final methodologies should be adaptable to different steering system designs (whether traditional hydraulic power steering or modern electric power steering). Moreover, the same core approach should handle different estimation objectives – from friction estimation to state reconstruction to actuator fault diagnosis – by appropriate configuration. By meeting this goal, the project results could serve as a template for state estimation in other automotive domains as well.

Requirements:

Students interested in this topic  should possess the following skills and background:
• Control Theory and Estimation
• Vehicle Dynamics Knowledge
• Simulation and Programming: MATLAB/Simulink, Python C/C++ 
• Problem-Solving and Initiative

Overall, this thesis offers a chance to apply control and estimation theory in a practical automotive context, developing an estimator that could enhance the safety and performance of heavy duty trucks. Candidates should be eager to combine theoretical work with practical validation, contributing to the state-of-the-art in vehicle state estimation and diagnostics.

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