Adaptive Handwheel Angle Control for Robust Lane Centering
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Solution Overview
Problem
Advanced Driver-Assistance Systems (ADAS) face challenges in vehicle lateral control due to suboptimal parameter identification, leading to underdamped or jittery lane-centering performance, which is exacerbated by uncertainties in vehicle components and environmental conditions.
Innovation Solution
An adaptive handwheel angle control method and system that identifies and compensates for uncertainties in steering systems by quantifying component variants and applying feed-forward control to adjust torque and wheel angle controls, ensuring robustness and optimal performance across various vehicle configurations and operating conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If suboptimal parameter identification is used in lateral control, then the system is simpler to implement, but lane-centering performance becomes underdamped or jittery
Solution Approach 1:
The system performs preliminary identification of steering system parameters (such as steering ratio, rack gear characteristics, and tire characteristics) during vehicle assembly or calibration. These identified parameters are stored and used as the basis for generating optimized lateral control parameters, so that when the vehicle operates, the control system can directly use these pre-identified parameters without real-time computation, thus achieving both simplicity and reliability.
Solution Approach 2:
The system changes control parameters based on the identified steering system characteristics. Specifically, lateral control parameters (such as proportional gain, derivative gain, and feedforward gains) are adjusted according to the previously identified steering parameters to optimize lane-centering performance. This parameter adaptation ensures that the control system is tailored to each specific vehicle's steering characteristics, eliminating underdamped or jittery behavior.
2Loss of time
If control systems are configured based on simulation results, then development time is reduced, but actual operating performance deviates due to actuator and sensor imperfections
Solution Approach 1:
The system introduces local compensation for specific components (actuators and sensors) by identifying their actual characteristics during vehicle assembly. Each vehicle's steering actuator and sensor are individually characterized, and compensation parameters are generated specifically for those components. This local quality approach allows the system to maintain the benefits of simulation-based development while correcting for actual component variations in each vehicle.
Solution Approach 2:
The system implements feedback mechanisms where actual vehicle performance data is collected and used to refine control parameters. The identified parameters from actual vehicle operation are fed back into the control system to adjust and optimize performance, creating a closed-loop process that bridges the gap between simulation and real-world operation.
3Adaptability or versatility
If component variants are used in vehicle assembly, then manufacturing flexibility increases, but control system performance varies due to uncertainties
Solution Approach 1:
The system automatically adapts control parameters based on the specific component variants installed in each vehicle. During assembly or initial operation, the system identifies the actual characteristics of the steering components (which may vary due to different manufacturers, batches, or configurations). Based on this identification, the lateral control parameters are automatically adjusted to match the specific component组合, ensuring consistent performance across all vehicle variants despite manufacturing flexibility.
Solution Approach 2:
The system creates a universal calibration and identification process that works across all vehicle variants. The same identification methodology and parameter adjustment mechanism can be applied regardless of which specific component variants are used, making the system universally applicable to all configurations while maintaining consistent performance.
4Reliability
If real-time adaptation of control parameters is implemented, then performance robustness improves, but computational complexity and processing requirements increase
Solution Approach 1:
The system performs the computationally intensive parameter identification and optimization work in advance, during vehicle assembly or initial calibration, rather than in real-time during operation. The identified parameters and optimized control settings are stored in memory, allowing the control system to simply retrieve and apply these pre-computed values during normal operation. This approach achieves robust performance adaptation without requiring complex real-time computation.
Data Source
AI summary
In various embodiments, methods, systems, and vehicle apparatuses are provided. A method for lateral control of a steering system includes identifying at least one parameter of at least one lateral control feature that results in an optimized value in an outcome of control for a lateral control feature enabling robustness to an uncertainty; adaptively adjusting an output control signal generated by a vehicle trajectory controller coupled to the steering controller, by quantifying the uncertainty of an uncertain value in a lateral control feature; and sending a control command including at least a torque control or a wheel angle control to compensate for the uncertainty of the at least one uncertain value in the configuration of components of an electronic power steering (EPS) system associated with the vehicle variant by correcting at least one parameter of the at least one lateral control feature.