Self-Tuning Steering Control for Off-Road Vehicles
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Solution Overview
Problem
Automatic steering systems in off-road vehicles, particularly agricultural tractors, face performance degradation due to varying vehicle configurations and soil conditions, making it challenging to achieve robust control, as vehicle parameters are hard to measure and access.
Innovation Solution
A closed-loop identification method using Iterative Learning Identification (ILI) is employed to develop a reduced-order vehicle model, which is used to calculate controller calibration constants for real-time control of the steering system, accounting for vehicle configurations and soil conditions, thereby improving control system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a full-order vehicle model is used for automatic steering control, then the control accuracy is improved, but the computational complexity increases making real-time control difficult
Solution Approach 1:
The patent extracts only the essential dynamic characteristics of the vehicle system by removing non-essential high-frequency modes. This is achieved through modal truncation where only the dominant low-frequency modes are retained in the reduced-order model, eliminating computationally intensive components while preserving the critical steering dynamics needed for accurate control
Solution Approach 2:
The patent transforms the vehicle model from a complex high-order representation to a simplified low-order representation by changing the model order parameter. This parameter reduction is accomplished through systematic model reduction techniques that maintain accuracy for the operating range of interest while dramatically reducing computational burden for real-time control applications
2Adaptability or versatility
If vehicle parameters are measured and accessed for model identification, then the control system adapts to different conditions, but the measurement and calibration process becomes complex and time-consuming
Solution Approach 1:
The patent enables the control system to automatically identify and adapt to vehicle parameters without requiring manual measurement or complex calibration procedures. The system performs self-identification by processing normal operational data to extract vehicle-specific characteristics, eliminating the need for separate calibration activities and reducing complexity while maintaining adaptability
Solution Approach 2:
The patent performs model identification and parameter extraction in advance during system initialization or setup phases, so that the reduced-order model is ready for real-time control before actual operation begins. This preliminary action separates the complex identification process from time-critical control operations, reducing overall system complexity
3Reliability
If iterative learning identification is performed in real-time, then the vehicle model is continuously optimized, but the computational load increases
Solution Approach 1:
The patent extracts only the essential update computations needed for iterative learning, removing computationally intensive operations from the real-time loop. By using the pre-established reduced-order model structure, the system performs only necessary parameter adjustments during iteration, significantly reducing computational energy while maintaining model optimization
Solution Approach 2:
The patent applies partial iterative updates only to the critical parameters that most influence control performance, rather than fully re-identifying all model parameters at each iteration. This selective updating approach maintains model accuracy where it matters most while reducing overall computational energy consumption
Data Source
AI summary
At least one example embodiment discloses a method of controlling steering of a vehicle. The method includes identifying a reduced-order vehicle model, the reduced-order vehicle model representing a vehicle transfer function in which a least one high-frequency pole is removed. The method further includes generating target closed loop pole locations based on at least one user preference parameter. The method further includes determining calibration parameters that place closed loop poles of a composite system at the target closed loop poles. The composite system in this context includes the reduced-order vehicle model and a steering control system. Finally, the method includes controlling the steering using the steering control system configured using the calibration parameters.