Adaptive Vehicle Control Model Iteration
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Solution Overview
Problem
Conventional vehicle control systems, such as those described in US9849880 and US2018164810, are overly complex and rely on a priori road grade information, which limits their computational efficiency and robustness, especially in handling variations between different vehicles of the same type and in unpredictable situations like ageing components or environmental changes.
Innovation Solution
An updatable vehicle control model that iteratively adapts based on the vehicle's behavior, using processing circuitry and sensors to adjust control strategies, simplifying the PID controller's role and allowing for smoother operation and adaptation to changing conditions, with the option to share updated models across similar vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional vehicle control systems use a priori road grade information and complex modeling, then control precision is improved, but device complexity increases and computational efficiency decreases
Solution Approach 1:
The system implements iterative adaptation where the vehicle control model is continuously refined using feedback from actual vehicle behavior data. Sensors monitor vehicle state parameters, and the processing circuitry updates the control model based on discrepancies between predicted and actual behavior, eliminating the need for complex a priori road grade information while maintaining control precision.
Solution Approach 2:
The vehicle control system performs self-adaptation by automatically updating its own control model using data from its own operation. The processing circuitry iteratively refines the vehicle control model based on observed vehicle behavior, allowing the system to adapt to individual vehicle characteristics without external intervention or complex pre-programming.
2Stability of the object's composition
If a priori road grade information is used for torque control, then vehicle operation smoothness is improved, but adaptability to unpredictable situations decreases
Solution Approach 1:
The system transitions from static a priori road grade information to dynamic iterative adaptation. The vehicle control model is continuously updated in real-time based on actual vehicle behavior, allowing the system to adapt to changing conditions and unpredictable situations while maintaining smooth operation through continuous refinement of control parameters.
Solution Approach 2:
The iterative update mechanism uses feedback from actual vehicle operation to continuously refine the control model, enabling the system to adapt to unpredictable situations and individual vehicle characteristics while maintaining operational smoothness through real-time adjustments.
3Ease of manufacture
If generalized vehicle control models are used for multiple vehicles, then ease of manufacture is improved, but manufacturing precision decreases due to inherent differences between vehicles
Solution Approach 1:
Each vehicle's control system automatically adapts to its own specific characteristics through iterative learning during operation. The processing circuitry refines the control model using data from the individual vehicle's sensors, allowing a standardized control system to achieve vehicle-specific precision without requiring custom calibration for each unit.
Solution Approach 2:
The system uses feedback from actual vehicle behavior to iteratively update the control model, enabling a single standardized control system to adapt to and compensate for inherent differences between individual vehicles, thereby achieving both ease of manufacture and vehicle-specific control precision.
4Adaptability or versatility
If complex adaptive algorithms are implemented, then adaptability to changing conditions is improved, but productivity and computational efficiency decrease
Solution Approach 1:
The system implements iterative adaptation using feedback from vehicle sensors, where the processing circuitry updates the control model based on observed behavior. This approach provides adaptability to changing conditions while maintaining computational efficiency by using a systematic iterative method rather than complex real-time calculations.
Data Source
Figure 1A~1C
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AI summary
The present disclosure relates to an on-board control system (200) for operating a vehicle (100, 102, 104), the control system (200) comprising processing circuitry (202) and a plurality of sensors (204, 206, 208) arranged with the vehicle (100). The control system (200) employs e.g. a general vehicle control model (M) for operating the vehicle (100, 102, 104), where the vehicle control model (M) is specifically adapted for the vehicle (100, 102, 104) based on an ongoing operation of the vehicle (100, 102, 104). The present disclosure also relates to a corresponding computer implemented method and to a computer program product.