Adaptive Vehicle Controller Using Neural Networks
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Solution Overview
Problem
Conventional controllers in transportation vehicles lack adaptability and stability, making them unsuitable for dynamic driving conditions and user-specific needs, despite the potential of artificial neural networks for adaptability.
Innovation Solution
A method and device that utilize an artificial neural network to adjust controller parameters based on transportation vehicle state information and user characteristics, ensuring stability and robustness by limiting parameter values within verified ranges, thereby adapting the control strategy to user preferences and driving conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional controllers are used in transportation vehicles, then stability and robustness are maintained, but adaptability to dynamic driving conditions and user-specific needs is insufficient
Solution Approach 1:
The controller transitions from a static conventional design to a dynamic adaptive system using artificial neural networks. The neural network continuously learns from driving data and adjusts control parameters in real-time, enabling the controller to adapt to changing driving conditions and user preferences while maintaining system stability through controlled adaptation mechanisms.
Solution Approach 2:
The invention changes the controller parameters from fixed conventional values to dynamically adjustable parameters determined by artificial neural networks. The neural network processes input data and generates optimized control parameters that adapt to specific driving scenarios, thereby improving adaptability while maintaining reliability through verified parameter ranges.
2Adaptability or versatility
If artificial neural networks are used to adjust controller parameters, then adaptability improves, but system complexity increases
Solution Approach 1:
The artificial neural network is designed as a universal adaptive component that handles multiple control tasks across different driving scenarios. Rather than implementing separate control systems for different functions, the neural network provides a unified adaptive control mechanism that can adjust to various driving conditions, user preferences, and vehicle states, thereby managing complexity through functional integration.
Solution Approach 2:
The neural network implements self-service by automatically learning from driving data and adjusting control parameters without requiring manual intervention or complex external configuration. The system continuously trains and adapts itself based on observed driving patterns, reducing the need for manual system complexity while maintaining high adaptability.
3Measurement precision
If controller parameters are dynamically adjusted, then control precision improves, but stability may be compromised
Solution Approach 1:
The system implements feedback mechanisms where the neural network continuously monitors control outcomes and adjusts parameters based on performance feedback. This closed-loop approach allows the system to achieve high control precision by learning from actual results while maintaining stability through corrective feedback that prevents excessive parameter deviations and ensures reliable operation.
Data Source
AI summary
A method for adjusting a controller of a transportation vehicle includes receiving transportation vehicle state information and information about a current value of at least one variable controller parameter of the controller, calculating a setpoint value for the variable controller parameter, and outputting the setpoint value for the variable controller parameter. The calculating the setpoint value includes using an artificial neural network based on the transportation vehicle state information and the information about the current value of the variable controller parameter.