Adaptive Target Slip Estimation for Vehicle Stability Control
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Solution Overview
Problem
Current target slip estimation algorithms fail to adapt to dynamically changing driving conditions, leading to inferior performance due to a lack of real-time adaptation to varying road surface conditions.
Innovation Solution
A system that includes a road surface detection module, a sensor system, and a controller module to generate adapted tuning parameters for a target slip estimator module, using real-time road surface conditions and sensor data to determine initial estimator values, gain, and forgetting factors, thereby improving the accuracy of target slip estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-adaptive target slip estimation algorithms are used, then device complexity is reduced, but measurement precision deteriorates due to inability to adapt to changing road conditions
Solution Approach 1:
The patent implements dynamic adaptation by continuously updating estimator parameters (forgetting factor, gain, initial values) based on detected road surface conditions. The system transitions from static fixed parameters to dynamic adaptive parameters that change with environmental conditions, resolving the contradiction between algorithm simplicity and estimation accuracy.
Solution Approach 2:
The system changes key estimation parameters (forgetting factor β, gain P0, initial frictional force Θ̂(0)) based on road surface condition detection. By adjusting these parameters dynamically according to detected conditions, the system achieves high measurement precision without requiring fundamentally complex algorithmic structures.
2Adaptability or versatility
If adaptive parameter tuning is implemented, then adaptability to changing conditions is improved, but device complexity increases due to additional detection and calculation modules
Solution Approach 1:
The patent segments the adaptation function into distinct modular components: road surface detection module, parameter determination module, and parameter application module. This segmentation allows the adaptive system to be built from manageable functional blocks, reducing overall system complexity while maintaining adaptability.
Solution Approach 2:
The controller module serves as an intermediary that receives road surface condition data and translates it into appropriate estimator parameters. This intermediary layer simplifies the connection between detection and estimation functions, making the overall system more manageable despite the added adaptability.
3Reliability
If real-time road surface detection is performed, then reliability of estimation is improved, but use of energy increases due to continuous sensing and processing
Solution Approach 1:
The system performs road surface detection and parameter updates at periodic intervals rather than continuously, reducing energy consumption while maintaining reliable estimation. The adaptive parameters are refreshed when significant condition changes are detected, balancing reliability with energy efficiency.
Solution Approach 2:
The target slip estimator uses its own operational data and detected road conditions to self-adjust its parameters, eliminating the need for external continuous calibration. This self-service capability maintains high reliability while minimizing additional energy requirements for external intervention.
Data Source
AI summary
Systems and methods are provided for generating adapted tuning parameters for target slip estimation, the parameters being adapted to real-time road surface conditions. The method includes, receiving, from a road surface detection module, a road surface condition, Sn, from among N road surface conditions S, range of friction, mu, and a confidence level, Ci. The method receives sensor system data from a sensor system, and determines, as a function of Sn, range of mu, and Ci, initial estimator values including an estimated initial frictional force {circumflex over (Θ)}(0), an initial gain, P0, and an initial projected range of signal bounds, (Pu) and (Pl). The method tunes (i.e., adapts) the initial estimator values to generate therefrom adapted tuning parameters based on received inputs. The method outputs adapted tuning parameters.