Fuzzy Adhesion Estimation for Vehicle Braking Control
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Solution Overview
Problem
Existing anti-lock braking systems in motor vehicles struggle to accurately determine the current adhesion value between the tire and ground, especially outside the driving dynamics limit range, leading to reduced braking efficiency and potential wheel damage.
Innovation Solution
A fuzzy-based control system that includes a brake pressure measurement unit, a signal processing unit, and a control unit, utilizing fuzzy logic to estimate the adhesion value based on brake pressure and other measurements, and a finite state machine to manage brake pressure application, allowing for continuous measurement and adaptation to external conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional anti-lock braking systems are used, then braking control can be maintained within the driving dynamics limit range, but the adhesion value cannot be accurately determined outside this range
Solution Approach 1:
The system dynamically adapts its measurement and control strategy based on the current operating state. The finite state machine transitions between different measurement modes (normal braking, threshold braking, hold braking, ramp braking) depending on whether the operating point is within or outside the driving dynamics limit range, enabling accurate adhesion determination across the full operating range.
Solution Approach 2:
The system changes the brake pressure parameter dynamically to enable adhesion measurement. By intentionally varying brake pressure through different braking modes (especially threshold and ramp braking), the system probes the tire-road interface to determine adhesion values outside the normal driving range where conventional systems fail.
2Productivity
If brake pressure is increased to improve braking force, then braking efficiency improves, but wheel locking and sliding occur reducing control
Solution Approach 1:
The system continuously monitors wheel speed, brake pressure, and slip ratio, using this feedback to adjust brake pressure in real-time. The finite state machine uses feedback from slip ratio measurements to determine when to transition between braking modes, maintaining optimal brake pressure that prevents wheel locking while maximizing braking efficiency.
Solution Approach 2:
The system applies partial braking force during normal operation and uses controlled excessive brake pressure during threshold braking to probe the adhesion limit. By carefully managing these partial and excessive actions within different finite states, the system determines maximum adhesion without causing harmful wheel locking.
3Measurement precision
If continuous brake pressure measurement is implemented, then real-time adhesion determination is possible, but system complexity increases
Solution Approach 1:
The control system is segmented into a finite state machine with distinct, well-defined states (normal braking, threshold braking, hold braking, ramp braking) and clear transition conditions. This segmentation organizes the complex measurement and control logic into manageable segments, making the system implementable despite its complexity.
Solution Approach 2:
The finite state machine serves multiple functions: it controls brake pressure application, manages measurement timing, determines operating mode, and transitions between different control strategies. This multi-functionality reduces the need for separate dedicated components, managing system complexity while enabling real-time adhesion determination.
Data Source
AI summary
A fuzzy-based control system in a motor vehicle for controlling a speed comprises a brake pressure measurement unit, a signal processing unit and a control unit. The brake pressure measurement unit is adapted as a finite state machine to measure a current brake pressure of a brake of a wheel of the motor vehicle dependent on a trigger. The signal processing unit is adapted to estimate a current adhesion value μ between a tyre associated with the wheel and the current ground, based on the current brake pressure of the brake and further measurement values. The estimating comprises an inference based on fuzzy rules and a fuzzification, a subsequently a defuzzification of the inference. The control unit is adapted to control a speed of the motor vehicle or the brake pressure of the brake, based on the estimated current adhesion value μ.