MDPS Fail Safety Control Using Yaw Rate and Torque Analysis
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Solution Overview
Problem
Existing motor driven power steering (MDPS) fail safety systems become complex and costly due to the need for additional sensors to detect failures like self-steering and no-steering situations, which are not accurately considered in overall vehicle states, leading to increased manufacturing costs and potential safety risks.
Innovation Solution
A control system and method that determines MDPS failures and their types (self-steering or no-steering) using existing vehicle state parameters like steering angle, steering torque, vehicle speed, and yaw rate change rate, without adding separate sensors, by integrating these parameters to identify threshold values for motor torque proportionality and yaw rate changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional sensors are added to detect MDPS failures, then measurement precision is improved, but device complexity and manufacturing cost increase
Solution Approach 1:
The system uses existing vehicle sensors (steering angle sensor, steering torque sensor, vehicle speed sensor, yaw rate sensor) to detect MDPS failures. The control unit analyzes data from these sensors to determine failure occurrence and type, making the system self-diagnostic without requiring additional dedicated sensors for failure detection.
Solution Approach 2:
Existing sensors serve multiple functions: they provide data for normal steering control and simultaneously provide data for failure detection and diagnosis. The steering torque sensor, for example, is used both for control torque calculation and for detecting abnormal torque patterns indicating failure.
2Reliability
If fail safety logic is added for each new MDPS function, then reliability is improved, but device complexity increases
Solution Approach 1:
The failure detection logic is merged with the existing control unit that manages normal MDPS operations. The control unit integrates failure detection algorithms alongside control algorithms, analyzing the same sensor data for both control and safety purposes, thereby avoiding separate dedicated safety systems.
Solution Approach 2:
The system detects failures by monitoring changes in operational parameters (steering torque, steering angle, vehicle speed, yaw rate) and comparing them against expected ranges. Failure detection is achieved through parameter analysis rather than dedicated safety sensors, simplifying the overall system architecture.
3Measurement precision
If additional sensors are added to detect MDPS failures, then measurement precision is improved, but manufacturing cost increases
Solution Approach 1:
The system uses existing vehicle sensors (steering angle sensor, steering torque sensor, vehicle speed sensor, yaw rate sensor) to detect MDPS failures. The control unit analyzes data from these sensors to determine failure occurrence and type, making the system self-diagnostic without requiring additional dedicated sensors for failure detection.
4Reliability
If current is immediately interrupted to stop MDPS operation, then reliability is improved, but loss of time occurs
Solution Approach 1:
The system performs preliminary failure detection and classification before taking corrective action. The control unit first determines whether a failure has occurred, then identifies the failure type (self-steering or no-steering), and only then interrupts motor current. This sequential approach ensures appropriate response while maintaining safety.
Solution Approach 2:
The system continuously monitors sensor data and provides feedback to the control unit, which adjusts motor torque based on detected conditions. When failure is detected, the feedback loop triggers immediate current interruption, ensuring rapid response while maintaining the ability to differentiate between failure types for appropriate safety measures.
Data Source
AI summary
Disclosed are a control system and a control method for fail safety of a motor driven power steering (MDPS). The system may include a straight motion/turning determination module for determining whether a vehicle is in a straight motion or turning based on a steering angle of a steering wheel, a steering torque of the steering wheel and a vehicle speed of the vehicle, a fail determination module for determining whether an MDPS fail occurs based on a steering torque of a driver and a motor torque of the MDPS if the vehicle is in the straight motion, a fail type determination module for determining a type of the MDPS fail using a yaw rate change rate if the MDPS fail occurs, and a yaw rate change rate detection module for inputting the yaw rate change rate to the fail type determination module.