Shift Fork Positioning With Hysteresis and Torque Ripple Compensation
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Solution Overview
Problem
Existing methods for actuator-driven shift fork systems fail to accurately position the gearshift sleeve due to hysteresis and torque ripple effects, leading to increased wear and mechanical play issues, which complicates the engagement and disengagement of gears in automatic transmissions.
Innovation Solution
A method utilizing a cellular automaton and learning algorithm to independently or jointly compensate for hysteresis and torque ripple effects, allowing for dynamic play correction and precise positioning of the shift fork, thereby minimizing wear and ensuring accurate gear engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hysteresis compensation is implemented using traditional control loops, then positioning accuracy is improved, but device complexity increases
Solution Approach 1:
The patent replaces traditional mechanical control loops and sensor-based feedback systems with a cellular automaton-based computational model. This software-based approach compensates for hysteresis effects through algorithmic prediction rather than physical feedback mechanisms, reducing hardware complexity while maintaining positioning accuracy.
Solution Approach 2:
The cellular automaton system performs self-learning and self-adjustment to compensate for hysteresis without requiring external calibration or complex control loops. The system automatically adapts to system characteristics through its inherent learning capability, eliminating the need for additional control complexity.
2Measurement precision
If torque ripple compensation is applied continuously, then positioning precision is improved, but energy consumption increases
Solution Approach 1:
The patent implements torque ripple compensation through periodic learning cycles rather than continuous adjustment. The cellular automaton learns torque ripple characteristics during idle periods and applies corrections only when needed, rather than continuously adjusting control signals, thereby reducing energy consumption while maintaining positioning precision.
Solution Approach 2:
The system performs preliminary learning of torque ripple characteristics during manufacturing or initial operation phases, storing compensation data for later use. This pre-learning approach eliminates the need for continuous real-time compensation calculations, reducing computational energy consumption during actual positioning operations.
3Measurement precision
If mechanical play is reduced through tighter tolerances, then positioning accuracy is improved, but manufacturing cost increases
Solution Approach 1:
The patent changes the control parameter space by introducing learning-based compensation algorithms that account for mechanical play variations. Instead of mechanically eliminating play through tight tolerances, the system software-compensates for play effects, allowing relaxed manufacturing tolerances while maintaining positioning accuracy.
Solution Approach 2:
The cellular automaton acts as an intermediary computational layer between the control input and mechanical output, mediating the effects of mechanical play. This software intermediary compensates for mechanical imperfections without requiring physical modifications to the mechanical components, reducing manufacturing costs.
4Measurement precision
If learning algorithms are implemented for play correction, then long-term positioning accuracy is improved, but initial setup time increases
Solution Approach 1:
The learning algorithm operates continuously in the background during normal system operation rather than requiring separate calibration phases. The cellular automaton learns and adapts to system characteristics during regular positioning tasks, eliminating dedicated setup time while maintaining long-term positioning accuracy through ongoing learning.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables precise positioning of the shift fork, reduces wear, and maintains positional accuracy, allowing for cost-effective manufacturing while ensuring reliable gear engagement and disengagement, thus extending the service life of components.
Implementation Method 1
a torque ripple of the actuator and a mechanical displacement of the gearshift sleeve are compensated independently of one another or in combination by means of a learning algorithm
Implementation Method 2
Actuators generally have hysteresis in their motion sequence. This means that the actuator moves from a first state into a second state, for example, in the presence of a control signal. If the control signal is moved back to 'zero,' the actuator no longer completely returns to the first state, however.
Data Source
AI summary
A method for the dynamically expanding play correction according to a method for hysteresis compensation for an actuator and for a shift fork which is movable by this actuator via an electric motor having a rotor and a stator and which guides a gearshift sleeve, by means of a cellular automaton, wherein a torque ripple of the actuator and a mechanical displacement of the gearshift sleeve are compensated independently of one another or in combination by means of a learning algorithm.