Active Seat Suspension Using Prefiltered Road Data for Phase-Delay Control
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Solution Overview
Problem
Conventional active seat suspensions struggle to effectively mitigate low-frequency, long-duration motions such as those encountered when traversing hills or curves, as real-time filtering introduces phase delays, reducing the system's responsiveness to these inputs.
Innovation Solution
An active seat suspension system that utilizes prerecorded road data prefiltered to exclude certain frequency and length scales, allowing it to anticipate and control seat movements based on anticipated road inputs, thereby mitigating high-frequency events while minimizing response to low-frequency, long-duration events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time filtering is used to mitigate low-frequency motions, then the system can respond to road inputs, but phase delays are introduced that reduce responsiveness
Solution Approach 1:
The system pre-filters road data along the path of travel before the vehicle actually encounters it, creating a predictive model of upcoming road inputs. This preliminary processing eliminates phase delays by having the filtering done in advance rather than in real-time, allowing the seat suspension to respond immediately when the vehicle reaches the predicted road features.
Solution Approach 2:
The system anticipates road inputs by analyzing pre-filtered road data and applies counter-actions through the seat suspension before the vibrations and motions fully transmit to the occupant. By predicting upcoming road irregularities and pre-positioning the seat to counteract them, the system neutralizes harmful effects before they occur.
2Adaptability or versatility
If the active seat suspension responds to all frequency inputs, then comprehensive mitigation is achieved, but low-frequency long-duration motions cannot be effectively controlled
Solution Approach 1:
The system segments the frequency spectrum by applying pre-filtering that separates high-frequency road inputs from low-frequency motions. The pre-filtered road data contains only high-frequency components above a threshold, allowing the control system to selectively mitigate high-frequency vibrations while ignoring low-frequency body motions that cannot be effectively controlled by the seat suspension.
Solution Approach 2:
The system changes the frequency parameter of the road data by applying a high-pass filter with a threshold frequency (e.g., 0.5 Hz). This parameter transformation removes low-frequency components from the control signal, enabling the seat suspension to focus its control efforts on the high-frequency range where it is most effective while preventing wasteful or harmful responses to low-frequency inputs.
3Reliability
If prefiltered road data is used to anticipate road inputs, then high-frequency events are mitigated, but the system must exclude certain frequency and length scales
Solution Approach 1:
The system extracts only the relevant high-frequency components from the complete road data by applying a high-pass filter. By taking out and isolating only the frequency components above the threshold (e.g., >0.5 Hz), the system eliminates the need to process and respond to low-frequency data, simplifying the control algorithm while maintaining effective high-frequency mitigation.
Data Source
AI summary
Embodiments related to the operation of an active seat suspension using prefiltered road data are described. In one embodiment, the prefiltered road data may be filtered to exclude road and/or driving inputs with low frequencies, or long length scales, such as hills or curves. Such an embodiment may at least partially reduce operation of an active seat suspension in response to these low frequency long length scale of inputs where displacements of the vehicle may be greater than a range of motion of the active seat suspension while still permitting the active seat suspension to respond to higher frequency inputs.