Vehicle Vibration Control Using Predictive Sensor-Driven Actuation
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Solution Overview
Problem
Conventional methods for suppressing vibrations in vehicles are static and do not fully utilize modern sensory systems, often focusing on post-vibration correction rather than real-time adaptive solutions, which limits their effectiveness in improving passenger comfort and vehicle durability.
Innovation Solution
An electronic system utilizing a machine learning system to process data from environment and internal sensors to determine actuator settings, enabling real-time control of vibrations and inertial forces across multiple areas of interest within a vehicle, thereby adapting to dynamic conditions such as road surfaces and passenger loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional static vibration suppression methods are used, then device complexity is reduced, but adaptability to dynamic conditions deteriorates
Solution Approach 1:
The patent implements dynamic vibration suppression by continuously adapting actuator settings based on real-time sensor data and machine learning predictions. The system transitions from static to dynamic control by predicting future vibration states and adjusting actuator parameters adaptively, allowing the system to respond to changing road conditions and vehicle states in real-time
Solution Approach 2:
The patent applies preliminary action by using machine learning systems to predict future vibration states before they occur. The system processes sensor data and generates predictions about upcoming vibrations, allowing actuators to be pre-positioned or pre-adjusted to counteract anticipated vibrations, rather than merely reacting after vibrations occur
2Reliability
If post-vibration correction methods are used, then device complexity is reduced, but effectiveness in improving passenger comfort deteriorates
Solution Approach 1:
The system improves passenger comfort effectiveness by predicting vibrations before they occur and preparing appropriate countermeasures. The machine learning system analyzes sensor data to forecast upcoming vibrations, allowing the control system to activate actuators in advance with optimal settings, thereby preventing discomfort rather than correcting it after the fact
Solution Approach 2:
The patent implements feedback by continuously monitoring sensor data from multiple sources (accelerometers, microphones, road sensors) and using this information to adjust actuator settings in real-time. The machine learning system processes this feedback loop, constantly refining predictions and control actions based on actual vehicle conditions and passenger responses
3Reliability
If conventional vibration suppression methods are used, then manufacturing cost is reduced, but vehicle durability improvement deteriorates
Solution Approach 1:
The system protects vehicle durability by predicting vibrations that could cause mechanical stress before they occur. The machine learning model identifies patterns in sensor data that indicate upcoming vibrations from road conditions or engine operations, allowing the control system to activate actuators in advance to counteract these vibrations, thereby preventing cumulative mechanical damage to vehicle components
Data Source
AI summary
An electronic system for controlling vibrations and/or inertial forces occurring at a plurality of areas of interest within an operating vehicle, the electronic device comprising circuitry configured to: receive input data comprising sensor data from one or more environment sensors (12) and/or one or more internal sensors (14); convert, by means of a machine learning system (18), the input data into actuator settings; and transmit the actuator settings to one or more actuators (20) to control vibrations and/or inertial forces occurring at each of the plurality of areas of interest within the vehicle.