Neural Oscillator Suspension Control for Road Oscillation Reduction
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Solution Overview
Problem
Existing vehicle suspension control technologies fail to effectively reduce oscillations caused by varying road surface conditions, lacking a robust control algorithm that can maintain predetermined amplitude performance across diverse oscillation patterns.
Innovation Solution
A method utilizing a platform model and suspension model expressed by virtual mass-spring-damper models, combined with first and second neural oscillator models that feed back outputs to acquire displacements and feedbacks, allowing for the calculation of pressing force required for suspension, thereby reducing oscillations and maintaining performance across different road conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional suspension control technologies are used, then the system structure is simple, but the oscillation reduction capability is insufficient under varying road surface conditions
Solution Approach 1:
The patent implements feedback control by using the neural oscillator model to continuously monitor platform displacement and suspension displacement, then adjusting the active suspension force based on the feedback signals. The neural oscillator receives input from the platform model and suspension model, and its output feeds back to control the active suspension, creating a closed-loop control system that adaptively reduces oscillations under varying road conditions.
Solution Approach 2:
The patent replaces conventional mechanical control algorithms with a neural oscillator-based control algorithm. Instead of using traditional mechanical feedback mechanisms or simple electronic control, the system employs a neural network model that processes displacement information and generates control forces, substituting complex mechanical control systems with an intelligent computational approach.
2Object-affected harmful factors
If passive-type vibration reduction is used, then the device complexity is low, but the oscillation amplitude cannot be controlled within predetermined range under diverse road conditions
Solution Approach 1:
The patent implements dynamic control by using the neural oscillator model to continuously adapt the suspension control forces based on real-time platform and suspension displacements. The system transitions from static passive vibration reduction to dynamic active control, where the control parameters are continuously adjusted according to the varying road conditions and vehicle response, enabling precise oscillation amplitude control within the predetermined range.
Solution Approach 2:
The neural oscillator model performs self-adjustment by processing the feedback signals from the platform and suspension models and automatically generating the appropriate control forces. The system serves itself by using its own output to regulate its input, creating a self-regulating control mechanism that maintains oscillation amplitude within the desired range without external intervention.
3Reliability
If active suspension control is implemented, then oscillation reduction performance is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The patent segments the control system into distinct functional modules: the platform model, the suspension model, and the neural oscillator model. Each module has a specific function - the platform model captures vehicle body motion, the suspension model captures suspension component motion, and the neural oscillator processes feedback and generates control forces. This segmentation allows for manageable complexity while maintaining high performance consistency.
Solution Approach 2:
The neural oscillator model serves multiple functions simultaneously: it processes platform displacement feedback, processes suspension displacement feedback, generates control forces for both platform and suspension, and adapts to varying road conditions. This multi-functionality reduces the need for separate dedicated components for each control function, thereby improving reliability without proportionally increasing system complexity.
Data Source
AI summary
The present invention provides systems, methods, and computer-readable media for controlling a suspension for a vehicle in which a platform model and a suspension model are provided and a first neural oscillator model and a second neural oscillator model that feed back an output to each model to acquire displacements of the platform model and the suspension model from displacements inputted into a tire, acquire feedbacks of the first neural oscillator model and the second neural oscillator model from the displacements of the platform model and the suspension model, and acquire pressing force required in the suspension for the vehicle from the feedback of the second neural oscillator model.